Forem: TestFort

Payment Gateway Testing: Use Cases, Test Cases, 2025-Fit Solutions

TestFort — Mon, 19 Jan 2026 11:17:29 +0000

If you’ve done any online shopping lately, it’s safe to assume your transaction was completed through a payment system or a payment gateway. This type of application may seem small compared to major eCommerce stores or media platforms, but it is both absolutely indispensable and completely ubiquitous in today’s internet landscape, illustrated by their revenue that increases year after year.

However, behind every great payment solution, there is an incredible amount of work from development and testing teams. Testing payment systems is the only way to make sure they perform flawlessly, have an appealing UX, and can withstand the variety of security threats present today. In this guide, we will tell you why testing payment gateways is important, how it works, and what kind of challenges you can encounter along the way.

What Are Payment Systems and Payment Gateways?

Online payment systems and payment gateways are two terms that are often used interchangeably. But are they really the same thing? No, these are two different payment options, although they do have a lot in common. Let’s look at the definitions of both solutions and why they are different after all.

A payment gateway is a software solution that acts as the bridge between the customer, the merchant, and the bank, transferring the details of the transaction back and forth along the line. You can think of payment gateways as the virtual-only alternative to POS terminals used in stores and other establishments. It deals with the authorization and processing of online payments and mainly relies on the front-end part that users interact with directly.

A payment system or payment processor is an application that is responsible for the complete processing of online transactions. While payment gateways are more focused on the front-end part, payment processors do the heavy lifting of online payments. In addition to communicating the payment information between the customer, merchant, bank, and credit card association, payment systems also check funds availability, perform fraud detection, and handle chargebacks in case of disputes.

Payment gateways and payment systems do not operate in isolation and are, in most cases, interconnected. This is why most payment testing projects focus on both the payment gateway and the payment processor, although that depends on the solutions implemented in the product: sometimes, it’s enough to test just the payment gateway and the way it is connected to the payment processor, while testing of the processor itself is primarily done by the supplier.

Why Is It Important to Test the Payment Process?

Testing is an integral part of any software development process, and one cannot be imagined without the other. Testing ensures the spotless functionality, utmost stability, complete security, and engaging UX of a software product. However, there are product types where testing is even more indispensable, and payment systems definitely belong on that list. Here are just a few reasons why payment gateway testing is absolutely crucial for your project:

User satisfaction. With plenty of different payment methods available online, users are only going to stick around a payment system that delivers flawless performance and an equally flawless user experience. And that is only possible when payment testing services are an integral part of the product development process.

Stability and scalability. One of the worst things that can potentially happen to a payment software product is being unprepared for a sudden spike in transactions or the user load increasing at a faster rate than expected. Timely testing ensures that the system can handle any workload without any performance gaps.

Data integrity and security. Any guide to payment gateway testing will tell you that tight security is what users expect from a payment system in the first place. Testing and quality assurance helps keep security risks at bay, making sure the sensitive payment data stays intact.

Regulatory compliance. Depending on where the product will be made available, you will need to ensure the system is compliant with all the relevant regulatory requirements — these may be specific to the financial domain, such as AML/CFT regulations in the United States, or applicable to all software released in a certain location, such as GDPR in Europe.

Data-driven decisions. With payment systems, testing can provide a valuable opportunity to make important business decisions that are based on precise calculations, large volumes of data being processed, and accurate predictions being made by AI and ML-based tools.

Proactive issue resolution. There are plenty of things that can go wrong with a payment gateway, but comprehensive testing allows the product team and the stakeholders to anticipate problems before they become a real threat to the app’s performance, security, or UX, getting rid of them exactly where they do the least amount of damage.

Continuous improvement. For online businesses, it is crucial to constantly improve their services, keeping a valuable competitive advantage and avoiding the common business pitfalls that come with a stagnating product. A testing team that is involved in the project long-term will always find new ways to further improve the product.

Common Types of Payment Gateway Testing

The exact set of testing types to be used on a payment system testing project depends on the specifics of the product, the goals of testing, and many other factors. Still, some types of testing are more relevant than others for payment solutions, and here are the ones that are most likely to be included in payment app testing services.

Functional Testing
Functional payment gateway testing verifies the correct functionality of the product — in other words, whether the website or application does everything it is expected to do according to the requirements. To efficiently test the functionality of the product, the team must have some insights into the payment process, although black box testing techniques also work well here.

Performance Testing
Performance testing involves verifying the way the application operates in different situations and under different conditions. Performance testing, along with its subtypes like load testing and stress testing, is indispensable for payment systems because it points towards the performance bottlenecks and potential operational gaps that would otherwise be discovered by users, which would inevitably damage the product’s reputation.

Usability Testing
Payment gateways are technically complex systems, but they are created for real human beings to use, and whether or not those human beings enjoy using the application will directly impact its future in the market. Usability testing exists to ensure a consistent payment experience that is equally accessible to all users regardless of their technical proficiency and does not create additional challenges for users completing transactions.

Security Testing
An impenetrable, fully secure payment system is nothing short of an industry standard. As users are getting more aware of the potential security risks and how elaborate online scams are getting, they will want to make sure the payment gateway is secure before they trust it to handle their sensitive financial information. For the payment card industry, data security is vital and can only be fully ensured through continuous testing.

Words by
Michael Tomara, QA Lead, TestFort
“Since security testing is particularly vital for payment products, and since security testing always benefits from being automated, we can also say that payment gateway testing is well-suited for white-box and grey-box testing. When testers know how the system works and are familiar with the code, their testing approach will generate better results and positively impact the product’s security.”

Localization Testing
Oftentimes, a payment gateway processes transactions globally, not limited to any particular location. This means that the application should be available in every target language, and the translations should be as accurate as possible. On top of that, localization testing is also used to ensure that the payment solution uses correct currencies, time settings, and does not violate local laws.

Compatibility Testing
With billions of smartphone users in the world, hundreds of available models, lots of operating system versions, and an endless number of their combinations, the variety of platforms is not something you can afford to ignore as someone who develops payment gateways. This is why it’s crucial for payment systems to be tested for device compatibility, and a little later in this article, we will discuss why it’s important to specifically test on real devices rather than emulators or device farms.

Accessibility Testing
Inclusivity is a growing concern in the world of financial software. Developers must make their products accessible to the widest category of users, including users with disabilities and physical limitations that prevent them from accessing the solution in the regular way. Software testers, in turn, need to check the accessibility with different groups of users and physical requirements in mind.

Integration Testing
One of the reasons why banking and financial software has been able to grow at such a rapid pace, both in terms of functionality and availability, is that financial applications are often developed with the help of individual products combined into one functional solution. Payment processors are also often used as part of a bigger product, which is why integration testing is required to check the way the final product operates.

Regression Testing
This is one of the testing types you would find on any testing project, not just as part of payment software testing services. Regression testing purposes are plain enough: to check whether the software product was negatively affected by the recent changes to the code — particularly during bug fixes in the previous round of testing. Regression testing allows teams to release software with confidence and with a lower risk of new bugs appearing.

Compliance Testing
In different local and foreign markets, banking and financial products, including digital payment methods, need to comply with regulatory requirements. These requirements exist to ensure the integrity of the software, the responsible practices of processing and storing user data, the absence of security risks, and other essential parameters of reliable software users can trust. Only a team with relevant experience can plan and run the tests for compliance, as the world of regulatory compliance is enormous, and regulations change frequently.

Automated Testing of Payment Systems

Test automation is a perfect fit for payment solution testing. It helps get the most reliable results of testing and avoid many of the quality-related risks while saving time and resources compared to manual testing. But what should you automate in the first place for a payment testing project? Here are the testing activities that make particular sense to automate:

Functional testing — checking the payment functionality, transactions, card statements, payment history, etc.
Regression testing — making sure the recent changes to the code did not negatively affect the application as a whole.
API testing — testing the APIs integrated into the end product, both on their own and as part of a larger system.
Performance, load and stress testing — ensuring the payment application can withstand any number of users and transactions.
Security testing — performing comprehensive checks to make sure the app is impenetrable for those who want to interfere with data.

Words by
Taras Oleksyn, Head of AQA, TestFort
“For payment applications, security threats are constantly evolving, and the attacks are getting more intricate and more daring. From Man in the Middle and cross-site scripting attacks to site injections and social engineering, automated security testing constantly has to deal with increasingly complex tasks. And that includes testing not only the payment application and the software it’s implemented with, but also the company’s software infrastructure that may not be directly linked to the payment solution but can still pose security risks to the whole ecosystem.”

The success of automation testing depends on several factors: the skills and experience of the team, the match between the project specifics and its goals, and the correctly selected stack of tools and frameworks. The choice of tools to use on the project is directly linked to the tech stack used to create the product. For example, when it comes to API testing, there are specific tools for each programming language used to create the API: there is REST Assured for Java, Requests module for Python, and so on. At the end of the day, the team uses a set of metrics to determine whether the setup is successful and whether the automation efforts pay off, or whether there are additional steps that need to be taken.

Preparing Test Cases for Payment System Testing: What Needs to Be Tested

The exact set of test cases for a payment gateway testing project will always depend on the project requirements, the specifics of the product, and the goals that need to be achieved. Still, having provided testing across dozens of payment systems and other related products, we have a firm grasp of what the team must test in a payment solution and what this segment of testing covers. Here are some of the most common sample payment gateway test cases to get you started:

Credit card authorization. Here, the system must take certain parameters, such as credit card details, currency, user’s location, and others, to verify that the payment is authorized by the bank and allowed to proceed.
Payment confirmation. Once the payment is processed, the testing team needs to ensure that it proceeds correctly and that the user receives a timely confirmation of the payment, while the bank or card issuing company also gets a payment confirmation on their end.
Connection check. A stable, uninterrupted internet connection is integral for the correct operation of the payment solution. For this stage of testing, the team will simulate different conditions to see how they affect the connection, and how the interruptions in the connection, in turn, affect the payment process: for example, whether the user gets a corresponding error message and whether the process resumes after the connection is restored.
Exchange rates. When the product is going to be available globally, one of the key tasks for the testing team is to ensure that the payment gateway uses correct exchange rates for all currency pairs. The exchange rates also need to be updated swiftly when they change, which is why the testing procedure should also include verifying how quickly it happens.
Negative scenarios. A significant portion of payment gateway testing ensures that the payment is completed successfully. However, that is not always the case, and an online payment can fail for a myriad of reasons. The team’s job is to account for both common and unlikely scenarios, overseeing the way the system reacts to payment failures and what options the user has for aborting or resuming the transaction.
Security checks. One of the most important things to check while testing a payment solution is the security of the application. Some security tests, like penetration testing and security scanning, need to be automated to cover as many scenarios as possible, but it is also possible to do manual security checks. For example, the team may test password rules, password and username strength, fail-open authorization, input validation, trust relationships, and more.
Refund processing. Refunds are a common byproduct of online transactions, and depending on the merchant’s policy, the refund can be requested and issued months after the purchase. This means the team has to make sure that all relevant data is stored securely for an extended period of time to make the refund possible, and that the refund itself works smoothly for all parties, including the customer, the seller, and the bank.

The Specifics of Testing in the Payment Domain

Many of the techniques and approaches in testing payment systems are widely used in testing other types of applications. However, payment testing also has a few techniques and trends of its own, and here are some of the key specifics of testing payment applications.

API testing. Most payment systems are comprised of several smaller solutions, typically carried out through the API technology. This is why, when it comes to testing payment methods, API testing is an integral part of the process: each API has to be tested both individually and as part of a larger application. While it is possible to perform API testing manually, sooner or later, it has to be automated. Since APIs don’t have a user interface, testing these products works better when the team has access to the code. Automated testing of APIs takes time for writing the tests initially, but it saves resources in the long run and increases the accuracy of the results.
Testing in real-life conditions. When it comes to testing payment gateways, sandbox testing and virtual card simulations are widely used throughout the project. However, the closer the product is to the release, the more important it becomes to also test it using real-life conditions and real credit cards with real money on them. This gives the team and the stakeholders a comprehensive idea of how the app behaves in real life and helps mitigate the risks of releasing an undertested product.

Words by
Michael Tomara, QA Lead, TestFort
“In most cases, it’s the client’s responsibility to provide both the cards and the funds, but the end result is worth the extra effort. A good idea is to use sandbox testing for edge cases and real cards to check the app’s normal behavior.”

Beta testing. A common technique for testing payment solutions is to release them to a small group of users, have them fully interact with the product as they would with a regular payment system, and collect the bugs they discovered. However, as effective as this technique is for gauging the real-life experience of using the product, it cannot be viewed as a substitute for professional testing, as it takes an in-depth knowledge of software QA to do the most thorough job.
Stress testing. We’ve already touched on the importance of performance testing and load testing for apps that deal with payments and transactions. In addition to that, we would like to point out the importance of stress testing, which is used to see how the app behaves in situations that venture from its normal operations. From Christmas shopping to the start of ticket sales for Taylor Swift’s next tour, using stress testing to prepare even for the most grueling scenarios can save the stakeholders from a lot of trouble.
Data testing. Data testing is a relatively new trend in software testing and, as the name gives away, it deals with data — specifically, the data produced by, stored, and exchanged through the payment system. Data should be checked for consistency, integrity, relevance, absence of duplicates, and so on. This type of testing is also closely connected to compliance testing — in particular, compliance with regulatory requirements concerning the handling of user data.
Shift-left testing. This trend is something we are witnessing more and more all across the software testing field, but it is also dominating the payment gateway testing segment. The shift-left approach to testing moves the quality assurance activities to the beginning of the development lifecycle, allowing the testing team to get involved from the start and begin performing quality assurance despite the fact that there is barely any code written. This results in cleaner, durable code that creates high-quality and scalable products.

How to Test Payment Systems and Payment Gateways

Having delivered payment gateway testing services for over 15 years and for dozens of clients, we have time and time again come to realize that precise planning is just as important for the success of the project as making immediate decisions based on the project specifics. This is why, while we always leave room for adjustments, our procedure for testing includes certain steps and activities we cannot do without. Here is how we approach testing payment applications.

Determine the Scope
Together with the project stakeholders and developers, the testing team will determine the scope of testing, e.g. what needs to be tested and on what scale. Taking the time to complete this step will save the team from wasting valuable resources and will help keep the testing process streamlined.
Create Use Cases
This is the stage where the testing team, along with business analysts and other specialists, work to create user personas and use cases for the application. For this stage to work properly, it is integral for the team to account for the whole variety of users and their needs, creating a diverse range of user personas, who, in turn, will influence the number and variability of use cases: the more potential scenarios are covered during testing, the more successful the release is going to be.
Prepare Test Cases
Using the information obtained at the two previous stages, this is where the testing team will create detailed test cases to be executed later. Depending on the specifics of the product, the test cases will be focused on functionality, security, compatibility, performance, accessibility, and other parameters of a well-tested payment gateway. The in-depth description of the steps and conditions contained in test cases for payment gateway testing is crucial for continuity in case another testing team has to take over or the developers need to replicate the event.
Get the Infrastructure Ready
Testing payment systems requires the use of specific infrastructure, which may include various hardware and software, as well as testing environments. For payment gateways, it is essential to test the application on different mobile and desktop devices, as well as on different operating systems and in different browsers. And while there is an option of using device emulators and farms, at the end of the day, nothing beats the efficiency and reliability of testing on real devices — the way we do it at TestFort.
Run the Tests
This is an integral part of the testing process where the team goes over the test cases one by one, executing the tests and noting whether they passed or failed. This process should run according to the predefined plan, and the team should watch out for any test runs that produce unexpected results. In most cases, this stage will include initiating a transaction based on a specific set of conditions, entering payment details, and completing the payment, or using different techniques to prevent it from being completed.
Observe the Outcomes
A vital part of testing a payment gateway is evaluating the outcome of the test run. Was the test successful? Did it happen the way it was described in the test case? What did the process feel like to an average user? Were the transaction stages and the accompanying system messages easy to understand and informative? Examining the payment process from the standpoint of a user is a highly useful activity that adds value to the testing process.
Document the Results
Documentation is a sometimes overlooked aspect of testing, but we cannot stress the importance of documentation enough. Documenting the process and especially the results makes them more tangible and easily accessible for the team, both the people currently involved in the project and the people who will be involved in it at later stages. Documentation can also be used by developers and project stakeholders, so this is not a step that can be omitted or taken lightly.
Create Reports and Analytics
Once the testing team has enough data, they should turn it into reports and use various tools, including AI-based ones, to describe the state of the application, its functionality, stability, performance, usability, and other essential parameters. Detailed reports and robust analytics will give the developers and other stakeholders a complete picture of where the product’s quality stands at the moment and what can be done to improve it.

The Importance of Testing on Real Devices

Here at TestFort, we pride ourselves on testing software on physical devices, and that includes payment solution testing. Our procedure for payment system testing involves checking the product on 250+ real devices ranging from entry-level smartphones and tablets to flagship devices, and that helps us ensure a few vital outcomes:

Testing the solution on different combinations of devices, versions of operating systems, and settings allows us to cover hundreds of target device setups, making the system’s behavior more predictable regardless of the platform or the user’s preferences in setting up their devices.
Using physical devices for testing a payment processing system and other types of payment solutions allows us to utilize all the software and hardware infrastructure available on the device, including various sensors, biometric authentication solutions, and built-in payment systems like Google Pay and Apple Pay.
Employing a fleet of real devices is the only real way to check the way the application being tested is impacted by the variety of events that take place on a mobile device, such as notifications, calls, system alerts, error messages, network and internet connection issues, and applications running in the background.

Challenges of Testing Payment Gateways
Payment gateway testing helps businesses make sure their software is release-ready and able to compete with the market leaders. And the payment system QA field has come a long way since its inception, introducing different types of testing, techniques, tools, and best practices to make the process more effective. Still, like any QA activity, testing payment systems has its challenges, and here are the most common ones.

Increasing Complexity of Systems
Online payment systems are getting more and more elaborate and include more and more cutting-edge functionality. In addition to testing credit and debit card payments, testing teams now also have to focus on cryptocurrency and other advanced technologies as well. Plus, as scammers are getting more creative with their attempts to steal sensitive information, testers should also get more creative with their testing approaches.

Variety of Integrations
In the online payment landscape these days, there is no shortage of range. There are dozens of diverse payment methods available, and those are integrated with an even bigger variety of banking, financial, and eCommerce solutions. As a result, testers often have to deal not just with the payment gateway itself, but also with the way it operates with other systems, and the sheer number of possible combinations can be overwhelming for a team with limited resources.

Words by
Taras Oleksyn, Head of AQA, TestFort
“On one hand, one of the ways to measure the payment system’s success is by the number of its integrations into other systems. On the other hand, this creates an additional challenge for testing teams, who have to test more and more combinations of the payment gateway with other solutions. When the team does not have enough resources to cover the increasing testing needs, a good solution to the problem can be to automate as much testing as possible, given that it adds value and makes sense in your context.”

Intricate Regulatory Requirements
As eCommerce and online payments are becoming more of a mainstay in our lives, governments around the world are making an effort to control the industry and prevent fraud and user data getting mishandled. There are numerous regulations specific to the financial domain in most foreign markets, which means the more locations you want to release your product in, the more regulatory requirements it will need to comply with, and the complexity of the regulations is a challenge on its own.

Outdated Testing Infrastructure
Testing advanced software solutions requires the use of equally advanced testing infrastructure. Unfortunately, that is not always the case, particularly when the company is trying to keep the testing operations in-house. In that case, the testing process may not end up meeting all the project goals. Plus, the cost of testing may increase because the outdated infrastructure requires more time and effort to complete a simple task. Entrusting testing to a trusted provider with all the necessary infrastructure is a great way to alleviate those risks.

Final Thoughts

Payment systems and payment gateways are one of those software types that are everywhere around us, even if we don’t always notice them. And even though there are a few great payment solutions in the market already, with the growing number of online transactions, there will always be room for more. At the same time, it’s important to remember that users are not going to have much patience for an application that is riddled with usability bugs or much trust for a product that has been involved in security-related controversies. Timely and all-encompassing software testing helps nip those risks in the bud, and we hope that our guide has made things clearer for you in this regard.

Offshore Software Testing: An Honest Look at Offshore QA Testing

TestFort — Mon, 19 Jan 2026 11:04:14 +0000

Businesses around the world regularly face the challenge of wanting to ensure the highest quality of software but also needing to refrain from overspending. This struggle has become more apparent in recent years, when the threat of an economic recession causes companies to cut costs wherever possible without sacrificing the quality of their products. And for many companies, the solution for these struggles is offshore testing. In this article, we’ll discuss the benefits and drawbacks of offshore testing, how to find the right partner, and how to make the most of an offshoring project.

Key Takeaways

Offshore quality assurance offers scalable, cost-effective QA support for companies of all sizes and domains.
The right software testing offshore partner can help speed up releases, improve test coverage, and provide 24/7 productivity.
Cost savings are real, but not the only reason to hire an offshore software testing team. Strategic value, flexibility, and access to niche expertise often matter more.
Risks like miscommunication, delays, or quality gaps can be minimized through mature processes and clear expectations.
Time zones, tools, and team overlap are critical factors when building a productive offshore testing model.
Choosing the right location and vendor affects everything from pricing to communication to long-term results.
Hybrid or managed offshore setups are a strong option for teams looking to scale without losing control.
Security, IP protection, and compliance must be part of your vendor evaluation, not an afterthought.
Offshore QA isn’t a one-size-fits-all solution, but when aligned with business goals, it delivers long-term value.

What Is Offshore Software Testing?

Offshoring is a term used in many industries and for many different services, not limited to software-related ones. However, offshore software testing services are definitely among the most popular types of offshoring. Offshore software testing is the practice of entrusting some or all of the company’s testing needs to one of the QA testing companies located in a different geographic and time zone.

Key Benefits of Offshore Software Testing

At first glance, the biggest advantage of offshoring testing seems to be cost savings. And while the desire to lower the cost of testing is probably the most common reason why companies go for an offshore over an in-house or onshore team, it’s definitely not the only one. Here is why a company or product owner is likely to find offshore testing an invaluable measure

Cost-effectiveness
Offshore testing significantly reduces labor costs and additional expenses. Vendors handle office space, taxes, and benefits, so you only pay for the actual QA work.

Strategic partnership
Reliable offshore vendors act as long-term partners, and many offshore contracts last for years. A software testing offshore team integrates with your development process and takes full ownership of quality, unlike short-term freelancers.

Higher quality of software
Independent QA units test the product delivered by the software development team with fresh eyes and zero bias, using proven practices to uncover issues and improve the overall quality of the software.

Access to a larger talent pool
Offshore markets give you access to specialized testing professionals and domain experts you may not find locally or can barely afford, especially in the current job market.

Faster releases
With round-the-clock test cycles across time zones, offshore teams help match even the most grueling production schedule, ensure fast turnaround, meet tight deadlines, fully support the production process, and respond quickly to market changes.

Government support
Popular offshore destinations often offer business-friendly laws, tax benefits, and strong legal frameworks that support foreign tech clients.

Absolute flexibility
An offshore QA facility typically offers flexible cooperation models. The right model can ensure smart spending and help you quickly scale your QA efforts up or down depending on project scope, timelines, and budget.

Robust documentation
Offshore QA teams follow strict documentation practices, ensuring all testing aligns with functional and non-functional requirements. Clear, consistent reporting helps prevent issues caused by miscommunication or missing data.

Limitations and Drawbacks of the Offshore Testing Model

Like pretty much any concept in the world, offshore testing services have both advantages and disadvantages. We’ve already talked at length about the advantages of this model; now let’s focus on the typical drawbacks of offshore testing QA services.

Cultural and language differences

Offshore teams often come from different cultural and linguistic backgrounds, and while most invest in English training, their proficiency may mainly cover technical needs. To collaborate effectively, it’s important to understand local work culture, such as views on deadlines, hierarchy, and time management. These differences, though, can be successfully navigated with the help of a dedicated project manager who fosters clear communication, cultural awareness, and mutual respect.

One notable example comes from a complex enterprise project led by Bruce Mason, a senior delivery manager at TestFort. The client initially struggled with fragmented communication, cultural differences, and a rigid test cycle that slowed releases. Drawing on years of outsourced offshore QA experience, Bruce restructured collaboration by introducing shared agile rituals, refining reporting standards, and setting up timezone-aware workflows. His hands-on approach helped align cross-border teams, improve transparency, and build a long-term relationship based on trust, performance, and shared accountability.

Time zone differences
Time zone gaps — often 9 to 13 hours — can be both a challenge and an advantage. While they enable near round-the-clock testing, they may limit live communication. Many offshore vendors help bridge this by offering adjusted work hours or using asynchronous collaboration tools to stay aligned with clients.

Substandard quality of work
This is an issue you typically don’t have to worry about when you work with a carefully selected team with a proven success record. However, when the vetting process is not taken seriously or skipped altogether, or when you go for the most low-cost vendor, you can often discover that the outcome of the cooperation is not what you imagined. This can also lead to hidden costs of you having to hire another team that is able to find and fix the issues missed by the original team.

When to Hire Offshore Testers and When to Do Testing In-House

Offshore software QA is an excellent solution that meets the needs of thousands of companies around the world. At the same time, we won’t go as far as claiming that everyone needs to outsource their testing projects immediately. Here is when it makes perfect sense to use offshore QA testing services:

You’re looking to save costs. Probably, the most popular reason for companies to go for offshore testing is that it costs significantly less compared to the cost of hiring an in-house team or working with a nearshore provider.
You need niche expertise. Some testing projects require highly specific expertise and services, whether it’s performance, security, or compatibility testing, and you can easily find a lineup of vendors who specialize in the required services.
You need 24/7 testing availability. A significant benefit of offshore testing is that by strategically choosing the team’s location, and, therefore, work schedule, you can achieve a nearly uninterrupted testing operation for round-the-clock quality assurance. You want quick scalability. An offshore testing vendor will easily adjust the collaboration to match your changing business needs. You can scale your team up or down in a matter of days, which is not the luxury you can afford with an in-house operation.
You have a short-term project. When you are not in need of continuous, long-term testing and simply want to test a new feature or a small update, hiring an offshore team makes more business sense and can give you the anticipated results faster.
Your project includes repetitive activities. For well-established projects with clearly defined, stable requirements and a lot of repetitive actions, such as regression testing or basic functional testing, offshore QA can be a better business match.

At the same time, there are situations where offshoring your testing needs is not the most ideal solution. Here is where you may rather consider in-house quality assurance instead:

You have a complex or specialized project. While offshore QA teams possess a wide range of skills and expertise, some projects still require highly specific knowledge of experience. This mainly includes one-of-a-kind software products or a highly niche industry.
Your project has advanced security needs. When dealing with protecting intellectual property, sensitive customer data, or other cases where advanced security is required, offshore testing may not be an option due to the complicated security clearance procedure that is required.
There is a need for face-to-face collaboration. For some projects, it’s critical to have the development, testing, and sometimes DevOps teams operating from the same building and in close proximity to one another to be able to exchange ideas in real-time.
Testing is your company’s core competency. If you’re involved in designing, creating, or testing software, quality assurance can be rightfully considered to be your core competency and therefore should not be outsourced, as you will have a harder time overseeing and managing the testing process in that case.

Assembling the Perfect Software Testing Offshore Team: Structure and Responsibilities

Building an offshore quality assurance team is one of the most fundamental decisions in the whole process. It can get even more challenging than regular hiring because you don’t get to meet the prospective candidates in real life and need to rely only on their CVs and video interviews to make the decision. Still, deciding who to hire and for which roles is something that will have a lasting effect on the whole project.

There are no universal standards when it comes to offshore teams because it all depends on the size and complexity of the project, its anticipated duration, as well as the already available testing resources. Still, proper resource planning can help avoid problems in the long run. In case you are considering handing over the entire scope of testing to an offshore team, its composition will typically include the following roles:

Manual QAs — these are specialists who do the biggest chunk of the work on a typical testing project. Manual QAs can get to work almost immediately without a lengthy preparation period. A medium-sized QA project is going to need 3 to 5 manual QAs.
QA Lead — a person who is in charge of managing the manual QA team. In some cases, the role of a Manual QA Lead is filled by a Senior-level member of the team; in that case, the Lead not only manages the team and communicates with the client, but also does hands-on testing work.
Automation QAs — essential specialists for any mid-sized to large testing project, especially one with a lot of tests that are run repeatedly, such as regression tests. The automation team typically comes on board after the manual team has already completed a portion of work, although sometimes Automation QAs can start simultaneously with the Manual QAs in case there are some tests completed already — for example, by a previous QA team. -Automation QA Lead — an engineer who oversees the work of the Automation QA team and participates in various testing tasks. A common scenario is when the Automation Lead joins the project before the rest of the team, so that, by the time other Automation QAs begin working on the project, there is already a solid foundation for their next steps.
Project Manager — a vital specialist who acts as the link between the client and the vendor. The PM can operate on the vendor’s side, but that only makes sense in the case of a large project that involves services besides testing. For most testing projects, a Project Manager working on the client’s side is the most fitting setup.
DevOps Engineer — a specialist responsible for creating the infrastructure for a project and making sure the development and testing departments have everything needed to work efficiently and without interruptions. The DevOps Engineer can work on the client’s side, but it can be a big advantage in favor of the vendor when they have their own DevOps department ready to take over all related tasks.
Business Analyst — a person who uses various ways to source business data and insights to then suggest ways for an organization to move forward in terms of business success. The work of a Business Analyst is directly linked to high-quality software produced by an organization, which is why a BA being involved in a testing project, at least in a part-time role, is a strong advantage.

In addition to that, the presence of the development team is integral for a successful start of a QA project and for achieving the desired results. Otherwise, the client risks hiring an offshore testing team only for them to run the initial tests and then have nothing much else to do because there is no one to fix the bugs. Many companies thrive by working with two offshore teams — one for development, and the other one for testing — or having the development team operate in-house. In any case, it’s up to the client to ensure smooth cooperation between all departments to avoid bottlenecks within the project.

Software QA Tech Stack: Most Popular Services

One of the main reasons why more and more companies now prefer to use offshore software testing services is that with offshoring, it’s possible to work with niche experts you cannot always easily hire locally. And that offshore QA expertise includes not only specific domain experience, but also familiarity with certain technologies. It’s very common for in-house teams to only work with a limited number of technologies, and getting your team to master those technologies takes time and money, which makes it not the most financially feasible option.

Luckily, an offshore team for testing software meets even the most unusual requirements in terms of types of testing, technologies, and latest tools you want to use on the project. Here are the technologies often requested by clients.

How to Find the Right Offshore Software Testing Partner

Whether you need a team for a small, limited-time task, or you are planning long-term collaboration with the same vendor, the choice of an offshore partner is one of the most fundamental ones you’ll have to make. A team selected for the wrong reasons, or with barely any selection process at all, may not deliver the results you expect, and the likelihood of facing common issues and obstacles increases. Here is how to hire an offshore testing team for a fruitful and mutually beneficial QA cooperation.

1. Get clear on the requirements
Clarify your project needs upfront: what services, scale, and duration you require. Identify any domain expertise or special skills needed. Also, estimate team size early, since smaller vendors may lack resources for large projects. Larger vendors can usually assemble suitable teams quickly.

2. Research the skills and services
Decide exactly which tasks your offshore team should handle and whether one team can cover them all. Agree on the team’s responsibilities to avoid gaps. While working with a single team is often more cost-effective, complex projects might need multiple vendors. Always verify skills through relevant portfolio examples.

3. Review the project portfolio
A solid offshore QA vendor should have a project portfolio showcasing delivered services, timelines, team size, and industry context. Look for case studies related to your domain to assess their experience and methods. This insight helps you gauge whether the vendor is a good fit.

4. Discuss the cooperation options
Offshore testing companies usually offer a selection of cooperation models that fit the needs of most clients. Most vendors provide the following minimum of models:

Fixed cost — where the cost of the project is known from the start after extensive calculations and changes are typically not allowed after the project begins.
Time and material — where you can benefit from flexible requirements and change the scope and scale of the project as you go, paying for the time of all employees involved in the project.
Dedicated team — where you can assemble an entire team of QA engineers and other specialists, and work with them for as long as you need, not necessarily tying the cooperation to just one project.

Additionally, many vendors offer a fourth model, called hybrid or fixed cost plus. During the initial consultations, the vendor can recommend you the most suitable model for your project requirements. Still, at the end of the day, it’s your choice to make, and the vendor needs to show understanding and flexibility.

5. Ask about communication practices
Since you won’t share an office, smooth communication is key. Ensure you and the offshore team agree on channels, sync schedules, and availability. Most vendors adapt to your preferred tools for calls and chats to keep collaboration seamless.

6. Consider time zone and language differences
Time zone and language gaps are common challenges but manageable with good communication. Confirm the team has sufficient English skills and a plan to improve if needed. For large time differences, many vendors adjust schedules to overlap with client working hours.

7. Evaluate the work ethic
While hard to fully assess upfront, you can gauge work ethic by how promptly and thoroughly managers respond to your inquiries. Slow or incomplete replies early on may signal future issues. Also, check independent client reviews to get an honest picture of the vendor’s reliability.

How the Cost of Offshore Testing Is Formed: All the Factors That Matter

The cost of offshore software testing services is never fixed simply because of how many different factors need to be taken into account. At the same time, these factors are not something you will find listed in your bill. If you want to better understand how the cost of offshore software QA is calculated, here are the key factors that impact the final price:

Geographic location. Depending on where offshore QA teams are located, the cost of their services can vary greatly. For example, Asian and Eastern European offshore companies are known to be very affordable, while Western European teams charge more.
Project complexity and duration. Highly complex tasks, such as large-scale performance or security testing, costs more than basic QA tasks. Long-term cooperation also requires a bigger budget compared to a one-month simple QA project.
Team composition and skill level. The most affordable QA specialists are usually Junior to Middle QAs with a general skill set. The more experienced specialists your project requires, and the more specific expertise you need, the more you should be prepared to pay.
Tools and technologies used. On some projects, the team can make do with open-source testing tools and technologies at no additional cost for the client. However, that is not often the case, and licensed tools can further increase the project budget.
Security requirements. Additional data protection and security requirements, especially in industries like fintech and healthcare, can impact the cost of the project, especially if the team needs to use paid security tools in their work.
Client’s management team involvement. Depending on the engagement model and agreement between the client and the vendor, the offshore QA team can work almost completely autonomously. Still, heavy involvement on the client team’s side is bound to increase the cost of testing.
QA standards and certifications. When you are determined to hire a team with specific QA certifications like CMMI or ISO, you should be prepared to increase your spending accordingly.

How to Make the Most of Your Offshore Testing Team

When you hire an offshore testing team, the choice of a vendor and the composition of the team are obviously important, as is the scope of work you plan to hand over to the new team. However, when using offshore software testing services, the work doesn’t stop there, as the team also needs to be continuously and effectively managed to keep the quality of your software product at a desired level. Here are some tips for efficiently managing your offshore partners.

1. Get to know your QA team members
Learn your offshore quality assurance team’s names and backgrounds to build strong bonds. Simple intros with photos and info help, especially if you have both onshore and offshore teams. An experienced project manager can foster this connection.

2. Be a strong communicator & handle the language barrier
While offshore QA team members usually have good English, communication challenges still arise. Regular calls, clear updates, and informal chats help reduce misunderstandings and bridge gaps.

3. Arrange a realistic onshore-offshore balance
Don’t assign all testing offshore just to cut costs. Consider your project’s complexity and system access to define which tasks suit offshore QA. Clear roles improve productivity by letting each team focus on their strengths.

4. Adapt your issue management process
Use web-based tools for tracking defects and queries, accessible to both onshore and offshore teams. With proper management, even a 13-hour difference or significant time difference can boost productivity and efficiency by enabling nearly continuous testing.

5. Manage the documentation on your deliverables properly
Set clear guidelines for documenting tests and results, and choose tools that work across locations. Starting early helps all parties quickly find information and avoid misunderstandings.

How to Measure the Effectiveness of Offshore Software Testing

By now, you know all the reasons why companies all over the world are going for offshore testing. But in the end, is it worth it? There are several ways to know if your offshore testing collaboration is going well, and these are the most important ones:

Adherence to deadlines and test coverage. The team will need to evaluate whether different stages of the project were completed on time and whether each phase produced the required amount of test coverage.
Cost-effectiveness. Here, you’ll need to compare the cost of testing against the previously determined budget to make sure the actual expenses don’t exceed it, given that cost savings are among the most popular reasons to go for offshore QA in the first place.
Efficient communication. Your job here is to determine how quickly and effectively the offshore software testing company was able to get involved with your day-to-day operations and whether the speed and efficiency of their contributions has had a positive impact on the development process overall.
Automation and innovation. Automation continues playing an increasingly vital role in software testing, and the more tests the offshore team has been able to automate, thus reducing the dependence on time-consuming and resource-intensive manual testing, the more innovation they have been able to contribute.
Adherence to testing processes and standards. As a stakeholder in an offshore testing project, you are the one calling the shots when it comes to the testing methodology, process, standards, technologies used, and project management: as long as the offshore team adheres to those requirements, the project can be classified as a success.
Customer and stakeholder satisfaction. Here, you’ll need to gather and analyze the feedback from the end-users regarding the finished version of your product. Interviewing the project stakeholders, including developers, project managers, and product owners, can also tell you whether the project was successful.
Software testing KPIs. There are dozens of KPIs used to measure the effectiveness of software testing, and naturally, many of them are also applicable to offshore testing. Most importantly, they include Defect Detection Rate, Defect Leakage, Defect Fix Rate, Test Coverage, Test Automation Coverage, Cost per Defect, On-Time Delivery Rate, Time to Defect Resolution, Defect Reporting Accuracy, Post-Release Defects, Regression Testing Efficiency, and Automation ROI.

Onsite-Offshore Software Testing Services: Are They Worth It?

While many companies around the world work exclusively offshore development and testing teams, there is an increasing number of companies that now use the combination of onsite and offshore testing. Onsite & offshore QA testing services definitely seem like an attractive idea on paper. Let’s look at this model and its pros and cons in more detail.

What is onsite & offshore QA testing?
The onsite and offshore software QA model is a hybrid delivery approach that takes the best from the two models and combines them into a new winning solution. Under this model, the entire job of testing a software product will be distributed between the onsite and the offshore team. There can be more than one offshore team working on the same project, and they are not necessarily going to be from different countries — although, more often than not, the term “offshore” means “overseas” in this context.

How onsite and offshore QA works
The overall composition and setup of a team on an onsite & offshore software testing project can depend on many factors, including the skill sets possessed by each team member and the specifics of the project. The client will also make the decision based on the business interests — e.g. how much of the work makes financial sense to entrust to an offshore team.

On some projects, the distribution of work is 30% and 70% to the offshore and onsite teams respectively. In other cases, it’s closer to a 50/50 distribution. And some companies prefer to hire offshore software testing teams only for a small fraction of the work — 20% or 30% of the scope. This is typically reserved for companies that are looking for specific QA expertise they may not have in-house, such as automation testing, usability testing, and accessibility testing.

Advantages of the onsite – offshore software testing model

At first glance, it may seem like planning an onsite and offshore QA project takes more time and effort than doing the work completely in-house or outsourcing the full scope to an offshore QA team. But why do so many companies prefer the combined delivery method nonetheless? These are the biggest advantages of using onsite and offshore testing services:

Uninterrupted software development and testing life cycle. When used right, this model can assure that there is work going non-stop.
Face-to-face client-team cooperation. Close cooperation helps build better communication and also enhances the business relationship between all parties.
A cost-effective delivery model. Offshore teams usually cost less and can be quickly scaled up and down, so you can easily reduce your expenses.

Potential challenges of onsite & offshore QA testing

As good as this delivery model is, there are possible challenges that can occur at any stage of the process. These are two of the most common issues encountered by the participants of the project:

Onsite resources say that offshore resources don’t know what they are doing and are not available when they need them.
Offshore teams complain that they are not getting the right inputs they need and that the onsite team is not always easy to reach.

The good news is that these issues can be easily resolved with enough commitment from all parties involved and clear communication. Here is how to remove those challenges:

Remember that onsite and offshore are two sides of a coin. Both parties have an equal impact on the success of the testing project. The two counterparts should coexist in harmony; otherwise, this model will eventually be broken.
Establish regular communication where all sides of the project can exchange information, share knowledge and news, and promptly resolve any misunderstandings. These meetings should take place over fixed periods of time — for example, weekly. However, you need to be mindful of possible time differences.
Have a list of what you need to do for one another and make sure you are working on the list and updating each other on the progress. This will help each party have a realistic view of the work in front of them, both in the short and long term.

Real-Life Case Studies of Companies That Outsource Testing

Companies all over the world have been opting for offshore QA testing services for decades now. Interestingly enough, the scale of those companies ranges from small startups to acclaimed tech giants like Google, which goes on to prove that most companies, regardless of the size, can benefit from offshore services. At the same time, the decision to hire an offshore software testing team doesn’t always bring the anticipated benefits. Here are some stories of companies outsourcing their testing needs to offshore vendors to varying results.

IBM: Scaling automation testing

Like many companies in recent years, IBM was facing the challenge of expanding its testing operations globally, speeding up product releases, and cutting testing costs. The solution was to outsource automation testing to an India-based vendor who implemented advanced automation frameworks for IBM’s cloud services and mainframe products. As a result, the average time to market for IBM’s products was reduced by 30%, while cost savings over three years amounted to 40%.

Slack: Ensuring global product quality

As Slack was rapidly expanding its global reach, the company had to make sure that the product was reliable, scalable, and localized for different markets. Having quickly reached the limit of in-house bandwidth, and with the increased cost of such extensive testing, Slack opted for offshoring these specific testing tasks to teams located in Eastern Europe and Asia. The outcomes perfectly aligned with the project goals: the teams, who possessed deep expertise in performance and localization testing, helped Slack get rid of critical bugs that could tarnish their position in the global market while keeping the cost of testing reasonable.

Airbnb: Specialized testing assistance

In over 15 years since its inception, Airbnb went from a niche startup to a company with a global presence and continuously expanding operations. This created the challenge of ensuring the systems were equipped to handle spikes in traffic, for example, during the holiday season. With internal teams being overwhelmed with feature development, the decision to outsource testing to an offshore partner in Asia was a no-brainer. Airbnb went for a boutique QA provider who specialized in performance and load testing. As a result, page load times were improved by 20% and the company saved 35% compared to the cost of performance testing being done in-house.

Global print-on-demand platform: Advanced solutions for complex problems
Our team partnered with a leading print-on-demand eCommerce platform handling 30+ million orders yearly. They faced the challenge of maintaining flawless performance across platforms while controlling testing costs.

As their offshore testing partner, we developed a strategy to address current challenges and adapt quickly to new ones. We supported their cloud migration, introduced AI personalization, and provided both manual and automated testing alongside their in-house developers.

US healthcare provider: Unmet expectations and communication challenges

Now, we move on to the stories of how offshore software QA doesn’t always work out for the client. The first example is a US-based healthcare software provider who developed a new, complex patient management system and was looking to cut QA costs by outsourcing the entire chunk of work to an Asian vendor. Unfortunately, the lack of documentation, the difference in communication styles, the mismatching time zones that caused delays in feedback and corrections, and the vendor’s unfamiliarity with US-specific healthcare regulations like HIPAA caused a string of problems, including significant release delays, a product recall due to failing to comply with HIPAA, and the client having to hire a local testing team to make up for the shortcomings, negating any initial savings.

Lessons learned from this example are the importance of detailed documentation, the need to establish stringent communication guidelines, and the crucial role of additional testing for offshore teams to understand industry-specific requirements.

European fintech startup: Security testing gone wrong

Startups, which are often trying to accelerate their development cycles while reducing the costs of development and testing as much as possible, frequently go for offshore testing QA services, and it’s completely understandable. The company in this example is a European fintech startup that chose to outsource testing to a vendor in Eastern Europe. The vendor, while proficient in various types of testing, including performance and functional testing, had little experience with security tests. The startup, in turn, failed to specify how much of a priority security testing was. As a result, several security vulnerabilities, including weak encryption protocols, were overlooked, leading to hackers exploiting a major security flaw and causing a data breach within a week of the release. Huge fines due to GDPR violations and the loss of customer trust were equally unwelcome outcomes.

Lessons learned from this case is the importance of finding an offshore vendor who specializes in the high-priority testing activities, whether it’s security, performance, or compatibility testing. Clear communication about the goals and desired outcomes is also a must.

Global retail giant: Failure to leverage Agile

The bigger a company is, the more likely it is to have stringent rules and practices in place, so that products are released quickly and feedback is incorporated as fast as possible. For many companies, the solution that allows them to meet those goals is Agile development and testing methodology. This is exactly what happened to the company from our last example: while preparing to launch an omnichannel shopping platform, they hired a European offshore vendor to provide continuous testing for web, mobile, and backend systems. One of the key requirements for the vendor was the ability to be able to work under Agile principles. This is where the vendor fell short: finding it challenging to keep up with the rapid iterations and continuous delivery, the vendor was consistently missing bugs, delaying feedback, and failing to fully integrate their tools with the onshore team’s DevOps environment.

Lessons learned from this situation are the vital role of clearly communicating goals and requirements before the start of the collaboration. It’s also crucial to achieve methodology and tool alignment between the offshore and the onshore/in-house teams.

Hybrid Offshore Testing: Global Execution With Local Control

For companies that want the cost benefits of offshore testing without giving up control or alignment, a hybrid model can be the best of both worlds. This approach pairs a local (onshore) lead or small coordination team with an offshore software testing center that handles most of the execution. It’s especially effective for projects requiring close integration with in-house development, high security standards, or constant user feedback.

Here is how to make the hybrid model work for your organization:

Appoint a strong onshore coordinator with deep domain and communication skills.
Use overlapping working hours for real-time updates and reviews.
Maintain a shared backlog, issue tracker, and test dashboard to ensure visibility for both sides.
Document responsibilities clearly — who drives planning, who signs off, who escalates.
Establish feedback loops that help both teams learn and adapt.

The hybrid model requires a bit more setup and management, but it often results in smoother collaboration, higher quality, and faster turnaround, especially in high-stakes or fast-moving environments.

The Evolution of Offshore QA: Most Prominent Trends

The software testing industry as a whole is one of the most rapidly developing ones, and naturally, those developments have also been noticeable in the offshore QA industry. Here are the key trends signaling where this field can go in the upcoming years:

Agile and DevOps integration. More and more often, clients are looking to integrate offshore testing into their Agile workflows to have a more predictable output and foster an effective collaboration with the development team. Moreover, offshore teams are becoming more involved in the DevOps pipeline, participating in continuous testing and helping improve the CI/CD processes.
Shift towards automation testing. Customers are becoming increasingly interested in automation as a way to speed up the testing process and minimize repetitive tasks such as regression testing. A particularly interesting trend is the use of AI-based automation for the automatic generation of scripts and predictive analysis for error detection.
AI and ML adoption in testing. Speaking of Artificial Intelligence and Machine Learning, these two major technology trends are also having a noticeable impact on offshore testing. With faster and more accurate test case generation, teams can quickly expand test coverage. ML can also be used for predictive analytics to identify areas of code that are prone to defects, while AI is widely employed for self-healing automation testing.
Focus on security and compliance. As cyberthreats are getting more common and more evolved, offshore teams are directing their efforts to in-depth security testing, including penetration testing and vulnerability assessment. Moreover, with the rise of heavily regulated industries like healthcare and fintech, offshore QA is also starting to include comprehensive compliance testing, including GDPR, HIPAA, and KYC regulations.
Specialized testing services. While many companies choose to outsource routine testing tasks, so that their in-house teams can focus on more challenging parts of QA, other companies go for the option of outsourcing specialized types of testing to offshore teams. This can include IoT testing, testing of embedded systems, high-scale load testing, mobile testing on real devices, and so on.

Bottom Line

By all accounts, the decision to offshore your software testing is a decision that pays off both in the short and long run. On one hand, you avoid overstaffing and facing the increasing costs of hiring locally. On the other hand, you get to work with highly skilled employees on your terms without having to enter strict contracts and being stuck with a large team even when you don’t have any immediate tasks for them. Offshoring your testing needs helps you get the best of both worlds — high-quality work and smarter spending — and is therefore worth being considered. And TestFort, as an offshore software testing company with 24+ years of experience, is ready to become your trusted provider.

Logistics Software Testing: How to Do Quality Assurance for Logistics

TestFort — Mon, 22 Dec 2025 13:12:50 +0000

Behind every successful delivery is a complex network of software systems working together in real time, from route planning and inventory tracking to payment processing and mobile apps for drivers. In an industry where delays, errors, or downtime can mean lost revenue and customer trust, logistics software must be fast, reliable, and secure.

However, ensuring impeccable quality of dynamic logistics software is no small task. With multiple integrations, constant data flows, and critical functionality, logistics systems require in-depth, ongoing testing. Find out why logistics software testing is more important than ever, what needs to be tested, and how a strategic approach to quality assurance can improve performance, scalability, and customer satisfaction from our article.

Why Logistics Software Testing Is More Important Than Ever
Logistics has become a high-tech, data-intensive industry. While the physical movement of goods still matters, it’s the software behind the scenes that determines whether operations run smoothly or break down. As logistics systems grow more complex, testing becomes critical not just for quality, but for business survival.

The rising complexity of logistics systems

Modern logistics environments involve a wide mix of technologies:

Legacy platforms still in use alongside new cloud-native tools
Multiple integrations with ERP, CRM, warehouse management, and transport systems
APIs for real-time tracking, customs declarations, and partner coordination
Mobile apps and portals for drivers, dispatchers, and customers
Each of these elements introduces dependencies. A small issue in one area, such as a faulty update to a routing API, can deliver impact across the entire ecosystem.

High expectations, low tolerance for failure

Given how much variety there is in the logistics industry, users are no longer going to be satisfied with subpar software applications. Customers now expect:

Real-time delivery updates
On-demand services and flexible shipping options
Seamless digital experiences across devices

To meet these expectations, logistics companies need software that performs flawlessly under pressure. But with frequent releases and ongoing digital transformation, it’s easy for undetected bugs to slip into production, unless there is a robust testing process in place.

Continuous change drives continuous risk

Logistics companies are in constant motion:

Migrating to the cloud
Rolling out new features
Expanding into new regions
Onboarding new partners

Each change introduces potential risks that must be managed across different environments, data sets, and user types. Without testing or with inadequate testing, businesses face delays, outages, compliance violations, and lost revenue.

Testing isn’t a bottleneck — it’s the central element

In fast-paced logistics operations, quality assurance isn’t a “nice-to-have.” It’s what keeps systems responsive, secure, and scalable. Testing is as important for logistics as development. The goal isn’t to slow down innovation — it’s to make sure it doesn’t break everything else.

Logistics domain testing has shifted from being reactive to proactive. Today, it’s foundational to delivering reliable services, maintaining customer trust, and staying competitive in an increasingly dynamic market.Testing isn’t a bottleneck — it’s the central element
In fast-paced logistics operations, quality assurance isn’t a “nice-to-have.” It’s what keeps systems responsive, secure, and scalable. Testing is as important for logistics as development. The goal isn’t to slow down innovation — it’s to make sure it doesn’t break everything else.

How Does the Logistics Industry Benefit from Testing?

Logistics software testing isn’t just about catching bugs — it’s about enabling smoother operations, lower costs, and better service. Here’s how quality assurance directly supports logistics businesses.

Fewer costly delays

Testing identifies issues before they disrupt order processing, routing, or shipment tracking. This reduces downtime, delivery errors, and customer complaints.

Better system reliability

Through QA involving activities like performance testing and load testing, owners can ensure that critical systems — like inventory management or driver dispatch — stay stable even under heavy load or during updates.

Smoother integrations

Testing confirms that your software works well with third-party systems, APIs, customs platforms, and payment gateways, preventing breakdowns and disruptions in the supply chain.

Accurate, actionable data

With test coverage for data validation and transformation logic, businesses can avoid bad analytics and poor decision-making caused by inconsistent or outdated data, meaning that software systems can get the necessary updates and improvements.

Faster and safer deployments

Automated tests help teams release updates quickly while minimizing risks. QA helps catch bugs early and supports CI/CD practices in logistics environments, speeding up the overall software development process, allowing you to release with confidence even under tight deadlines.

Enhanced customer experience

From shipment notifications to self-service portals, well-tested interfaces improve usability, reduce user frustration, and build trust with clients and end-users.

Scalability you can trust

As your traffic grows or your business expands to new markets, QA ensures your infrastructure and applications scale reliably, without performance disruptions, delayed deliveries, and other unwanted outcomes.

What Happens Without Quality Assurance in Place

Timely testing exists to improve the quality, efficiency, and productivity of logistics and transportation software, as well as meet the needs and expectations of the users better. Without QA, on the other hand, logistics companies take on significant risk — not just technical, but operational and reputational. Here are the other consequences you can face when you don’t take the time to fully test software prior to the release.

Disconnected operations

Without proper testing, apps and systems may not communicate effectively. That means warehouse systems might not reflect transport updates, or customer portals could show outdated delivery statuses.

Chaotic releases

Deployments often break something, especially in logistics ecosystems with legacy components, which includes most systems running today. QA reduces post-release bug correction, whereas without it, development becomes a cycle of rushed fixes.

Vulnerabilities go unnoticed

Security testing is critical in environments handling sensitive shipment data and third-party integrations. Without it, the system becomes an easy target for cyber threats or compliance violations, which can tarnish the brand’s reputation beyond repair.

Innovation stalls

Teams hesitate to introduce new features or scale operations when they’re unsure of software stability. A lack of QA creates fear of change and slows progress, causing the team to delay software updates and further frustrate the stakeholders.

Missed business opportunities

Unstable platforms or buggy customer tools can damage partner relationships, causing lost contracts or failed bids. Reliability is a key differentiator in logistics tech, helping improve business operations and increase the brand’s appeal.

Focus Points: What Needs to Be Tested in a Logistics System

Logistics software touches nearly every part of the supply chain. This means that each area introduces unique testing priorities and potential issues that might cause discrepancies and errors across the pipeline. Here are the key aspects of software systems that end-to-end logistics QA testing typically focuses on.

Order and shipment management

Teams use testing to ensure the accuracy of order processing, inventory status, dispatch workflows, and real-time shipment tracking under various real-world scenarios and data volumes.

Route planning and optimization

Test cases here focus on calculation accuracy, responsiveness to dynamic data like traffic or weather, and seamless updates across integrated systems.

Carrier and warehouse integrations

Ensure reliable data exchange between external systems (carrier APIs, warehouse management systems). Here, teams also need to test for edge cases like system outages or partial updates.

User interfaces

Whether it’s internal dashboards or customer-facing portals, it’s important to ensure software usability, responsiveness, localization, and accessibility across devices.

Data handling and reporting

Here, the team will verify the accuracy, integrity, and consistency of data used for business analytics, regulatory reports, and daily operational decisions.

Payment and billing systems

This area of testing is responsible for transaction processing, pricing logic, invoicing accuracy, and compliance with tax rules and international payment standards.

Scalability and performance

To ensure adequate performance in different conditions and situations, the team must simulate peak loads. This includes holiday spikes, new customer onboarding, or international expansion. It helps identify bottlenecks early and avoid service disruptions once a new update is deployed to the live environment.

Security and compliance

To facilitate data safety throughout all business processes, it’s vital to test for vulnerabilities, access control flaws, and compliance with standards like GDPR, SOC 2, or industry-specific regulations like CTPAT (Customs-Trade Partnership Against Terrorism).

Challenges That Logistics Software Testing Solves

Software quality in logistics isn’t just about “does it work?”. It’s also about scale, accuracy, speed, and security in highly interconnected, high-pressure environments. Below are some of the most common and critical QA challenges logistics companies face, drawn from real client experiences and case studies.

1. Legacy systems and complex architectures

Many logistics companies operate on a patchwork of legacy and modern applications, which often leads to:

Frequent production outages
Difficulties maintaining quality control across environments
Scalability limits due to outdated, overly complex architectures
Challenges in supporting in-flight releases and interdependent deployments
Testing in these environments requires an understanding of both old and new systems, and the ability to validate them as one cohesive unit.

2. Data issues: quality, migration, and consistency

Data is the backbone of logistics, but poor QA practices often lead to:

Outdated or inconsistent data after poorly managed migrations
Manual database handling that slows down operations
Out-of-sync datasets generating inaccurate analytics and customer miscommunication
The need for testing strategies that validate real-time data integrity at scale

These problems are especially severe when dealing with big data and billions of transactions annually, such as in payment systems or order management.

3. Performance and scalability under pressure

Performance failures in logistics can mean missed shipments, SLA breaches, and lost revenue. Key challenges include:

Ensuring systems can handle massive transaction volumes under peak load
Preparing for user base spikes, such as onboarding a large new client
Verifying system behavior in thousands of performance scenarios or with complex mobile apps
Compatibility of tools like JMeter for load testing the specific application architecture

Logistics QA must include rigorous performance testing and monitoring to ensure reliability under real-world usage.

4. Release speed and test automation gaps

Fast releases are essential, but they are not worth much without quality. Many companies struggle with:

Managing frequent updates across numerous parallel projects
Lack of automated regression and integration testing
High-maintenance test frameworks that slow down delivery
The need for CI-integrated automation and low-code system support

Solutions like Cypress-based test automation and synthetic monitoring have proven effective for improving speed without sacrificing stability.

5. Security and compliance concerns

In high-availability, data-driven logistics environments, security can’t be an afterthought:

Companies need live security audits and penetration testing before the solution goes live
Identifying and fixing vulnerabilities is essential for customer trust and compliance
QA must cover GDPR, trade regulations, and IT infrastructure resilience

Security-focused QA ensures systems are both robust and compliant in high-risk environments.

6. Limited in-house QA capacity

Finally, many logistics companies face challenges simply because:

Their in-house QA teams are overstretched or under-resourced
They lack the expertise in methodologies and tools for public cloud, mobile, or cross-platform testing
They need help with daily functional testing, end-to-end testing, and manual testing

Working with a specialized QA partner enables logistics firms to scale quality processes alongside product growth, instead of falling behind.

Test Automation for Logistics: Benefits and How to Implement It

Frequent updates, massive test data flows, different testing environments, and integrations with numerous third-party systems make the process of logistics system software testing highly complex. Automation helps maintain software quality without slowing down delivery, reducing manual errors and ensuring that critical operations remain uninterrupted. Here is why else you should consider automation:

Speed. Automated tests execute faster than manual tests, enabling quicker release cycles and helping teams respond to changes or issues without delays.
Consistency. Tests run the same way every time, eliminating the variability and potential oversights of manual testing and ensuring reliable results.
Coverage. Automation allows for broader and deeper test coverage, including edge cases and scenarios that are difficult or time-consuming to cover manually.
Cost-efficiency. While there’s an upfront investment in building automated test scripts, it pays off over time by automating repetitive QA activities, reducing manual effort, shortening regression cycles, and catching issues earlier in development.

What to automate in the first place?

While the benefits of automation are impossible to argue with, it’s also neither feasible nor wise to automate everything. Plus, some aspects of the software, such as the way unrelated customer notifications impact the system’s operations, are best tested manually and on real devices. At the same time, there are areas that are perfectly suited for automation:

Regression tests for core workflows. Automate frequently used functionalities like order processing, dispatch scheduling, or inventory synchronization to ensure these always work after updates.
API and integration validations. Verify data exchanges between internal systems (for example, WMS, TMS, and ERP) and third-party services like carriers, customs, and payment processors to ensure seamless operation.
Performance and load testing. Simulate peak loads to make sure your system can handle real-time processing of large transaction volumes without slowing down or crashing.
Data integrity checks. Ensure that warehouse and shipment data remain accurate and up-to-date across multiple systems, reducing the risk of misdeliveries or delays.
Security scan automation. Automate vulnerability scanning and authentication tests to strengthen defenses against data breaches and system intrusions.

How to automate logistics software testing

The testing team must approach an automation process with a clear idea of what they want to achieve in the end and how they are going to get there. Here are a few tips that will help you streamline your automation QA process and get everything you want from it:

Start small. Choose a stable, business-critical module like order tracking to automate first, allowing teams to build experience with minimal risk. Don’t jump headfirst into automating complex solutions involving IoT or sophisticated sensors.
Set priorities. Focus on test cases that are time-consuming to run manually, prone to human error, or critical for business continuity.
Build maintainable scripts. Use modular test structures and follow good coding practices to make your automation framework scalable and easy to update.
Integrate into pipelines. Plug tests into your CI/CD workflow to automatically validate builds and prevent defects from reaching production.
Scale gradually. Expand automation coverage based on test insights, team readiness, and product complexity, always validating ROI along the way.

Best Practices and Tips for Logistics QA Testing

When you want to test the system that is used for managing logistics and transportation, it’s important to not just follow a standard sequence of steps, but also include industry best practices that are relevant to your product and domain. Here are 7 expert-approved tips for making the most of your testing process.

1. Test with real-world data samples

Use realistic shipment volumes, delivery routes, and inventory levels to simulate actual operating conditions. This helps uncover edge cases that only occur under production-like loads or geographic diversity.

2. Validate time zone logic

Logistics systems often span countries and continents. Ensure time-based operations like dispatch scheduling, delivery ETA, and cutoff times behave correctly across multiple time zones.

3. Monitor third-party dependencies

Testing shouldn’t stop at your system boundaries. Monitor carrier APIs, payment gateways, and customs systems during integration testing to detect external failures and implement fallback mechanisms.

4. Test for offline and reconnect scenarios

In field operations (like warehouse or delivery apps), network interruptions are common. Validate how your system behaves when going offline and reconnecting, especially for data syncing and order updates.

5. Track and test compliance rules

Different regions enforce unique transport, storage, or customs regulations. Build test cases that validate compliance with laws like temperature tracking for perishables or document handling for cross-border shipments.

6. Use exploratory testing for edge flows

Automation covers the routine, but logistics systems often face non-standard flows (for example, package re-routing or emergency drop-offs). Use exploratory testing to uncover usability or logic flaws in these irregular paths.

7. Test mobile devices in realistic environments

With at least 54% of logistics companies adopting mobile technology, and that number expected to grow significantly in the near future, mobile testing is a must. If your logistics system includes driver or scanner apps, it’s crucial to test on various devices, operating systems, and under conditions like glare, gloves, or moving vehicles.

When to Start Logistics System Software Testing and How We Can Help
Logistics domain testing is a multi-faceted operation that can bring sizable benefits at every stage of the development and deployment process. Here is where software testing makes particular sense for logistics software.

1. During planning and architecture

QA can help identify risks tied to legacy systems, data migration, and scalability. Early involvement allows you to map dependencies, validate feasibility, and catch structural issues that might delay or derail development later on.

2. Before and during integration

When multiple modules or third-party services are about to be integrated — like route optimization tools, payment gateways, or ERP systems — QA ensures that each component functions reliably and securely within the ecosystem.

3. Before major data handling operations

Logistics systems handle vast volumes of real-time data. QA plays a vital role in ensuring data consistency, accuracy, and integrity across systems, especially during migrations or when building analytics pipelines.

4. When scalability is a concern

Whether preparing for peak season, onboarding new enterprise clients, or expanding globally, performance testing can help simulate high loads and prevent slowdowns, failures, or outages.

5. When your team is under pressure

Rapid releases, frequent requirement changes, or QA bottlenecks can overwhelm internal teams. Bringing in external experts helps ensure continuous quality without slowing development.

How we can help

Testing a logistics system in-house can be daunting. These platforms often involve interconnected modules — like inventory, warehouse, transport, ERP, and billing — each with specific performance, compliance, and data handling requirements. Add in third-party integrations, mobile use cases, and real-time data processing, and you’re looking at a highly complex QA challenge. Many internal teams struggle with limited QA expertise, lack of domain knowledge, or pressure to release fast without compromising quality.

That’s where we come in.

With over 20 years in software testing services and extensive experience performing quality assurance for logistics platforms, we understand the business and technical demands of this industry. Our team knows how to build stable testing strategies even in high-pressure, high-complexity environments. We work as a seamless extension of your team, adapting to your processes, timelines, and goals. Whether you’re launching a new system, modernizing legacy infrastructure, or scaling operations globally, we help you do it with confidence and quality.

Here’s how we support your logistics system testing:

QA strategy development tailored to logistics. We start by assessing your system’s architecture, integrations, and business logic to develop a QA roadmap that matches your goals and operational needs.
Comprehensive testing across all modules and touchpoints. From warehouse systems to mobile driver apps, we test across devices, interfaces, and environments to ensure reliability, usability, and interoperability.
Implementation of test automation at scale. We build or extend automated test suites to cover frequent workflows, regression checks, and data validation tasks, saving time and reducing release risk.
Specialized testing: API, performance, data, mobile, and more. We test APIs and integrations under real-world loads, validate data accuracy across systems, ensure mobile UX consistency, and stress test infrastructure.
Compliance, security, and failure recovery validation. We ensure your logistics platform is resilient, secure, and compliant with relevant standards like GDPR and ISO, and can recover quickly from system failures or outages.

Final Thoughts

In logistics, where delays, errors, or inefficiencies are felt across entire supply chains, quality assurance isn’t a luxury — it’s a necessity. As logistics platforms grow more complex, integrated, and mobile-driven, ensuring reliability, security, and scalability becomes a continuous effort. Effective testing gives you control over that complexity and confidence in your system’s performance, even under pressure.

Whether you’re digitizing operations, expanding your service scope, or launching a new product, the right QA partner can make all the difference. With a focused strategy, proven tools, and logistics-savvy testers, we help you keep your systems running smoothly and your customers happy.

Test Automation as a Service: The Complete Guide to TAaS

TestFort — Fri, 07 Nov 2025 15:04:26 +0000

Automated software testing as a service is one of the biggest buzzwords of the testing industry of 2024, and it can only get bigger from here. Automation testing as a service is a relatively new service that takes the convenience, cost efficiency, and reliability of software testing outsourcing to the next level. But what exactly is automated testing as a service, how does it work, and how can it benefit your company? Here is all the information you need to decide whether automated testing as a service (TAaS) is right for you.

Key Takeaways

TAaS, or test automation as a service, is the practice of entrusting some or all of the company’s testing needs to an outside service provider — typically, a vendor with rich professional experience in automation and complete availability of human and technical resources.
TAaS works like this: the client identifies their automation testing needs, either on their own or together with the vendor; the team plans the project, and the client oversees its completion. As a rule, the client does not need to be involved in the project on a daily basis.
By nature, automation as a service is similar enough to testing outsourcing, but it also has several important differences — mainly when it comes to work structure, pricing, flexibility, and level of involvement required from the client.
The main use cases for using automated testing as a service include the need to speed up the software release cycle, the limited in-house resources, the requirement for highly specific types of testing or testing technologies, and the desire to get a fresh outside perspective.
Key situations where TAaS may not prove to be the ideal solution include small or short-term projects, projects involving sensitive data or intellectual property, and situations where automation is the core business of the company.
The biggest benefits of automation as a service include the ability to release software faster and be more confident in its quality, the opportunity to focus on core business processes, the easy scalability and unparalleled flexibility, and the fast access to advanced automation tools and technologies.
The drawbacks of the service include the often delayed project start due to the extensive amount of planning and preparations, the higher cost compared to outsourcing, and the client often being detached from the project while the team works mostly autonomously.
A few things to look for in an ideal vendor include a reasonably wide range of services, a proven track record of similar projects, compliance and security standards, continuous availability, and readiness to match the client’s work practices and requirements.

What is Test Automation as a Service?

Test automation as a service is the practice of entrusting some or all of the testing needs to an outside service provider. The service provider is usually a vendor who does automated testing professionally and has a sufficiently sized team to handle multiple projects at once for their clients. An automated testing as a service solution is typically designed for long-term cooperation, starting from two months, although shorter-term projects are also possible — for example when a client needs an automation testing audit or requires help in setting up the test environment or training the team to then perform automation in-house.

TAaS projects do not always deal with automating testing from scratch. Very often, the company will start automation testing in-house, but as the size of the test suite grows while the size of the team remains the same, the company may come to realize that it can no longer handle the increasing body of work on its own. This is where an automated testing as a service solution can take over application testing and let the team go back to the core tasks.

How Does TAaS Work?

What happens after you decide your company needs automation testing as a service? The specifics will always depend on your requirements and the vendor’s tried and tested approach to automated software testing as a service. However, most TAaS projects go over a certain sequence of steps, which include:

1) The client identifies their testing needs. Most companies arrive at the decision to use automation as a service after their current testing procedure no longer satisfies them in terms of software quality or resources spent. This is why, for a TAaS project to go the right way, the client needs to know for sure what they want to achieve in the end.
2) Together with the client, the vendor will meticulously plan the automation project, using tangible methods to demonstrate the process and the outcomes to the client. It can be a test plan, test strategy, proof of concept of a specific framework, or sample tests to showcase the vendor’s expertise.
3) Upon agreeing on the scope of testing, project timeframes and milestones, and composition of the team, the external AQA department can finally get to work. In most cases, the TAaS team works autonomously from the in-house operation while giving regular updates and communicating project changes to the client.
4) The vendor’s team automates test after test, going over the initial plan and taking into account the client’s requirements, which can sometimes change over time. All test results are documented and transferred to the client in the form of deliverables.
5) The project goes on for as long as specified in the contract. The client can make additional requests, expand the scope of work, and let the project continue after the initial portion of tasks is completed. The client can also downscale the project, leaving just one or two AQA engineers for ongoing project maintenance.

How Is TAaS Different from Testing Outsourcing?

Test automation as a service and automation outsourcing are very similar in their nature: they both reduce the strain on the company’s software development and management teams, allowing an organization to automate testing efficiently and with fewer resources used. The difference between the two types of services can be blurry, and companies often use these terms interchangeably.

Taras Oleksyn, Head of AQA, TestFort
“A company providing automation testing as a service usually offers TAaS as its specialty and the only type of service available. Outsourcing companies typically offer a comprehensive range of services, which include both automated and manual testing, and often even development services. But when a vendor is only focused on testing automation, they are more likely to possess all the knowledge and tools needed to do a perfect job. ”

However, upon closer inspection, it’s easier to see how the two types of automation services are not the same. The key difference is the level of involvement in both types of projects. Outsourcing is typically used as an extension of the in-house operation: you can hire one or more specialists with the required expertise to strengthen the internal team, perform an audit, or set up an automation department. However, the client is typically responsible for most of the project features, from the infrastructure to test planning, and is continuously involved in the process.

With TAaS, the client usually entrusts the entire automation testing project to an outside vendor. Vendors offer turnkey automation solutions, so the customer can enjoy solid results with minimal involvement. All the client needs to do is select the vendor, specify the request, select the size and composition of the team together with the vendor, and agree on the project goals and milestones. The rest of the project, from hiring decisions and setting up the cloud infrastructure to the day-to-day work of the automation department, is the vendor’s responsibility.

So, if you are looking for minimal involvement in the project while still meeting your goals faster, then test automation as a service may be exactly what you need.

Taras Oleksyn, Head of AQA, TestFort
“Another difference between outsourcing companies and TAaS companies is that TAaS vendors often have a killer feature that outsourcing companies may not have. Personally, I’m witnessing a surge in the number of vendors that offer AI as a way to enhance the automation process, and we can expect it to become even more ubiquitous in the industry in the upcoming year.”

When Should You Use TAaS and When Should You Perform Testing In-House?
Automation as a service’s surge in popularity is not accidental: as more organizations discover the benefits of turnkey automation services and find a way to incorporate them into their software development and testing routine, we can expect TAaS to be featured in even more projects. Here is when it makes particular sense to use automated software testing as a service:

You want to speed up your release cycle. In a highly competitive world, where any new software solution will face several established rivals, the ability to release software fast and with fewer bugs becomes a crucial advantage. Automated testing helps shorten the release cycle without sacrificing the quality.
You lack in-house resources or expertise. Establishing testing automation from the ground up is a big endeavor that requires strong AQA expertise and enough resources to fill key roles. With TAaS, you can find specialists even with the rarest expertise and quickly assemble the perfect team.
You need highly specific types of testing. There are testing types, such as compatibility testing or regression testing, that most testers are familiar with and can do successfully. But what if your project requires activities like hardware and IoT testing or HIPAA compliance testing? In that case, your best bet is a TAaS vendor with proven expertise in the required field.
You require an outside perspective. When automation is done by your internal development team, the results of testing may be biased, as developers want software to work and are not conditioned to look for bugs. An outside team is free of this bias and can therefore deliver more reliable results on a continuous basis.
You want to leverage cloud services. While it is possible to use the cloud on most AQA projects, a typical automated testing as a service solution actively employs cloud services to establish the necessary infrastructure, analyze test data and generate reports, improve test scripts, and implement a more efficient CI/CD process.

We’ve already talked about the most popular use cases for automation as a service. Clearly, TAaS is a great fit for a wide range of projects and testing needs. However, we won’t go as far as to claim that turnkey automation works for everyone. Here is when establishing an internal AQA department is a better idea:

You have a small, short-term project. While it’s possible to automate even a small testing project to make it even more cost-efficient and consistent, investing in automated testing as a service makes better financial sense for medium and small automation projects. Smaller AQA jobs can be left to the development team, provided that the team members have the necessary expertise and the time outside of their main scope of work.
Automation is your core business. When you have other organizations relying on your company for testing automation, the best way to ensure the unwavering quality of work and the loyalty of your customers is to do everything in-house. When you hire specialists for your internal team and help them develop into automation experts, you can always expect splendid results and industry success.
You work with highly sensitive data. A reputable TAaS vendor will always take all the necessary precautions to ensure the security of the testing process and the integrity of the data used. However, when your AQA project involves large volumes of sensitive data, especially when it’s data that belongs to the users, following every rule to protect the information may turn out to be too much of a hassle.

How Can TAaS Benefit an Organization?

Automated software testing as a service brings a number of benefits to any organization dealing with software. Here are the biggest benefits of automation as a service and why you should consider it in the first place.

Higher-Quality Software Developed Faster
The first and biggest reason to invest in automation testing as a service is that it allows you to develop better software faster and with fewer internal resources used. You can avoid unnecessary spending on hiring an entire automation team, skip the lengthy hiring and onboarding period, and increase the scope of testing, all while having the quality of your software improve dramatically and the cost of fixing a bug noticeably drop.

Focus on Core Business

For most companies, automation testing is just a part of ensuring the spotless quality of their software products, not their core business. In that case, entrusting testing to an external team is a perfectly sensible decision that frees your mind and the minds of your team members. It allows them to focus on further developing and improving the solution while being absolutely confident in the quality of the solution.

Long-Term Strategy and Smart Planning

When your organization lacks the resources to test software in-house, there are different ways to fill those gaps. Some companies choose to work with freelancers or gig employees, and this strategy usually makes sense from a financial standpoint. However, continuous cooperation with a TAaS vendor allows you to build a long-term QA strategy and plan your project with meticulous attention to detail, and that may turn out to be more valuable than the ability to cut costs.

Easy Scalability

Operating an internal AQA department usually means that you have to make the same team setup work no matter how the project needs to change. This is not the case with automated testing as a service solution, as this type of cooperation offers unprecedented scalability. You can increase the size of the team to handle a bigger load of tasks or scale it down during a slow period in a matter of days or even hours.

Complete Visibility

One of the reasons why some organizations are wary of working with remote teams is that they worry about the level of visibility and oversight that is available to them. However, reputable TAaS vendors offer transparency as a core benefit of their services. You can get updates as often as you want and can always have the most complete idea of where your project currently stands and where it is expected to go.

Cost Savings

TAaS eliminates the need for companies to invest in expensive testing tools, infrastructure, and in-house expertise. With a pay-as-you-go model, companies only pay for the services they use, which reduces overall costs. Additionally, TAaS minimizes long-term operational expenses, such as tool maintenance and upgrades, making it a cost-effective option for businesses of any size.

Continuous Integration Support

TAaS integrates seamlessly with Continuous Integration (CI) and Continuous Delivery (CD) pipelines, enabling frequent automated testing. This helps identify bugs early in the development cycle, ensuring quick feedback and reducing the time it takes to release updates. This integration not only improves efficiency, but also enhances collaboration between development and testing teams.

Broad Tool Access

Automation testing as a service providers often have access to a wide variety of testing tools, from open-source to enterprise-grade solutions. This allows companies to leverage the best tool for their specific testing needs without the need to invest in expensive licenses. It also enables organizations to stay current with the latest tools and technologies without the overhead of managing them internally.

Risk Reduction

Automated testing as a service reduces the likelihood of human error, ensuring consistent and reliable test execution. TAaS minimizes risks by identifying and addressing bugs earlier in the development cycle, improving overall software quality. By reducing the reliance on manual testing, companies can mitigate potential vulnerabilities and ensure a more stable and secure software product.

Cost-Benefit Analysis: TAaS vs. In-House Testing

Broad Tool Access

Risk Reduction

Are There Any Downsides to Automation as a Service?

Automation as a service is a model that only continues to grow in popularity. However, that is not to say that it’s completely free of flaws, although the flaws usually stem from TAaS not being the right fit for the project, not the testing engagement model itself. These are the main disadvantages of TAaS:

High project cost. While automation as a service is associated with cost savings compared to establishing an in-house automation department, it is still more expensive than outsourcing the project to an external vendor because TAaS has a broader scope of services included in the project budget.
Delayed project start is possible. With testing outsourcing, especially when using the dedicated team or time and material models, it is possible to launch a project in a matter of days. With TAaS, more precise calculations and planning are required before the start of the project, which can increase preparation time.
Varying level of control. The nature of automation as a service, which offers turnkey solutions for organization, implies minimal control on the client’s side. The client will get regular updates and deliverables stipulated in the contract, but it’s not very common for them to be involved in daily operations. With outsourcing, where clients often use a mix of in-house and external staff, there is a higher level of control available.

Types of Testing Test Automation as a Service Can Handle

Any testing expert will agree that the scope of potential uses of automation is vast. However, in the experience of many organizations, it doesn’t always make sense to hire a professional TAaS vendor to handle 100% of the company’s automation needs. Here are the types of testing that automation as a service is best equipped to handle.

GUI Testing

User interface testing used to be mostly performed by manual testers and heavily relied on the human eye. However, software testing automation technology has come a long way, and now it’s not just possible to automate GUI testing, mimicking the way real users interact with software — it’s one of the commonly requested types of TAaS.

Functional Testing

The functionality of a desktop, mobile, or web application is what attracts users in the first place, which is why comprehensive functional testing is a must. Modern automation software testing tools provide full coverage and help spot every defect, and it’s a typical component of any TAaS solution.

Unit Testing

Unit testing deals with the smallest fragments of software to make sure they work flawlessly on their own and are ready to be integrated into the main solution. For software applications that are constantly growing and changing, automated unit testing is likely the only option to parallel test multiple units at once to speed up the releases.

Load and Performance Testing

Stable performance, even during unexpected events such as load spikes, is critical for a software solution’s spotless reputation. All-encompassing automated load and performance testing takes a lot of effort and resources when done in-house, whereas entrusting it to a TAaS vendor gives you a clear idea of your app’s performance without affecting your day-to-day operations.

Security and Compliance Testing

Users won’t accept anything less than a secure, impenetrable application that protects their personal and financial information and is compliant with all applicable laws and regulations. Automated security testing allows the team to expand the number and complexity of test cases to ensure absolute security and all-around compliance.

Compatibility Testing

Developing a functional application with a strong user appeal is not an easy feat, but the number and variety of possible platforms can complicate things even further. Automating compatibility testing helps you test the smooth performance of your solution on every hardware and software combination, both on real and virtual devices.

Regression Testing

Automated regression testing is crucial when the development team produces frequent code changes and there is a need to maintain the optimal quality of the application. TAaS vendors have a robust selection of tools and techniques to automate regression testing to allow you to do more each sprint.

API Testing

With thousands of APIs available in the software market, every product owner or developer has the opportunity to enhance the functionality and usability of the application. However, APIs also need to be rigorously tested, both on their own and as part of the bigger solution, and automated API testing can locate every bug before it has the chance to affect the overall performance of the app.

Industry-Specific Uses of Automation Testing as a Service

Test Automation as a Service offers tailored solutions across various industries, addressing each industry’s unique challenges and requirements. By providing specialized testing strategies, TAaS can ensure that industry-specific regulations and needs are met efficiently.

1. Healthcare

In healthcare, software must comply with strict regulatory standards such as HIPAA or FDA regulations, ensuring data privacy and patient safety. TAaS can automate compliance checks, functional tests, and data integrity validation, ensuring that healthcare applications meet regulatory standards without manual intervention. Automated tests also improve efficiency in handling large volumes of medical data, ensuring that critical applications like Electronic Health Records (EHR) systems, telehealth platforms, and medical devices operate safely and reliably.

2. Finance

For the finance industry, security and accuracy are paramount. TAaS providers can automate security testing, including vulnerability scans and penetration testing, ensuring that applications comply with financial regulations such as PCI DSS and SOX. TAaS can also handle load testing for high-frequency trading platforms, banking apps, and payment gateways to ensure they function seamlessly during peak usage. Automated regression testing helps quickly verify that updates or patches do not disrupt critical operations or introduce security risks.

3. Retail

Retail applications, especially e-commerce platforms, require scalability and performance testing due to fluctuating traffic patterns, particularly during sales events. TAaS can automate testing across multiple browsers and devices, ensuring that customers enjoy a seamless shopping experience. Additionally, TAaS enables real-time testing of payment systems, inventory management, and order processing, ensuring that the backend systems run smoothly.

4. Telecom

In the telecom industry, TAaS is used to automate testing for large-scale networks, billing systems, and customer service platforms. Telecom companies benefit from continuous performance and stress testing to ensure network reliability, especially during peak usage.

5. Manufacturing

For manufacturing, TAaS ensures that critical systems, such as supply chain management, product lifecycle management, and IoT devices, are thoroughly tested. Automation helps ensure system integration, data accuracy, and uptime for smart factories.

Frameworks Used in Test Automation as a Service

A successful automated testing as a service solution starts with a well-selected framework. A framework plays a crucial role in enhancing the efficiency, consistency, and scalability of a Test Automation as a Service project. It helps make the testing process more standardized and consistent, allows for better reusability of test components, improves test maintenance and scalability, and ensures better reporting. The frameworks used in TAaS are similar to the frameworks used in regular testing projects but provide some specific and valuable benefits to automated testing as a service. Here are the four frameworks used in TAaS.

Data-Driven Framework

In a data-driven test automation framework, test data is stored separately from the test scripts, often in external files like Excel, CSV, or databases. This allows the same set of test scripts to be executed with different data inputs. For TAaS, data-driven frameworks provide flexibility in testing various scenarios without changing the test code, increasing test reusability and maintainability. It is particularly useful for testing large datasets and helps reduce redundancy in test case creation.

Modular-Driven Framework

A modular-driven framework divides the entire application under test into independent, reusable modules or functions. Each module contains a part of the test script, and these modules are combined to form more complex test cases. In the context of TAaS, modular-driven frameworks enhance test maintenance by isolating changes to specific modules. This reduces the impact of application updates on automated tests and makes it easier to manage testing efforts for complex applications.

Keyword-Driven Framework

In keyword-driven frameworks, test cases are written using keywords that represent actions to be performed on the application under test. Keywords like “click,” “input,” or “verify” are combined with test data to automate test execution. TAaS providers use keyword-driven frameworks to enable non-technical testers to contribute to test automation without writing code. By abstracting the technical details, this approach simplifies test design and allows businesses to achieve faster automation with less technical expertise.

Behavior-Driven Development (BDD) Framework

Behavior-Driven Development (BDD) frameworks, like Cucumber or SpecFlow, focus on creating test cases in a human-readable format, using plain language syntax like Gherkin. BDD emphasizes collaboration between developers, testers, and business stakeholders by aligning test cases with business requirements. In TAaS, BDD frameworks enhance communication between teams and ensure that test automation aligns closely with business goals. By fostering clearer requirements and better test documentation, BDD frameworks improve test coverage and reduce misunderstandings during software development.

What to Look for in a Reliable TAaS Vendor

Test automation as a service is rapidly growing in popularity, and so is the number of vendors providing this type of service. However, just like with outsourced testing, the success of the project largely depends on the vendor and their work practices and quality standards. There are many components to a mutually satisfying TAaS cooperation, but here are the key features to consider when choosing among the available vendors:

Range of services. The first thing to check is whether your potential vendor actually offers the services that you’re seeking. For example, some TAaS vendors limit their range of services to just a few testing types, such as regression testing or compatibility testing, while others provide more all-encompassing services.

Proven track record. Even though automation as a service is a relatively new field, a typical TAaS company has been providing regular automation services for years. This means it needs to have a significant number of automation projects completed and plenty of former clients who can attest to its quality of work.

Security standards. Software testing and automation projects often deal with sensitive data and intellectual property. This is why every employee on the vendor’s team must be aware of possible safety risks, a secure testing environment must be created, and there must be an NDA in place that specifies all security-related provisions.

Compliance standards. Closely connected to the aspect of security is the aspect of compliance. When developing software for a specific industry — such as healthcare or fintech — it is crucial that your automation testing partner is aware of all required compliance standards and knows how to incorporate them into the testing process.

Availability. At the end of the day, the main reason you are choosing automation as a service is probably to speed up the testing cycle and create a smooth cooperation with the in-house development and testing process. And that is very difficult to achieve if the vendor’s team is only available during your off-hours and the vendor is not ready for compromise regarding the team’s availability.

Final Thoughts: Is TAaS Worth It?

With test automation as a service making strides on an increasingly larger scale, more and more organizations will start to consider adding TAaS to their software quality-related process. And, given the many benefits TAaS brings, from speeding up the development and testing process to lowering the cost of fixing a bug, it’s easy to see why.

Moreover, the flexible and scalable nature of TAaS makes it a perfect fit for most software testing projects. This is why, if your goal is to develop flaw-free software, it may be time to start thinking about automation testing as a service and how to incorporate it in your organization.

Mainframe Testing: Process, Challenges, Best Practices, and More

TestFort — Fri, 10 Oct 2025 13:45:02 +0000

Originally developed in the 1950s, mainframes are powerful, high-performance computing systems designed to handle large-scale transactions and data processing for enterprises. Despite their age, mainframes have only gotten more ubiquitous with age, continuing to be the backbone of critical industries like banking, healthcare, and retail.

Right now, mainframes handle 68% of global IT workloads while consuming just 6% of overall IT expenses. This exceptional reliability, availability, and security allows mainframes to remain widely used despite the existence of cloud solutions and edge computing. At the same time, mainframes require rigorous testing to ensure uninterrupted operations, data integrity, and seamless integration with modern applications. Find out how to test mainframes and what else to know to make the testing process even more effective.

What Is Mainframe Testing?

Mainframe testing is the process of testing software located on mainframe systems. The mainframe testing definition implies that its main aim lies in confirming that the execution and reliability of software or application are at the highest level and that the app is fully prepared to launch. It is performed on the deployed code for various data combinations set into the input file. Mainframe testing uses various validation procedures and approaches to ensure everything goes flawlessly.

What Is Mainframe Testing?

Classification of Mainframe Manual Testing

According to the traditional classification, two significant types of mainframe testing are distinguished: online and batch job testing.

Online testing

Online testing is essentially testing CICS (Customer Information Control System) screens, the process similar to testing the web page. One can modify the functionality of the existing screens or add new ones. Applications can have inquiry and update screens. Thus, both screens’ functionality has to be checked in terms of online testing.

Batch testing

The batch job testing is the implementation of batch jobs for every functional element in the recent release. Mainframe batch testing is a two-phase process. At first, every job gets checked individually. Then, the interaction between them is validated by providing a file to the first job and validating the database.

Key Benefits of Mainframe Testing

Mainframe testing is a complex process that requires a lot of time and resources, but it’s not something that can be considered optional, given how much of the company’s digital ecosystem depends on the mainframe’s normal operation. The advantages of performing mainframe testing include the following positive impacts:

Making optimal use of resources: Ensures efficient allocation of hardware, software, and personnel by identifying bottlenecks, optimizing performance, and reducing system inefficiencies.
Avoiding redundant work: Prevents teams from redoing tasks by catching defects early. Automated regression testing eliminates the need for repeated manual efforts.
Improving user experience: Ensures that the system displays fast response times, reliable transactions, and seamless functionality, leading to higher user satisfaction and fewer complaints.
Reducing production downtime: Minimizes unexpected failures through proactive testing, ensuring uninterrupted operations and preventing financial and reputational damage.
Boosting customer retention: Reliable systems build customer trust, encouraging long-term loyalty by reducing service disruptions and ensuring smooth transactions.
Lowering IT operational costs: Prevents expensive post-deployment fixes, reduces maintenance efforts, and cuts down on manual testing through automation.
Ensuring compliance and security: Helps meet industry regulations and security standards by identifying vulnerabilities before deployment, protecting sensitive business and customer data.
Enhancing scalability and future readiness: Enables mainframe systems to handle growing workloads and integrate with modern technologies, supporting long-term business growth.

Mainframe Attributes: What to Focus On in Testing

In mainframe testing, there are the following mainframe features that should be considered:

Virtual storage
It aims to make disc storage more economical and efficient by simulating that real storage is more extensive.

Multiprogramming
The essence of it is understandable — a computer runs multiple programs simultaneously.

Batch Processing
This method allows for completing the task in parts.

Time-Sharing
Every user gets access to the system via the terminal device.

SPOOLing
Simultaneous Peripheral Operations Online is utilized to collect and save the output of an application or software, then the result may be sent to devices.

Best Approaches and Methodologies for Mainframe Testing

Mainframe application testing has been around long enough to give testers time to develop extensive knowledge of mainframe testing and design approaches and methodologies that take the efficiency of this testing task to the next level. Here are the most popular methodologies used in mainframe testing today.

Agile adaptation for mainframes

While mainframe systems traditionally follow waterfall methodologies, Agile principles can be applied to improve flexibility. This involves breaking testing into smaller iterations, enabling faster feedback and defect resolution. By adopting Agile ceremonies — such as sprint planning and retrospectives — mainframe teams can align better with modern development cycles.

Parallel and shift-left testing

Testing earlier in the development lifecycle prevents costly defects in production. By running functional and integration tests in parallel with development, teams can shorten release cycles without compromising quality. Implementing shift-left testing ensures that potential issues are identified before they impact operations.

Model-based testing

This approach uses predefined models of system behavior to generate test cases automatically. It is particularly useful for complex mainframe applications where manual test case design is time-consuming. Model-based testing helps increase coverage while maintaining consistency across test executions.

Hybrid cloud testing

Combining cloud resources with on-premises mainframe environments enables scalable testing while reducing infrastructure constraints. Hybrid cloud testing provides the flexibility to run large-scale performance tests and validate integrations with modern applications.

Types of Testing Used for Mainframes

Mainframes and approaches to testing them may be unique, but the types of testing mainframe solutions are not that different from the types used for regular software. Here are the types of testing most widely used in the mainframe testing process today.

Functional testing

Functional testing ensures that mainframe applications work as expected by validating business logic, data processing, and system interactions. It includes unit, integration, and system testing to confirm that individual components and end-to-end workflows function correctly.

System testing and system integration testing

This methodology involves the two testing types — batch and online — we have talked about earlier, as well as two others — online-batch integration and database testing.

Online-batch Integration testing is where the data of the online screens, batch jobs, and interaction between those two are checked.

Database testing is related to databases in which the data from the mainframe application layout and storage are validated.

System integration testing, in turn, aims to check the work of all the systems interacting with the tested system. The interacting systems don’t get directly impacted by the requirement, though considering that they use data from the primary system, one should validate the flow between them and their resultant actions.

Regression testing

This one is typical for any project. This testing activity aims to assure that the new functionality does not affect batch jobs and online screens that are not supposed to interact with the system directly and that new updates or fixes do not introduce defects in previously working functionalities.

Performance testing

Performance testing evaluates how mainframe applications handle high workloads, including transaction volumes and concurrent user requests. Load, stress, and scalability tests identify bottlenecks to ensure optimal system performance.

Security testing

Security testing is used to detect vulnerabilities, unauthorized access risks, and data breaches. It includes penetration testing, encryption validation, and compliance checks to ensure mainframe systems meet regulatory and cybersecurity standards.

Usability testing

In the context of mainframe testing, usability tests focus on the user experience for mainframe interfaces, ensuring accessibility, efficiency, and ease of navigation. This is especially important for modernized mainframe applications with web or mobile frontends.

Data validation testing

This activity tests data integrity, consistency, and accuracy in mainframe databases. It ensures that data transformations, migrations, and batch processing deliver correct results.

Disaster recovery testing

Disaster recovery testing is done by simulating system failures to verify backup and recovery processes. It ensures that mainframe environments can restore operations after hardware failures, cyberattacks, or data corruption.

How to Do Mainframe Testing

Each testing project is unique, and the range of activities largely depends on the specifics of the hardware and software parts, as well as business and technical requirements. At the same time, the testing procedure for mainframes can be broken down into several standard steps. Typically, the mainframe testing process consists of several stages, one leading to another, and here is what this process usually looks like.

Stage #1: Planning

The initial stage focuses on preparation. Teams must document the expected modifications and assess their impact on existing processes. Test cases and scenarios are then designed to ensure all affected areas are covered.

Stage #2: Scheduling

Once documentation is complete, testing teams align their schedule with the overall project plan. Proper scheduling ensures smooth collaboration between development and testing teams while minimizing disruptions to critical systems.

Stage #3: Deliverables

Before execution begins, all test deliverables are reviewed. Test plans, scripts, and data sets must be clearly defined and aligned with business objectives to ensure comprehensive validation.

Stage #4: Implementation

This stage involves executing test cases in two key phases:

Testing requirements: Validates system functionality based on documented specifications.
Testing integration: Ensures seamless data exchange across interconnected applications and system components.

Stage #5: Test execution and monitoring

After the implementation phase, the testing team executes the test cases in the mainframe environment. Continuous monitoring during execution is essential to track system performance, identify bottlenecks, and ensure that all requirements are met without affecting production operations.

Stage #6: Defect identification and fixing

During the execution, defects are identified, documented, and shared with the development team. The development team works on resolving the issues, followed by a re-test to ensure the fixes were successful. This cycle may repeat until the system passes all test scenarios.

Stage #7: Reporting

Test results are consistently shared with the development team for quick resolution of defects. Clear communication between teams enables faster issue resolution and ensures testing aligns with business goals.

Stage #8: Regression testing

After defects are addressed, regression testing is performed to ensure that recent changes do not negatively impact existing functionality. This step is crucial in maintaining the integrity of legacy systems while implementing updates or new features.

Stage #9: Project sign-off

Once all testing activities are completed, a final report is prepared, summarizing test results, defects, and resolutions. A sign-off from all relevant stakeholders indicates that the system is ready for deployment, ensuring business leaders are aligned with testing outcomes.

Stage #10: Post-deployment monitoring

After the system is deployed, post-deployment monitoring is critical for identifying any unexpected issues that may not have been caught during testing. This allows teams to address them proactively before they impact business operations.

Test Automation for Mainframe Solutions: Automation Testing Tools and Strategies

Manual testing is the cornerstone of ensuring the quality, stability, and security of mainframes. At the same time, most winning QA strategies typically include at least some degree of mainframe test automation. Mainframe automation testing is critical for maintaining the reliability and efficiency of applications while keeping up with modern development practices. Automating mainframe testing reduces execution time, enhances accuracy, and ensures continuous validation of critical business processes. It also enables integration with DevOps workflows, supporting faster releases and minimizing system downtime.

There are automation techniques suitable for pretty much every aspect of mainframe quality. Still, certain areas are a better fit for automation efforts — whether due to the fact that they are too time-consuming and resource-intensive when done manually, or due to the sheer amount of testing that needs to be done. However, several aspects of mainframe testing can particularly benefit from automation:

Regression testing ensures that updates and bug fixes do not disrupt existing functionality.
Performance testing assesses system response times and stability under heavy loads.
Security testing automates vulnerability scans and compliance checks.
Data validation testing verifies data accuracy across mainframe databases.
API and integration testing ensures seamless communication between mainframes and modern applications.

Tools and techniques for mainframe automation testing

Selecting the right tools is crucial for successful mainframe test automation. Mainframe environments have unique architectures, requiring specialized solutions that can handle batch processing, complex data flows, and integration with modern applications. The best tools support functional, performance, security, and regression testing, ensuring comprehensive coverage across different mainframe components, as well as successfully integrating with DevOps and CI/CD pipelines, enabling continuous testing to align with agile development cycles.

Additionally, they should support service virtualization to allow testing without direct access to production environments, reducing costs and system dependencies. Several tools available today also support automated mainframe testing:

IBM Rational Test: Automates functional, regression, and performance testing for mainframes.
CA Test Automation: Provides automation for batch and online transactions.
Selenium: Useful for automating web interfaces connected to mainframe applications.
JMeter: Conducts performance and load testing for mainframe transactions.
Micro Focus UFT: Supports automated GUI and API testing for legacy and modernized mainframe applications.

Challenges of Mainframe Testing

Mainframes are built to deal with highly specific tasks, which makes the challenges of mainframe testing highly specific as well. Here are the most common challenges the QA team must be prepared to tackle when dealing with this type of testing.

1. Limited testing environments

Mainframes are high-cost, mission-critical systems, often shared across multiple teams. Limited access to mainframe environments delays testing, while dedicated test environments can be prohibitively expensive. Additionally, executing tests on production systems risks data corruption and service disruptions.

How to overcome: Service virtualization enables teams to create simulated mainframe components, allowing testing without direct system access. Cloud-based mainframe testing solutions provide scalable environments at lower costs, improving flexibility while reducing dependency on physical mainframes.

2. Long execution times

Mainframes process vast amounts of transactions and rely on batch jobs, which can take hours or even days to complete. Running full regression and performance tests on these systems can significantly delay software releases. Manual testing further compounds the issue, making iterative testing cycles impractical.

How to overcome: Automating regression and performance tests helps speed up execution. Risk-based testing strategies focus on high-impact areas, reducing unnecessary test cases. Additionally, parallel processing techniques can help optimize execution times for large data sets.

3. Skills shortage

Many experienced mainframe professionals are nearing retirement, and few younger IT professionals specialize in mainframe technology. As a result, companies struggle to find skilled testers with expertise in legacy systems, leading to delays and increased costs.

How to overcome: Upskilling existing teams through training programs in modern mainframe testing tools — for example, IBM Rational and CA Test Automation — ensures continued support. Outsourcing mainframe testing to specialized vendors provides immediate access to expert resources while reducing hiring challenges.

4. Integration with modern architectures

Mainframes must now communicate with APIs, microservices, and cloud applications, creating complex dependencies. Testing these integrations is difficult because mainframes often rely on different programming languages, protocols, and transaction processing systems that are not inherently compatible with modern platforms.

How to overcome: Implementing API testing and service virtualization helps validate integrations without requiring full system access. Continuous testing in DevOps pipelines ensures real-time validation of interactions between mainframes and external applications, minimizing risks.

5. Compliance and security risks

Mainframes store vast amounts of sensitive data, making them prime targets for cyberattacks. Additionally, industries such as banking, healthcare, and government must adhere to strict regulatory requirements like GDPR, PCI DSS, and HIPAA. Non-compliance can lead to severe penalties and reputational damage.

How to overcome: Automating security and compliance testing ensures early detection of vulnerabilities and regulatory issues. Regular penetration testing, role-based access control, and real-time security monitoring help protect mainframe data and ensure adherence to industry standards.

6. High testing costs

Mainframe testing requires specialized tools, dedicated environments, and skilled professionals, all of which contribute to high costs. Maintaining legacy systems while investing in modern testing technologies can strain IT budgets.

How to overcome: Cloud-based testing platforms reduce infrastructure costs while improving scalability. Open-source and hybrid testing solutions help minimize licensing expenses. A risk-based testing approach ensures that the most critical components receive priority, optimizing test coverage without unnecessary spending.

7. Difficulty in implementing Agile and DevOps

Mainframe development traditionally follows the waterfall methodology, making it difficult to align with Agile and DevOps practices. The slow pace of mainframe change management can create conflicts with the need for rapid iteration in modern software development.

How to overcome: Integrating automated testing into CI/CD pipelines enables faster feedback loops. Encouraging collaboration between mainframe teams and DevOps groups helps break down the blockages. Implementing test automation frameworks designed for mainframes ensures that testing keeps pace with modern development cycles.

Best Practices for Mainframe Software Testing

Mainframe applications are critical to business operations, requiring a structured and reliable testing approach for a smooth, uninterrupted run. Unlike modern cloud-based systems, mainframes often run complex, high-volume transactions with strict uptime requirements. This means that in order to ensure stability, security, and performance, businesses must adopt best practices tailored to the unique challenges of mainframe environments. These are the best practices to consider when testing mainframes.

1. Define clear test objectives and coverage criteria

Establishing precise goals for functional, performance, and security testing ensures that all critical mainframe components are validated. Creating detailed test coverage maps helps teams focus on high-impact areas, reducing unnecessary testing efforts.

2. Maintain a centralized test data management strategy

Mainframes handle vast amounts of structured data, making test data integrity essential. Using data masking and synthetic test data generation ensures compliance with privacy regulations while enabling realistic test scenarios.

3. Implement robust change management processes

Given the stability requirements of mainframe applications, even minor updates can introduce risks. A well-defined change management process, including impact analysis and controlled rollouts, prevents disruptions and ensures smooth software releases.

4. Adopt environment-independent testing approaches

Separating test cases from specific environments improves flexibility. Using emulators, cloud-based testing environments, and virtualization tools allows testing to continue even when mainframe access is restricted.

5. Validate batch processing and job scheduling

Since many mainframe applications rely on batch jobs, testing should include validation of job execution, error handling, and scheduling dependencies. Ensuring that jobs execute in the correct sequence prevents operational failures.

6. Ensure backward compatibility with legacy applications

Many enterprises still run decades-old mainframe applications alongside modern systems. Testing should confirm that new updates do not disrupt legacy workflows, ensuring seamless interoperability across different versions and platforms.

7. Establish real-time monitoring and analytics for test execution

Integrating real-time dashboards and analytics into mainframe testing provides instant insights into test performance, defect trends, and system stability. Proactive monitoring helps identify anomalies before they impact production.

Mainframe Testing in 2025: Key Trends and Technologies

Testing software for the mainframe has been around for decades, but it has been constantly transforming and improving to accommodate the increasingly crucial role of mainframe computing in today’s digital ecosystems and the latest developments in the world of tech. Here are the trends and technologies used today for ensuring the software system is secure, reliable, and continues to meet the company’s needs.

The role of AI and automation

AI-driven test automation speeds up mainframe testing, reducing manual effort and errors. Machine learning improves defect detection and test coverage. Automated scripts ensure stability after updates. For business leaders, automation means cost savings, faster releases, and improved software quality. Investing in AI-powered tools helps modernize legacy systems while maintaining reliability and adaptability.

Continuous testing for mainframes

Continuous testing ensures early defect detection, reducing costly failures in production. It integrates with CI/CD pipelines, improving agility and reliability. By testing mainframe applications frequently, businesses prevent service disruptions and enhance security. Leaders should prioritize shift-left testing to meet compliance requirements and maintain operational efficiency.

Cloud-based mainframe testing

Cloud-based testing reduces infrastructure costs while enabling scalable and flexible test environments. Virtualized mainframe testing accelerates execution, improves collaboration, and supports disaster recovery. Business leaders should explore cloud solutions to enhance efficiency while ensuring robust quality assurance for critical mainframe applications.

Service virtualization for mainframe testing

Service virtualization mimics mainframe components, enabling parallel testing without full system access. It reduces costs, accelerates development, and ensures comprehensive test coverage. Leaders should consider this approach to improve efficiency, especially for complex transactions and third-party integrations.

Shift to risk-based testing

Risk-based testing prioritizes business-critical functions like transaction processing and security. Instead of exhaustive testing, it optimizes resources to focus on high-impact areas. Testers should adopt this strategy to balance quality assurance, cost-effectiveness, and operational stability.

Compliance-driven testing

Automated compliance testing ensures mainframes meet evolving regulations without manual effort. It reduces legal risks, prevents penalties, and strengthens security. Organizations should invest in compliance-driven testing to maintain regulatory adherence and long-term business continuity.

Bridging the skills gap

With mainframe experts retiring, businesses face a skills shortage. Companies running mainframes must invest in upskilling, hiring, or outsourcing. Training preserves institutional knowledge, while outsourcing provides access to specialized expertise. A hybrid approach balances internal capability with external support, ensuring long-term mainframe reliability.

Final Thoughts

Mainframe computers still play a vital role for large companies in every domain imaginable. So, it is — and will continue to be — essential to test the applications hosted in the mainframe before the launch. Both batch job and online testing should be performed rigorously and with particular attention to detail not to miss any functionality elements stated in the requirement documents, and no test case should go unnoticed. Only then can you be fully confident in your solution’s quality and anticipate the best possible outcomes.

The Guide to Mobile Game Testing: Types, Techniques, Challenges, and More

TestFort — Mon, 15 Sep 2025 14:41:36 +0000

The mobile gaming industry moves fast, but players move on even faster. One crash, one lag spike, or one broken reward can mean thousands of lost users. That’s why testing isn’t just a mobile game development step — it’s a make-or-break part of your product’s success.

In this guide, we’ll walk you through what makes mobile game QA different, what it takes to do it well, and how the right testing strategy can turn good gameplay into a lasting player experience.

Key Takeaways

Mobile game testing evaluates gameplay, performance, compatibility, monetization, and backend services to ensure a polished user experience.
QA prevents negative reviews, ensures smooth play across devices, validates in-app purchases, and maintains app store compliance.
Different game genres require tailored testing focusing on specific features, performance needs, and player interactions.
Common testing types include functional, performance, load, compatibility, usability, localization, security, compliance, and regression testing.
Manual testing handles real-time gameplay and UX, while automation covers repetitive tasks and regression efficiently.
Android game testing companies address device fragmentation, while iOS game testing services focus on OS updates and App Store rules.
Testing on real devices is vital, as emulators can’t fully replicate hardware-specific issues.
QA tools support automation, performance monitoring, network simulation, and beta testing throughout development.
Key challenges include real-time interaction, multiplayer networking, frequent updates, monetization accuracy, localization, and anti-cheat security.
A strong QA strategy involves early, continuous testing, clear goals, device coverage planning, pipeline integration, and balanced test methods.
The right partner offers game-specific expertise, broad device access, balanced testing, live operations support, and clear communication.

What Is Mobile Game Testing and Why Does It Matter?

Mobile game testing is the process of evaluating a game’s functionality, performance, compatibility, and overall quality across different mobile devices and operating systems. The goal is to identify and resolve bugs, inconsistencies, and technical issues before the game reaches users.

Unlike general mobile app testing, testing of mobile games requires a focus on real-time responsiveness, immersive user experiences, and complex gameplay mechanics. Testers must account for varying user behaviors, device capabilities, and environments, such as unstable networks or limited hardware resources.

Mobile game testing typically involves a combination of manual and automated methods to cover areas like core gameplay, controls, graphics, sounds, monetization features, and backend services such as multiplayer logic or cloud saves.

It’s a highly iterative process that begins early in game development and continues through launch and post-release updates, ensuring that the game meets both technical requirements and player expectations.

Why it’s important to test mobile games

With thousands of excellent games to choose from in every category imaginable, users are not going to settle for anything less than a flawless game application that delivers a consistent experience in any scenario. The mobile game testing process is designed to ensure the game is ready to meet and wow its players. Here is why else mobile game testing is crucial:

Prevent negative reviews and uninstallations. Players have little patience for buggy or broken games. Even a minor glitch can result in poor ratings, reduced visibility in app stores, and a significant drop in user retention.

Ensure smooth gameplay across devices. With thousands of device models in circulation, especially on Android, it’s essential to test how the game performs on different screen sizes, OS versions, and hardware configurations to avoid game-breaking issues.

Identify functional bugs and logic errors. Core mechanics, game rules, UI elements, and player progression must work exactly as designed. Missed bugs in these areas can ruin the experience or block progression entirely.

Catch performance issues early. Mobile games must run efficiently without lags, frame rate drops, excessive battery use, or overheating. Performance issues are one of the top reasons players quit and uninstall games.

Validate in-app purchases and ads. Errors in payment flows, reward delivery, or ad display can directly impact revenue and damage user trust. Every monetization path needs thorough testing across scenarios.

Maintain a consistent user experience in all environments. Whether users are playing offline, on a slow 3G connection, or switching between networks, the game must behave predictably and recover gracefully from connectivity issues.

Comply with app store requirements. Both Google Play and the App Store enforce strict guidelines. Poor testing can lead to rejection or delays in launch, especially if crashes or content violations are detected.

Support updates and live operations. Mobile games often evolve through seasonal content, patches, and limited-time events. QA ensures that new changes don’t break existing features or introduce new bugs.

Detect and prevent security vulnerabilities. Multiplayer games are especially vulnerable to cheating, exploits, or data breaches. Security testing protects your players and your reputation.

Build trust and grow a loyal player base. A polished, bug-free experience reflects positively on the game studio and increases the chance that players will stay, spend, and recommend the game to others.

Types of Mobile Games and How They Affect Testing

Not all mobile games are created equal, and neither are their testing needs. The game’s genre, complexity, game mechanics, target audience, and monetization model all influence the testing strategy, testing frameworks and tools, as well as the selection of different types of testing. Below are the most common types of mobile games and what they mean for QA.

Casual games

These games are often puzzle-based or arcade-style with moderate complexity. Testers need to cover multiple levels, progression mechanics, in-app purchases, and social sharing features. Cross-device compatibility is especially important here.

Hyper-casual games

These are lightweight, minimalist games with simple mechanics and quick gameplay loops. Testing focuses on UI responsiveness, ad integration, and performance across low-end devices. Since updates are frequent, fast regression testing is crucial.

Mid-core games

These games offer more depth, often involving character progression, resource management, or turn-based strategy. QA teams must validate in-game economies, balance, multiplayer features, and longer user sessions. Test automation and test data management often play a bigger role.

Hardcore/AAA mobile games

These titles mimic console or PC-like experiences with high-end graphics and real-time multiplayer. Testing is intensive and must include device performance profiling, stress testing, network fluctuation handling, and security validation to prevent cheating or exploits.

Multiplayer and live games

Any game with PvP, co-op, or matchmaking introduces a new layer of complexity. Testing must simulate real-world conditions (such as lag, disconnects, sync issues), validate server logic, and ensure fairness between players.

Educational or gamified apps

These games combine learning with interaction and often involve adaptive content. Testing focuses on logic accuracy, personalization features, and accessibility.

The Most Common Types of Mobile Game Testing

Mobile games are complex, interactive systems that require thorough testing from multiple angles. Below are the most common types of testing used to ensure quality, stability, and a seamless player experience:

Functional testing. This activity is used to verify that the game works as intended. This includes core gameplay mechanics, user interface, navigation, controls, menus, level transitions, scoring systems, and win/loss conditions.
Performance testing. A common mobile game testing type that evaluates the run of the mobile game in real-world conditions, both normal and stressful. Mobile game performance testing measures frame rate stability, memory usage, battery consumption, CPU/GPU load, and app responsiveness across different devices.
Load testing. Mobile game load testing simulates high user volumes and concurrent gameplay sessions to check how the game — and especially backend services like matchmaking or leaderboards — handles stress. This is critical for multiplayer games and launch readiness.
Compatibility testing. This type of testing ensures the game runs smoothly on a wide range of mobile devices, screen sizes, resolutions, chipsets, and operating system versions. Given the fragmentation of Android in particular, this testing type is essential.
Usability testing. UX and usability testing assess how intuitive and enjoyable the user experience is. This includes evaluating first-time user flows, menu clarity, control responsiveness, and accessibility for different types of players.
Localization testing. This is used to check translated content for accuracy, context, UI overflow, and formatting issues. Localization testing is essential for assessing cultural appropriateness and ensures the game behaves correctly in various regional settings like date formats and currencies.
Security testing. Security tests help identify vulnerabilities related to cheating, data leaks, tampering with in-app purchases, or unauthorized access to player accounts and game servers. That’s why security testing should never be skipped.
Compliance testing. This helps verify that the game adheres to app store policies, platform guidelines, and relevant regulations such as GDPR and COPPA. Failure to comply can lead to rejection or removal from app stores.
Regression testing. Teams perform regression testing to ensure that after code changes, updates, or bug fixes, previously working features still function correctly. This is crucial in games with frequent updates, events, or evolving content.

Mobile Game Testing Techniques: Manual Testing vs. Automated Testing

Effective mobile game QA relies on a smart mix of manual testing and automation. Each approach has its strengths, and choosing the right one depends on the game’s design, platform, and development pace.

Manual testing

Manual testing is essential for validating complex, real-time interactions, visual quality, and overall user experience. Human testers can assess gameplay flow, intuitive controls, responsiveness, and edge-case behaviors that automated scripts may miss. It’s especially valuable for:

Exploratory testing
New feature verification
UI/UX evaluation
Ad placement and IAP behavior
Multiplayer and network disruption scenarios

Manual testing also plays a key role in localization, accessibility checks, and player feedback validation during beta testing.

Automation testing

Mobile game automation testing helps speed up repetitive tasks and improve coverage across devices. It’s most effective when applied to stable, predictable areas of the game, such as:

Main menu navigation
Login and registration flows
Settings and non-interactive screens
Regression testing of known functionality
Basic gameplay sequences in casual or turn-based games

Frameworks like Appium, Unity Test Framework, or AltUnity Tester allow teams to script tests that run across multiple devices and OS versions, reducing human error and effort in large-scale projects.

Finding the right balance

Because mobile games are highly interactive, automation cannot fully replace manual testing. Instead, it should complement it, freeing up testers to focus on high-value areas while automation handles the routine. The right blend ensures broader coverage, faster release cycles, and a more consistent gaming experience across devices and updates.

Performing Mobile Game Testing Across Different Platforms

Testing mobile games across platforms isn’t just about checking that things “work” on both Android and iOS — it’s about understanding how each platform behaves, how users interact with it, and how the underlying ecosystems affect quality.

Android vs. iOS: different platforms, different priorities

Android game testing is especially demanding due to the vast fragmentation in the Android device market. With thousands of models running different OS versions, screen sizes, and chipsets, compatibility and performance issues are common. An experienced Android game testing company will select a representative mix of devices based on your game’s target audience and regions to ensure maximum coverage.

Android game testing services also focus on issues like UI scaling, background process interference, and manufacturer-specific behaviors, which may not appear in emulators. Games that perform well on one Android device might break on another, making real-device testing essential.

In contrast, iOS game testing services deal with a more standardized environment, but the stakes are just as high. Apple’s frequent OS updates, limited backward compatibility, and strict App Store policies mean that even minor bugs can delay your launch or result in rejection. Device-specific performance testing, especially between newer and older iPhones, is also critical.

Real devices vs. emulators

While emulators and simulators are helpful for early-stage testing and automation, they can’t fully replicate how games behave on actual hardware. They miss key factors like input responsiveness, network fluctuations, GPU limitations, or battery usage.

For both Android and iOS games, real-device testing is vital for accurate performance profiling, gameplay validation, and final release QA. Many Android game testing companies and QA providers use cloud-based labs (like Firebase Test Lab or BrowserStack) to access a wide range of physical devices without maintaining an in-house collection.

Consistency builds player trust

Whether on Android or iOS, even small inconsistencies in UI, controls, or ad behavior can hurt player satisfaction. Cross-platform testing ensures your game delivers the same level of polish, stability, and engagement, no matter where it’s played.

Mobile Game Testing Tools to Consider

Choosing the right tools can significantly improve the efficiency and coverage of testing, allowing mobile game testers to cover more ground in less time and ensure more consistent results. Below are some widely used platforms and frameworks that support different aspects of the mobile game QA process.

Functional and automated game testing

Tools for verifying game logic, UI behavior, and user interactions, automatically or manually:

Appium. Open-source tool for automating mobile UI testing; useful for navigation flows and simple game interactions.
Unity Test Framework. Built into Unity, it supports unit and integration testing of game logic directly within the engine.
AltUnity Tester. Designed for Unity games, it allows UI automation by interacting with game objects rather than screen coordinates.
Airtest + Poco. A visual automation tool from NetEase, combining image recognition with object-based testing; suited for 2D/3D mobile games.
Detox. A grey-box testing framework for mobile apps, useful for games built with React Native.

Performance and stability monitoring

Tools that help track in-game performance metrics, detect bottlenecks, and prevent crashes:

GameBench. Game-focused performance analysis platform for real devices; tracks FPS, CPU/GPU usage, memory, and power consumption.
Firebase Performance Monitoring. Tracks latency, rendering times, and network usage; integrates well with Firebase-based games.
Crashlytics. Real-time crash reporting tool with detailed diagnostics, stack traces, and impact analysis.
Backtrace. Crash and exception management solution built for games, with support for native and cross-platform engines.

Network and backend testing

Important for testing multiplayer features, server sync, and in-game communication:

Charles Proxy. Inspects HTTP/HTTPS traffic, useful for analyzing in-game API calls, purchases, and server interactions.
Fiddler. Similar to Charles Proxy; helps test backend connectivity, response validation, and simulate throttled networks.
Wireshark. For more advanced users, this network protocol analyzer provides low-level packet inspection in real-time sessions.

Device compatibility & cloud testing

Test on real devices at scale across different OS versions, screen sizes, and hardware configurations:

Firebase Test Lab. Cloud-based device testing across many Android devices; integrates with CI pipelines.
BrowserStack App Live. Provides remote access to a wide range of real iOS and Android devices for manual and automated testing.
Sauce Labs. Similar to BrowserStack, offering cross-platform device testing with parallel test execution support.
Kobiton. Device lab management and testing platform for automated and manual mobile testing.

Beta testing & user feedback

Tools to distribute pre-release builds, gather feedback, and observe real user behavior:

TestFairy. Lets you share builds with testers, record sessions, and collect crash data and feedback.
HockeyApp (now part of App Center). Microsoft’s solution for beta distribution, crash reports, and user analytics.
Google Play Console/Apple TestFlight. Official platforms for distributing test builds, collecting feedback, and managing external testers.
The right toolset depends on your tech stack (Unity, Unreal, or custom engine), the game’s complexity, and the level of in-house QA automation. For most teams, a combination of visual testing tools, real-device performance tracking, and crash analytics delivers the best coverage.

Unique Elements of Mobile Gaming: How Do They Impact QA?

Mobile game testing goes far beyond standard app QA. Games are dynamic, interactive, and constantly evolving, which introduces a specific set of challenges that require a tailored approach. Here are the areas that make mobile game testing different from other software testing tasks.

Real-time gameplay and user behavior

Games are designed to be immersive and reactive, meaning even small delays, input lag, or glitches can break the player experience. Testers must assess not just whether something works, but how smoothly and intuitively it works under pressure. Timing, responsiveness, and feedback loops all play a critical role in gameplay satisfaction.

Multiplayer, connectivity, and backend logic

Multiplayer and live-service games rely heavily on stable, real-time communication with servers. QA teams must test scenarios involving latency, packet loss, server delays, matchmaking fairness, synchronization issues, and player disconnections. This requires simulating various network conditions and edge cases, especially in PvP or co-op environments.

Live updates and event testing

Unlike most apps, games often run on seasonal content, time-limited events, or live balance changes. QA must validate frequent updates quickly while ensuring older features still work. This makes testing for regressions and rapid test cycles essential to avoid disrupting the user experience during live ops.

In-app purchases and ads

Monetization is baked into many mobile games via ads, reward systems, and in-app purchases. Testers must verify payment flows, ad placements, reward delivery, and error handling, in case of issues like failed transactions or missing items. Ad-related testing also includes integration with third-party ad networks and ensuring compliance with app store policies.

Localization and accessibility

Games are released globally, and any translation issues or cultural mismatches can break immersion or even offend players. Localization testing ensures that translated content fits in the UI, reads naturally, and behaves correctly. Accessibility is also becoming more important, especially for casual and educational games, and must be validated across devices and input methods.

How to Develop a Successful Strategy for Mobile Game QA

A solid QA strategy is the backbone of any successful mobile game release. Without a clear plan, mobile game QA testing becomes reactive, fragmented, and prone to costly oversights. Here’s how to build a QA process that supports quality from day one.

Align QA with development stages
Start testing early — ideally during prototyping or alpha builds. This helps catch fundamental design flaws, logic gaps, or performance issues before they grow into major blockers. Adjust the depth and scope of testing as the game evolves toward beta and release.
Define what success looks like
Set clear quality goals: performance benchmarks, crash tolerance levels, supported device list, multiplayer stability, ad performance, and compliance requirements. These criteria will guide your test coverage and priorities.
Choose the right mix of testing types
Incorporate functional, performance, compatibility, usability, and regression testing as core layers. Add security, localization, and compliance testing where relevant. A successful mobile game QA strategy is never one-size-fits-all — it’s tailored to the game’s genre, complexity, and audience.
Balance manual and automated testing
Use automation for repeatable flows and regression, but rely on manual testing for gameplay, visuals, and user experience. This hybrid approach ensures speed without compromising quality.
Plan for real-device coverage
Test across a carefully selected range of devices that reflect your user base. Don’t rely on emulators alone — real-device testing is essential to catch hardware-specific issues.
Integrate QA into your release pipeline
QA should be embedded in your development process, not an afterthought. Use continuous testing to support frequent builds, updates, and live events without sacrificing stability.

Key Challenges in Mobile Game Testing

Testing mobile games comes with unique hurdles that often go beyond typical app QA. These challenges impact time-to-market, user satisfaction, and long-term revenue if not addressed properly. These are the challenges testers are most likely to encounter when testing a mobile game app.

Device and OS fragmentation

The thousands of Android devices, varied screen sizes, chipsets, and OS versions make consistent performance difficult to guarantee.

How to overcome: Build a prioritized device matrix and test on real hardware using cloud device labs or an in-house collection.

Complex, real-time interactions

Timing, animation, touch sensitivity, and gameplay flow must feel seamless — something automation alone can’t fully evaluate.

How to overcome: Combine exploratory manual testing with well-defined test cases focused on gameplay feel and responsiveness.

Multiplayer and network variability

Real-time syncing, disconnections, lag, and cross-platform issues are hard to simulate and easy to miss.

How to overcome: Use network simulation tools to test under poor conditions, and validate multiplayer logic with structured test scenarios and edge cases.

Frequent updates and live events

Regular patches, balance changes, and new content can introduce regressions and break stable features.

How to overcome: Perform regression testing to ensure stability and integrate continuous QA into the update pipeline.

Monetization flow errors

Broken in-app purchases or ad integrations result in revenue loss and angry players.

How to overcome: Test all payment paths and ad scenarios (success, failure, interrupted flows) across multiple devices and network conditions.

Localization and cultural fit

Poor translations, UI text overflow, or culturally inappropriate content can disrupt the user experience.

How to overcome: Combine linguistic review with localization testing in real environments and devices set to target regions.

Anti-cheat and security gaps

Exploits, cheats, and backend manipulation can undermine fair play and damage your brand.

How to overcome: Include security testing, anti-cheat validation, and backend vulnerability checks in your QA process, especially for multiplayer games.

Working With a Mobile Game Testing Company: What to Look For

Partnering with a mobile game testing company can significantly reduce risks, speed up time-to-market, and improve user retention — but only if you choose the right one. Not all QA providers are equipped for the demands of mobile games, which involve real-time responsiveness, device fragmentation, in-app purchases, and frequent content updates.

What to look for in a QA partner

Expertise in mobile game testing services. Look for a provider that understands not just mobile platforms, but the specific dynamics of gameplay testing: input timing, device overheating, ad behavior, and user progression.
Access to real devices. Emulators have limits. A serious QA team should test on a wide range of physical Android and iOS devices to catch edge cases early.
Balanced test strategy. The best companies combine manual and automated testing to cover both gameplay experience and repetitive flows like login, menus, or IAP.
Support for live operations. Games don’t stop after launch. Your partner should be able to handle regression testing, seasonal updates, and performance optimization as part of ongoing mobile game testing services.
Transparent communication. A responsive, detail-oriented team will provide clear reporting, frequent check-ins, and actionable QA insights, not just bug lists.

Why choose us for mobile game testing services

TestFort is more than a team of bug-hunters — we’re a QA partner that understands how games should feel, function, and perform in the hands of real players. Our mobile game testing services are designed to help studios release confidently, scale quickly, and keep players engaged long after launch. From first-time user flows to multiplayer sync, we test where it matters — on 250+ real devices, across iOS and Android, using a balanced mix of manual and automated methods. As a result, you get better stability, smoother gameplay, fewer production delays, and happier users.

Over the years, we’ve worked on mobile games with social, multiplayer, and real-time interaction features. Below are two examples that show how our testing services directly supported product success.

Social gaming app for iOS with gamified features

For a startup creating a social app with game-like elements, we delivered end-to-end QA alongside development. Our team conducted usability, integration, stress, and automated regression testing through a CI pipeline, ensuring each release met user experience expectations. Our involvement helped reduce QA turnaround time by 40%, allowing the client to launch two weeks ahead of schedule without sacrificing stability.

Real-time multiplayer battle game for iOS

In a fast-paced 2D multiplayer game, we focused on ensuring real-time stability, social feature reliability, and cross-device performance. Automated regression testing and load simulation helped catch early-stage sync issues and ensured smooth gameplay across iPhones. After launch, the app saw an average session length increase of 28% in the first month and achieved a 4.8 rating on the App Store — up from 4.2 during beta testing.

Final Thoughts

In mobile gaming, quality isn’t just a nice-to-have — it’s one of the strongest competitive advantages there can be. A well-tested game builds trust, keeps players engaged, and supports long-term growth in a crowded market. Whether you’re launching a new title or scaling an existing one, reliable QA ensures that every update, feature, and interaction delivers the experience your players expect.

With the right testing partner, you don’t just catch bugs — you accelerate development, protect your reputation, and raise the bar for player satisfaction. And we are here to help you do exactly that.

Accessibility Testing Guide: How to Make Content Accessible in 2025

TestFort — Mon, 15 Sep 2025 14:16:39 +0000

Accessibility may not have been the central principle of developing digital solutions a couple of decades ago, but now, with the public’s growing awareness of the importance of inclusion, businesses can no longer afford to ignore accessibility when establishing their online presence. Right now, organizations can face lawsuits and fines if they fail to comply with relevant accessibility standards. However, accessibility is not just about avoiding legal troubles. It’s about ensuring that every user, regardless of ability, can engage with your services without barriers and that no user is left underserved due to a disability.

Accessibility testing is the only way to ensure that your digital product is inclusive and compliant with the increasingly stringent requirements. In this article, we will discuss the importance of providing accessible services, key accessibility testing methods and best practices, and current accessibility standards in the EU, UK, and US.

Key Takeaways

Accessibility testing is essential not only to comply with legal requirements but also to ensure digital products are inclusive and usable by people with diverse disabilities, enhancing overall user experience.
Key accessibility regulations and laws in 2025 include the EU’s European Accessibility Act, the UK’s Equality Act and Public Sector Accessibility Regulations, and the US Americans with Disabilities Act and Section 508.
Web Content Accessibility Guidelines (WCAG) remain the global standard for accessibility, emphasizing four principles: perceivable, operable, understandable, and robust (POUR).
Accessibility testing involves multiple approaches, such as manual testing, assistive technology testing, user testing with people with disabilities, expert audits, and hybrid testing combining these methods for comprehensive coverage.
Automated accessibility testing tools can detect common issues and support continuous integration but cannot replace manual checks for context, dynamic content, and real-world usability.
Building a culture of accessibility requires leadership commitment, early integration into product design, involvement of users with disabilities, cross-functional collaboration, accountability, and ongoing communication.
Outsourcing accessibility testing can be a strategic choice for organizations lacking in-house expertise or resources, enabling faster compliance and objective evaluation by specialists.

What Is Web Accessibility and Who Does It Serve?

Accessibility testing is the process of evaluating digital products, such as websites, apps, and software platforms, to ensure they can be used by people with disabilities. This includes checking whether users with visual, auditory, motor, or cognitive impairments can perceive, navigate, and interact with your product effectively.

The goal is to identify and fix barriers that prevent equal access, whether that means missing alt text, poor color contrast, keyboard traps, or incompatibility with assistive technologies like screen readers.

Accessibility testing directly benefits users who rely on assistive technologies or specific design patterns to interact with digital content. This includes:

Blind and low-vision users
Deaf or hard-of-hearing users
People with mobility limitations
Users with neurodivergent conditions or cognitive challenges

On top of that, good accessibility improves usability for all. Features like captions, clear navigation, mobile responsiveness, and voice commands help users in many contexts, whether they’re on a slow internet connection, using a small screen, or temporarily injured.

The Importance of Accessibility Testing in 2025

The history of accessibility, including accessibility of tech products, dates back to the 1970s, but the role of accessibility checks has only grown more critical with time. Today, accessibility is considered to be one of the pillars of developing digital products, and that critical role stems from the increased attention to better inclusion for different audiences. Here is why accessibility testing should be on your agenda if you’re developing software products.

Tighter regulations and higher legal risks

In 2025, accessibility is no longer a “nice to have” feature — it’s a legal requirement for most digital products in many regions. The EU’s European Accessibility Act is coming into full effect by mid-2025, expanding obligations for web and mobile accessibility across key sectors, including eCommerce, banking, transportation, and media. The UK continues to enforce the Public Sector Bodies (Websites and Mobile Applications) Accessibility Regulations, and businesses face increased scrutiny under the Equality Act 2010. Globally, similar mandates are growing in scope and enforcement. Failing to comply now carries a higher risk of fines, reputational damage, and litigation.

Rising expectations from users and partners

Users in 2025 expect digital products to be inclusive and comply with accessibility standards by default. Poor accessibility isn’t just a technical flaw — it’s viewed as a signal that a brand isn’t attentive to user needs. Enterprises and governments increasingly require accessibility compliance in vendor contracts, so B2B companies that overlook testing may lose out on valuable partnerships. Accessibility has become part of procurement checklists, due diligence reviews, and vendor risk assessments.

Mature tools, maturing practices

Unlike in the past, accessibility testing is no longer hard to justify or difficult to implement. Tooling has matured significantly, with more accurate automated checks, stronger assistive tech emulation, and better integration into development workflows. Teams have access to detailed WCAG guidance, training resources, and community support. In 2025, not testing for accessibility is more often a sign of incorrect priorities than a lack of capability.

A shift toward proactive compliance

The market is moving from reactive accessibility fixes to proactive, ongoing strategies. This shift is driven by evolving customer expectations and stronger compliance frameworks. In practice, this means accessibility is now being considered as early as UX wireframes and built into component libraries, with testing embedded into CI/CD pipelines. Organizations that fail to make this shift risk falling behind in terms of both legal compliance and user trust.

Benefits of Accessibility Testing: Why Perform Accessibility Tests

Accessibility problems can become a bigger issue for a business than many can realize. From legal troubles to loss of customer trust and partnership opportunities, the consequences of failing to address accessibility issues will be swift and often severe. Here are the key advantages of making accessibility testing your priority.

Legal and regulatory compliance

Accessibility testing helps businesses meet regional and international requirements such as the European Accessibility Act, the UK Equality Act, and WCAG standards. It significantly reduces the risk of fines, legal disputes, and public backlash related to digital discrimination or exclusion.

Expanded market reach

Over one billion people globally live with some form of disability. By ensuring accessibility, businesses can tap into this underserved demographic while also improving usability for elderly users, people with temporary impairments, and users in challenging environments like bright sunlight or noisy surroundings.

Improved user experience for all

Accessible design benefits everyone — not just users with disabilities. Clearer layouts, intuitive navigation, and better content structure make digital experiences smoother for all users, including those on mobile devices, with slow internet connections, or using voice commands.

Better SEO performance

Many accessibility practices — like using semantic HTML, descriptive link text, and image alt attributes — also align with SEO best practices. This means improved visibility in search engines, potentially leading to higher traffic and better engagement without additional marketing expenses.

Enhanced brand reputation

Demonstrating a commitment to inclusion builds trust and goodwill with customers, partners, and the public. Accessibility testing positions your business as responsible and forward-thinking, which can strengthen brand loyalty and attract values-aligned audiences.

Lower support and maintenance costs

Accessible interfaces are easier to understand and use, reducing user frustration and support requests. Clearer code and consistent UI elements also make maintenance easier for developers and help avoid costly redesigns or remediation down the line.

Future-proof digital assets

Investing in accessibility now ensures your product is ready for evolving legal standards, technology shifts, and customer expectations. It also makes future upgrades smoother, as accessible components are typically more modular, reusable, and stable.

Stronger partnership opportunities

Accessibility is increasingly among the most vital requirements in government and enterprise vendor selection. Testing and certifying your product’s accessibility can open the door to contracts and partnerships that would otherwise be out of reach.

Global Regulatory Landscape: USA, UK, EU

The United States, the United Kingdom, and the European Union are at the forefront of developing accessibility requirements, monitoring their implementation, and overseeing the fines and other consequences for violations of accessibility standards. This is what accessibility laws and regulations currently look like in these regions.

United States

In the US, the two primary laws regulating accessibility requirements include:

Americans with Disabilities Act. Title III of the ADA requires that public-facing websites and digital services be accessible to individuals with disabilities. Though not explicitly detailing digital requirements, it has been interpreted to apply to websites and apps.
Section 508 of the Rehabilitation Act. This law applies to federal agencies and contractors, requiring that all electronic and information technology is accessible to people with disabilities.

Under these laws, digital services must be perceivable, operable, understandable, and robust for users with disabilities. This includes government websites, banking services, retail, and more. Declining to comply with the regulations may trigger the following consequences:

Lawsuits filed by individuals or advocacy groups
Expensive settlements and remediation costs
Reputational damage and loss of contracts, particularly for federal vendors
Increasing DOJ enforcement actions

United Kingdom

In the UK, accessibility compliance is governed by:

Equality Act 2010. It requires reasonable adjustments to be made for people with disabilities. The Equality Act applies broadly to businesses and service providers, including digital products.
Public Sector Bodies (Websites and Mobile Applications) Accessibility Regulations 2018. This law requires all public sector websites and apps to meet accessibility standards and publish an accessibility statement.

Private companies must ensure equal access to digital services under the Equality Act. Public sector entities are held to even stricter standards and must comply with accessibility regulations and reporting obligations. Non-compliance with the standards can lead to:

Legal action under civil rights law
Investigations by the Equality and Human Rights Commission
Public scrutiny and reputational damage
Difficulty winning government contracts

European Union

In the EU, complying with standards of accessibility involves two primary laws:

European Accessibility Act (EAA). Coming into full force by June 2025, this act requires that a wide range of digital products and services meet accessibility standards. It applies to eCommerce, banking, transport, e-books, and more.
Web Accessibility Directive. In force since 2016, it applies to public sector bodies, mandating web and app accessibility compliance and monitoring by national authorities.

Private businesses and public services operating in the European Union must ensure digital accessibility across key domains. Affected companies must adopt compliant design and testing processes and publish accessibility statements. Failure to comply with the regulations can result in:

Fines and administrative penalties set by member states
Exclusion from public tenders or contracts
Mandatory corrective actions enforced by local regulators
Increased risk of user complaints and formal audits

Who Can Be Exempt from Accessibility Compliance?

Timely, in-depth accessibility evaluation is critical for all businesses — reputation damages, user backlash, and loss of competitive advantage are just a few reasons why you need to ensure that the website or app you’re releasing to the public offers full accessibility. However, legal consequences of non-compliance with accessibility guidelines have some exemptions. Here is who can be exempt from fines and legal ramifications of failing to comply with accessibility requirements.

European Union

Exemptions under the Web Accessibility Directive and European Accessibility Act:

Microenterprises (fewer than 10 employees and annual turnover or balance sheet under €2 million) may be exempt from the European Accessibility Act, though individual member states can apply stricter rules.
Disproportionate burden clause: Organizations may claim exemption if compliance imposes a disproportionate financial or administrative burden, but this must be documented and justified.
Archived content not needed for active services is also exempt from public sector obligations.

United Kingdom

Exemptions under the Public Sector Accessibility Regulations and Equality Act:

Non-government entities are not covered by public sector regulations, but are still subject to the Equality Act 2010, which does not include formal accessibility exemptions.
Disproportionate burden exemptions may apply to public sector bodies, but they must:

Conduct an assessment
Justify the claim publicly
Review it regularly

Private companies must still provide reasonable adjustments if requested by a disabled user, regardless of size.

United States

Exemptions under the ADA and Section 508:

Private businesses under the ADA rarely qualify for exemption. Title III applies to all public-facing businesses regardless of size.
Section 508 applies only to federal agencies and their contractors. Private companies not selling to the government are not legally bound, though compliance is often expected.

There is no specific exemption for small businesses under ADA, but enforcement often focuses on larger organizations or repeated violations.

Key Standards to Follow: WCAG, ARIA, and More

Being familiar with accessibility regulations and laws is integral for operating in the digital domain, particularly if you plan to expand your business to different global markets. However, it’s only possible to conduct accessibility testing effectively when you are familiar with universal standards and guidelines designed to ensure that the information and services online don’t prevent certain categories of users from accessing them. Here is a quick look at the most important global accessibility standards in place today.

Web Content Accessibility Guidelines

Web Content Accessibility Guidelines, or WCAG, is the most widely adopted standard for digital accessibility. Published by the W3C, it provides a set of testable success criteria based on four principles: content must be Perceivable, Operable, Understandable, and Robust. These principles are known together as POUR.

The current version is WCAG 2.2, with WCAG 3.0 in development. It defines three levels of conformance: A (basic), AA (recommended), and AAA (advanced). Level AA is the common legal requirement in the US, UK, and EU. Complying with WCAG helps ensure your digital products meet legal requirements and are accessible to people with diverse disabilities, including visual, auditory, motor, and cognitive impairments.

Accessible Rich Internet Applications

ARIA is a technical specification for making dynamic web content and user interface components more accessible. It’s especially relevant for modern, JavaScript-heavy websites and applications that use custom controls not natively accessible by browsers.

ARIA provides roles, states, and properties that developers can use to describe how UI elements behave. It helps assistive technologies interpret elements like modals, tabs, dropdowns, and sliders. When used correctly, ARIA makes advanced user interfaces accessible. When used incorrectly, it can make them less accessible, so it’s best implemented by experienced developers with accessibility expertise.

Types of Accessibility Testing to Consider

Unlike standard software testing, which can be broken down into several well-known testing types like performance testing, configuration testing, or functional testing, testing products for individuals with disabilities includes a different range of testing types. Here are two primary groups of testing activities typically used for checking accessibility compliance.

By stage of the development cycle

Digital accessibility takes place at different stages of the software development lifecycle, starting from the earliest phases and continuing throughout the software product’s entire existence, complete with updates and improvements. Here are the testing techniques used to identify accessibility issues at various stages of the SDLC.

Static code analysis

Static analysis is performed early in development by reviewing the source code before the UI is rendered. It helps spot issues like missing alt text or incorrect ARIA roles using linters or IDE plugins. Catching problems at this stage is cost-effective and prevents accessibility defects from progressing further.

Dynamic testing

Dynamic testing evaluates accessibility on a functioning interface. It checks how users interact via keyboards or screen readers and identifies issues like broken focus states or unclear labels. This method reveals real-world usability barriers that code-level tools can miss.

Regression accessibility testing

When updates are made, regression testing ensures that past accessibility fixes still work. It involves rechecking known components through manual or automated tests, often as part of CI/CD. This helps maintain consistent compliance and prevents the reintroduction of old issues.

By focus area
Accessibility testing focuses on a variety of things, but the exact range of focus areas depends on the type of product and its architecture: for example, a solution consisting of a website and app will require a different testing strategy compared to a self-service kiosk. Here is what to focus on when testing accessibility for different digital solutions.

Web accessibility testing

This is the most common focus area, covering websites and web applications. Website accessibility testing ensures that users with disabilities can access and interact with content via screen readers, keyboard navigation, and other assistive technologies. Testing includes checking page structure, color contrast, form labels, and navigation flow.

Mobile accessibility testing

Mobile accessibility focuses on native apps and mobile-optimized websites. Mobile app accessibility testing, as well as testing mobile browser versions of websites, involves testing on different devices and platforms (iOS, Android) with tools like VoiceOver or TalkBack. Key areas include gesture support, screen reader compatibility, scalable text, and accessible touch targets.

PDF and document accessibility

Documents such as PDFs, Word files, and presentations also need to be accessible, especially in regulated industries. Testing checks for tagged content, reading order, alt text for images, and compatibility with screen readers. Accessibility in documents is often overlooked but critical for compliance.

Multimedia accessibility

This involves ensuring that audio and video content is accessible to all users. Common requirements include captions for videos, transcripts for audio, audio descriptions for visual content, and accessible media players. Multimedia testing is essential for training materials, marketing content, and webinars.

Hardware and interface accessibility

For businesses offering physical interfaces — like kiosks, ATMs, or POS systems — hardware accessibility is key. Testing focuses on tactile elements, screen readability, input device support, and overall user interaction. It ensures that users with mobility or vision impairments can operate the device independently.

Common Accessibility Testing Approaches

Accessibility testing involves a range of activities that often resemble traditional testing but require a different approach to be effective and have a positive impact on the product’s accessibility for different categories of users. These are the testing approaches used to ensure compliance with accessibility guidelines.

Manual testing

Manual testing is done by humans to catch issues that automation can’t detect — such as keyboard navigation flow, logical reading order, or whether image alt text is meaningful in context. It often includes using screen readers, navigating via keyboard only, and simulating different user needs.

Manual testing works best for: Real-world usability checks, compliance validation, and edge cases.

Assistive technology testing

This involves testing your product using the actual tools people with disabilities use — like screen readers (NVDA, JAWS, VoiceOver), screen magnifiers, or switch devices. It’s essential for evaluating compatibility and ensuring that your content is perceivable and operable.

Assistive technology testing works best for: Ensuring compatibility with user environments and tools.

User testing with people with disabilities

As one of the most insightful approaches, this one involves gathering direct feedback from users with various impairments as they interact with your product, not just professional accessibility testers. It reveals practical usability barriers and uncovers issues that neither tools nor QA teams might anticipate.

User testing works best for: Real-world validation, improving user experience, and inclusive design feedback.

Expert audits

Accessibility specialists perform structured reviews of your product against standards like WCAG or EN 301 549. These audits often include both automated and manual testing, detailed reporting, and remediation guidance.

Expert audits work best for: Formal compliance assessment, pre-launch reviews, and legal documentation.

Hybrid testing

Hybrid accessibility testing combines multiple approaches — typically automation, which we’ll talk about later in this article, manual reviews, and expert input — to maximize coverage and efficiency. Some teams also incorporate limited user testing or leverage QA engineers trained in accessibility as part of their routine test process. A combination of manual and automated testing plus additional techniques helps bridge the gaps in each method and use their joint strengths to identify potential accessibility issues faster and with higher precision.

Hybrid testing works best for: Organizations looking to balance accuracy, speed, and cost while maintaining consistent accessibility practices.

Automated Accessibility Testing: What Test Automation Can and Cannot Do

Automation testing is usually viewed as the holy grail of quality assurance for traditional web and mobile software products. Accessibility testing, however, is different: while automation does play an important role in compliance with accessibility standards, it is estimated that it can only uncover 30-40% of accessibility issues. This means that automation can be used to enhance the efficiency of manual testing efforts, but it cannot be the only testing activity on the project. Here is what you can and cannot do by automating testing, which tools to use, and where automation makes the most sense.

What you can do with automation

Detect common, rule-based accessibility issues. Automated tools excel at finding issues that violate standard accessibility rules. These include missing alt text on images, incorrect heading structure, inadequate color contrast, missing form labels, and misuse of ARIA attributes. These checks are fast, repeatable, and useful for establishing a baseline level of compliance across digital products.
Provide rapid feedback during development. Developers can run automated scans directly in their browsers or IDEs while coding, catching issues early before they become embedded in designs or codebases. This supports shift-left practices, saving time and reducing rework later in the development cycle.
Support large-scale testing and continuous integration. Automation is ideal for websites or apps with many pages, components, or frequent releases. It integrates well into CI/CD pipelines, enabling teams to automatically test for accessibility with every build or pull request. This ensures accessibility is continuously monitored and regressions are flagged immediately.

What you cannot do with automation

Understand user context and intent. Automated tools cannot interpret whether alt text is meaningful, whether a button label is descriptive, or if a link makes sense when taken out of context. These are areas where human testers are needed to ensure a logical and user-friendly experience for assistive technology users.
Evaluate interactivity and dynamic behavior. Tools can’t simulate how real users interact with content. They miss issues like incorrect tab order, inaccessible custom widgets, or modals that don’t receive focus. Dynamic content updates, such as live alerts or loading spinners, may also go untested if not explicitly coded for accessibility.
Verify usability with assistive technologies. While some tools attempt basic emulation, they can’t fully replicate how users interact with screen readers, voice navigation, or switch controls. Testing with real assistive tech — or with users who rely on it — is essential for catching gaps in compatibility and real-world usability.

Popular tools for accessibility testing automation

Unlike manual software accessibility testing, which primarily relies on the tester’s skills and knowledge of applicable regulations, automation requires specific testing tools to ensure the accessibility of any customer-testing solution. Here are the accessibility testing solutions and general-purpose testing tools most frequently used for automating accessibility checks:

axe DevTools by Deque is a popular browser extension that identifies accessibility violations directly in the developer tools panel. It’s easy to use and suitable for both developers and testers. Its core engine, axe-core, is open source and widely adopted in custom integrations.
Lighthouse, built into Chrome DevTools, offers accessibility scoring alongside performance and SEO metrics. It’s useful for quick overviews and auditing individual pages, especially during manual reviews.
WAVE is a visual browser extension that overlays accessibility issues directly on the page, making it ideal for understanding issues in design and layout from a non-technical perspective.
Pa11y is a command-line tool often used for automated testing in CI pipelines. It generates detailed reports and supports custom configurations for more advanced teams.
Tenon provides an API-based service that allows teams to test pages and components via HTTP requests, making it easy to plug into enterprise-level workflows and CMS platforms.
Accessibility Insights, developed by Microsoft, combines automated checks with guided manual test flows. It’s particularly effective for teams that want to bridge the gap between tool-driven scanning and human-led testing.

When to use automation

It is possible to complete an accessibility testing project using manual testing alone, but automation does have its uses and can significantly boost the efficiency of testing when applied correctly. Here is when automating your test process makes the most sense from the quality and business perspectives.

During early development stages. Automating accessibility checks early in the development process helps identify basic issues before they become deeply embedded in the code. This is particularly valuable in agile workflows, where rapid iteration is common and quick feedback is crucial.
For regression testing after updates. Automation ensures that previously fixed accessibility issues do not reappear during updates or new releases. Automated tests can be scheduled to run frequently or triggered by changes in code, allowing teams to quickly identify regressions in accessibility.
For large-scale or content-heavy platforms. Automated testing shines in environments with large websites or complex applications where checking every page manually would be time-consuming and impractical. Automation ensures that accessibility is maintained consistently across all pages and components, providing broad coverage.
In CI/CD workflows. Integrating automated accessibility testing into continuous integration and continuous delivery pipelines ensures that accessibility checks are performed with every code push. This guarantees ongoing accessibility compliance and provides immediate feedback to developers during the development process.
When resources are limited. In projects where there are constraints on time or budget, automation serves as an initial step to highlight the most critical accessibility issues. It helps prioritize areas that require deeper manual testing, especially when a full manual audit might not be feasible.

Building a Culture of Accessibility: How to Serve People With Disabilities Better

Legal compliance is just the baseline. To truly support users with disabilities — and build products that serve everyone better — organizations need to embed accessibility into their culture. This starts with leadership, filters through teams, and shows up in everyday decisions. Here are some accessibility best practices to implement in your organization starting today.

https://testfort.com/wp-content/uploads/2025/06/4-Accessibility-Testing-Guide-768x379.png

Start with leadership commitment

Executive buy-in is essential. Accessibility efforts are more successful when senior leaders treat them as part of the business strategy, not just a compliance checklist. Set clear expectations, allocate budget, and communicate its value company-wide.

Make accessibility part of product thinking

Accessibility shouldn’t be an afterthought. Product owners and managers can lead by prioritizing inclusive design early — during research, requirements gathering, and roadmap planning. This ensures features are built for all users from the start, reducing rework later.

Involve people with disabilities

It is crucial that accessibility testing involves human testers, but real user feedback is key as well. Work with users who have disabilities during user research and usability testing. Their insights go beyond technical standards and help you understand real-world challenges and opportunities to improve.

Establish cross-functional teams

Everyone has a role in accessibility. Designers, developers, marketers, and support teams all influence the user experience. Offer basic accessibility training to non-technical roles so they understand how their decisions affect usability.

Create accountability

Define clear responsibilities and success metrics. Include accessibility checks in project reviews, audits, and release criteria. Assign team champions to advocate for accessibility throughout the development lifecycle.

Talk about accessibility, both internally and externally
Celebrate progress and share your commitment publicly. Internally, communicate wins and challenges to keep momentum. Externally, highlight your accessibility efforts to build trust with customers, partners, and job candidates.

Outsourced Accessibility Testing Services: Are They Right for You?

Accessibility testing works only when it’s consistent, takes place across the entire development cycle, and is performed by experts with both deep knowledge of accessibility practices and rich hands-on experience in implementing them. For these reasons, establishing a continuous accessibility testing process using nothing but the company’s own resources can be challenging.

A popular solution for this problem is outsourcing at least some aspects of testing to offshore accessibility experts. Outsourcing accessibility testing particularly makes sense for organizations that lack in-house accessibility specialists, need to accelerate compliance, or want an objective evaluation of their product’s accessibility from people who weren’t involved in the development process. Here is where else outsourcing accessibility testing is a great option:

You’re launching a new product and want to avoid accessibility issues from day one
You’re updating an existing system and need a full accessibility audit
You lack internal expertise in accessibility standards and assistive tech
You’re preparing for a public tender or compliance check (especially in the EU or UK)
You’ve received an accessibility complaint or legal threat and need remediation fast
You want a baseline assessment to plan in-house improvements later

Having provided software testing services for over two decades, we at TestFort have been there for every stage of accessibility requirements becoming more and more integral for developing popular and compliant software solutions. We have everything it takes to evaluate the accessibility of your product, from in-depth knowledge of global and local accessibility standards to robust manual and automated testing expertise. Let us make your solution accessible to the widest group of users, so that your market reach knows no bounds.

Final Thoughts

The important thing to remember about accessibility testing is that it’s not a one-time fix designed to remediate accessibility issues. With more and more attention being paid to the special needs of different audiences, accessibility checks are going to be the mainstay of developing future-proof software solutions. Timely, all-encompassing accessibility testing helps you strengthen your brand, unlock new markets, and avoid legal troubles and reputation losses that usually come along with non-compliance. Whether you are prepared to handle the entire bulk of accessibility tests yourself, or you’re planning to outsource at least some of the testing tasks, ensuring all-around accessibility will only have positive outcomes for your business.

How to Test AI Applications and ML Software: Best Practices Guide

TestFort — Thu, 04 Sep 2025 16:06:40 +0000

Testing Artificial Intelligence systems should be based on a fundamentally different approach than old-school software testing.

Traditional software follows clear rules and produces predictable outputs. AI solutions learn from data and make probabilistic decisions.

The consequences of inadequate AI testing result in biased hiring recommendations, inaccurate healthcare information, or misclassified objects in safety-critical situations.

What makes AI testing particularly challenging is its complexity. Traditional software either works correctly or fails obviously. AI systems can appear to function well while hiding subtle problems that only emerge in specific situations or with certain data inputs.

The EU AI Act introduces clear requirements and significant penalties for non-compliant systems. Organizations need to implement robust testing frameworks not just for technical performance, but also for fairness, transparency, and privacy.

The cost of not properly testing AI systems — in terms of regulatory penalties, reputational damage, and potential harm — far outweighs the investment in proper testing procedures.

This article is all about them.

Key takeaways

AI fails differently. Traditional software crashes. AI gives wrong answers that look right.

Data testing comes first. Bad data guarantees bad models. Quality checks prevent 30-50% of AI failures.

Three-layer testing approach. Test the foundation, the model itself, and real business impact.

Non-deterministic challenges. The same inputs can yield different outputs. Use statistical testing instead of exact matches.

Ethical testing isn’t optional. EU AI Act penalties are severe. Bias testing is now a legal requirement.

Specialized metrics matter. Use AI-specific metrics: AUC-ROC, precision/recall, RMSE, BLEU, perplexity.

Generative AI needs unique approaches. LLMs require specialized testing for hallucinations and prompt sensitivity.

Continuous monitoring is essential. Models degrade as real-world data shifts. Monitor constantly.

Documentation as defense. Document limitations and test results to protect against compliance issues.

Cost-benefit reality. Thorough testing costs more upfront but delivers 4-5x ROI through reduced failures.

Why Test AI Applications at All?

Unlike traditional software, AI and ML systems aren’t programmed explicitly — instead, they learn from data. This makes them powerful but introduces peculiar risks and uncertainties.

Accuracy and reliability. Even small errors in AI predictions can significantly affect business operations and user trust. Continuous testing of AI applications identifies inconsistencies and improves prediction reliability.

Risk of bias. AI models learn from data that often reflects existing biases. Testing helps your models to remain fair and compliant with ethical standards and regulations.

Security and privacy. AI-driven systems frequently handle sensitive data. Security testing reveals vulnerabilities and protects data integrity, confidentiality, and user privacy.

Regulatory compliance. Increasingly strict regulations around AI (e.g., EU AI Act, GDPR, HIPAA) require robust testing documentation. Failing compliance = heavy penalties and brand damage.

Robustness and stability. Users expect AI applications to perform consistently under real-world conditions. You need to make sure your model maintains stable performance despite unexpected inputs or scenarios.

If you don’t, you risk unreliable outputs, reinforce harmful biases, violate compliance standards, or expose sensitive information.

Current Challenges Associated with Testing AI Software

We will not talk much here about standard problems and tech issues every software has. You know those already. Let’s focus on challenges of testing machine learning models and Gen AI tools that are caused by their inherent complexity and learning-based nature.

Technical challenges

Non-deterministic outcomes. AI models can produce different results even with identical inputs. It complicates validation and verification. Unpredictability demands extensive testing and monitoring scenarios for consistent performance.

Complexity of training data and model behavior. Large datasets and sophisticated model architectures make finding the exact source of errors difficult. You need advanced testing solutions to analyze data quality, relevance, and coverage.

Versioning and reproducibility. AI models constantly evolve through retraining and updates. Managing model versions and reproducing past behaviors to validate improvements or identify regressions is technically demanding.

Adversarial vulnerability. AI products, especially deep learning ones, can be susceptible to adversarial attacks — inputs intentionally crafted to deceive models. Planned testing must consider methods that detect and defend against such vulnerabilities.

Resource intensity. AI and ML model testing often requires significant computational power and specialized infrastructure, making testing resource-intensive and potentially costly.

Operational challenges

In our experience, scale, complexity, and continuous evolution of machine learning workflows affect operational aspects of testing AI.

Integration into CI/CD pipelines. Traditional CI/CD processes often don’t effectively accomodate ML workflows. AI testing requires frequent model retraining, data updates, and performance validation, requiring specialized integrations.

Dataset management. AI model testing demands handling large, diverse datasets that must be continuously refreshed and validated. Efficient storage, access, and dataset versioning is critical but challenging to manage at scale.

Scalability and performance constraints. AI tests require vast computational resources and can quickly strain infrastructure.

Ethical and regulatory challenges in testing AI

Very soon, when speaking about how to test AI models, we will start not with performance or even security but with ethics and compliance of ML testing. The traditional software testing approach is no longer viable for planning QA for AI-based applications.

It’s fair. Regulators know that most of the companies have experienced QA teams to cover technical testing of AI systems and machine learning applications. But resilience of AI in terms of personal data vulnerability, bias risks and general applied ethics field requires both extra attention and extra regulations.

Bias detection and fairness

Bias isn’t theoretical — it has real-world implications. Consider Amazon’s recruitment AI, scrapped after it systematically disadvantaged female candidates due to historical hiring data biases. Bias audits and fairness testing methodologies, like IBM’s AI Fairness 360 toolkit, allow early detection and correction of biases.

Transparency and explainability

Healthcare AI recommending treatments without explaining the rationale already leaves doctors hesitant and confused, leading to slow adoption. Robust explainability testing, employing tools like SHAP, LIME, or Explainable Boosting Machines (EBM), ensures AI decisions are transparent, justified, and trustworthy.

Data privacy and protection

In 2021, an AI-driven banking app mistakenly exposed customer transaction details, resulting in a multi-million euro GDPR fine and damaged trust. Effective AI testing must enforce rigorous data anonymization practices and rely on secure testing environments.

Compliance with the EU AI Act

The EU AI Act introduces clear risk-based classifications (unacceptable, high, limited, minimal) with defined testing and documentation standards. Organizations should adopt comprehensive AI lifecycle documentation, maintain robust audit trails, and implement continuous compliance checks.

_Companies that neglect rigorous AI testing and transparent documentation face substantial financial penalties and possible product bans within EU markets. _

Dealing with ethical and regulatory challenges proactively mitigates risk and reinforces user trust, brand reliability. It also ensures your AI-driven solutions sustainably align with societal and regulatory expectations. “Testing for ethics” will be a new type of testing used for AI algorithms alongside compliance, security and usability testing.

Quick questionnaire for ethical AI testing
Use these simple questions to start evaluating your AI system’s ethical and regulatory readiness:

AI App Testing: Types, Tools, Differences

Testing AI applications requires a more comprehensive approach than traditional software testing. The unique characteristics of machine learning models — their probabilistic nature, reliance on data quality, and potential for unexpected behaviors — demand specialized testing methods. Here’s a breakdown of essential testing types for AI systems:

Data testing

AI performance directly depends on data quality. Poor or biased training data inevitably leads to flawed models, making data testing a critical first step.

Model validation testing

This testing validates that the model works as intended across various scenarios, not just on cherry-picked examples.

Security testing

AI systems introduce unique security concerns beyond traditional applications, including data poisoning, model stealing, and adversarial attacks.

Functional testing

Functional testing focuses on whether the AI system meets its specified requirements and performs its intended tasks correctly.

Load and performance testing

AI systems often have different performance characteristics than traditional software, with unique resource needs and potential bottlenecks.

*Bias and fairness testing *

Ethical considerations are crucial for AI systems to ensure they treat all users fairly and don’t perpetuate or amplify existing biases.

Generative AI-specific testing

Generative AI systems like chatbots and image generators require specialized testing approaches that evaluate the quality and appropriateness of outputs.

Automated Testing Frameworks for Generative AI

Unlike deterministic systems that produce consistent outputs for given inputs, generative AI creates novel content — text, images, code, audio — that can vary significantly even with identical prompts. This fundamental difference requires specialized approaches to testing generative AI applications.

Specific testing challenges of generative AI

Output variability. The same prompt can produce different outputs each time, making traditional exact-match assertions ineffective.

Hallucinations. Models can generate plausible but factually incorrect information that’s difficult to automatically detect without reference data.

Qualitative evaluation. Many aspects of generative output quality (creativity, coherence, relevance) are subjective and hard to quantify.

Prompt sensitivity. Minor changes in prompts can drastically alter outputs, requiring robust testing across prompt variations.

Regression detection. Model updates may fix certain issues while introducing others, making regression testing complex.

Key testing frameworks and tools

LangChain testing framework

Provides tools specifically designed for testing LLM applications.

from langchain.evaluation import StringEvaluator from langchain.smith import RunEvalConfig # Define evaluation criteria evaluation = StringEvaluator(criteria=”correctness”) # Configure test runs eval_config = RunEvalConfig( evaluators=[evaluation], custom_evaluators=[check_factual_accuracy] )

Strengths

Visual interface for test management
Supports multiple LLMs for comparison
Enables version control of prompts

Limitations

Limited support for non-text outputs
Mainly focused on prompt engineering

TruLens

TruLens focuses on evaluation and monitoring of LLM applications.

from trulens.core import TruSession from trulens.evaluators import Relevance session = TruSession() relevance = Relevance() with session.record(app, evaluators=[relevance]) as recording: response = app.generate(“Explain quantum computing”) # Get evaluation results results = recording.evaluate()

Strengths

Real-time monitoring capabilities
Multiple built-in evaluators (relevance, groundedness, etc.)
Works with major LLM frameworks

Limitations

Steeper learning curve
More focused on evaluation than comprehensive testing

MLflow with LLM Tracking

MLflow has expanded to support LLM testing.

import mlflow from mlflow.llm import log_predictions, evaluate_model # Log model predictions log_predictions( model_name=”my-llm”, inputs=test_prompts, outputs=model_responses ) # Evaluate model results = evaluate_model( model_name=”my-llm”, evaluators=[“factual_consistency”, “toxicity”] )

Strengths

Integrates with existing ML workflows
Comprehensive experiment tracking
Supports model versioning

Limitations

Requires additional setup for generative AI metrics
Lacks specialized generative AI testing features

Deepchecks

Deepchecks provides data validation and model evaluation.

from deepchecks.nlp import Suite from deepchecks.nlp.checks import TextDuplicates, OutOfVocabulary suite = Suite( “Generative Text Validation”, checks=[ TextDuplicates(), OutOfVocabulary() ] ) results = suite.run(train_dataset, test_dataset, model)

Strengths

Strong focus on data quality
Detects drift and outliers
Visual reporting

Limitations

Less focused on creative aspects of generation
Primarily designed for NLP models

Testing strategies for different generative AI outputs

Text Generation Testing

Assertion-based approaches

Content inclusion. Check that outputs contain key required information
Content exclusion. Verify outputs avoid prohibited content or misinformation
Semantic similarity. Use embeddings to assess closeness to reference answers

Example implementation

def test_response_contains_required_info(prompt, response): required_points = [“pricing options”, “delivery timeframe”] return all(point in response.lower() for point in required_points)

Image generation testing

Automated visual quality checks

CLIP-based evaluation. Measure text-image alignment
FID and IS scores. Assess perceptual quality and diversity
Style and content consistency. Verify adherence to input specifications

Code Generation Testing

Functional validation

Compilation testing. Verify generated code compiles without errors
Unit test execution. Run generated code against test cases
Static analysis. Check code quality metrics (complexity, maintainability)

Example approach

def test_generated_code(prompt, code_response): # Write code to temp file with open(‘temp_code.py’, ‘w’) as f: f.write(code_response) # Execute code with test inputs result = subprocess.run([‘python’, ‘temp_code.py’], input=’test input’, capture_output=True) # Check execution succeeded return result.returncode == 0

Automated testing workflow integration

To effectively integrate generative AI testing into development workflows.

Define test suites. Create collections of prompts and expected response characteristics.
Implement CI/CD pipelines. Automate testing on model updates or prompt changes

Example GitHub Actions workflow steps: – uses: actions/checkout@v3 – name: Run LLM tests run: python -m pytest tests/llm_tests.py – name: Evaluate model responses run: python evaluate_model_outputs.py

Set up monitoring. Track performance metrics in production to detect degradation

Response quality scores
User feedback metrics
Factual accuracy rates

Establish feedback loops. Continuously improve test coverage based on production issues

Human-in-the-loop testing

Some aspects of generative AI require human evaluation:

Human evaluation processes

Controlled A/B testing. Compare outputs of different models or prompts
Quality rating scales. Define consistent criteria for human evaluators
Diverse evaluator panels, Ensure different perspectives are represented

Automation opportunities

Automated filtering. Use models to pre-filter outputs for human review
Targeted evaluation. Direct human attention to high-risk or uncertain cases
Learning from feedback. Use human evaluations to train automated classifiers

An NLP development team reduced manual review time by 65% by implementing an automated classifier that flagged only the 12% of outputs that fell below confidence thresholds for human review.

Test data management

Effective generative AI testing requires careful test data handling:

Representative prompt collections. Create diverse prompts covering various use cases, edge cases, and potential vulnerabilities

Golden dataset curation. Maintain reference outputs for critical prompts to detect regressions

Adversarial examples. Include prompts designed to challenge model limitations or trigger problematic behaviors

Version control. Track changes to test prompts and expected outputs alongside model versions

Measuring test coverage

Traditional code coverage metrics don’t apply well to generative AI. Instead, consider:

Prompt space coverage. How well do test prompts cover the expected input space?
Edge case coverage. Are boundary conditions and rare scenarios tested?
Behavioral coverage. Do tests verify all expected model capabilities?
Vulnerability coverage. Are known failure modes and risks tested?

The future of generative AI testing

As generative AI continues to evolve, testing frameworks are advancing to address emerging challenges:

Multi-modal testing. Integrated testing across text, image, audio, and video outputs
Self-testing models. Models that can evaluate and verify their own outputs
Explainability tools. Frameworks that help understand why models generate specific outputs
Standardized benchmarks. Industry-wide standards for generative AI quality and safety

By adopting these automated testing frameworks and strategies, development teams can deliver more reliable, accurate, and trustworthy generative AI applications that meet business requirements while managing the unique risks these systems present.

ML Software Testing Best Practices

Machine learning systems demand a fundamentally different testing mindset than traditional software. Where conventional applications follow deterministic rules, ML models operate on probabilistic patterns, creating unique quality assurance challenges.

Three layers of ML testing maturity

ML models are designed differently from anything we have seen before. That is why it requires unique testing approach — not just rigorous testing, but Quality Engineering that takes into account how the model is trained and which decisions based on that data will be made.

Think of ML testing as a pyramid with three distinct layers, each building upon the last to create increasingly robust systems.

https://testfort.com/wp-content/uploads/2025/06/4-How-to-Test-AI-Applications-and-ML-Software_-Best-Practices-Guide-768x450.png

Layer 1: Foundation testing

At the base of our pyramid sits the fundamental infrastructure that supports ML operations. This layer focuses on testing the technical components that enable model operations.

Testing at this level ensures your data pipelines, training processes, and deployment mechanisms function correctly.

Data pipeline validation confirms data is flowing correctly from sources to training environments.
Environment consistency checks ensure your development, testing, and production environments process data identically.
Integration testing — API endpoints, data serialization/deserialization, and error handling — verifies that your model correctly interfaces with upstream and downstream systems.

Layer 2: Model-centric testing

The middle layer focuses on the ML model itself — its accuracy, behavior, and performance characteristics.

The central question at this level: “Does the model perform as expected across various scenarios?”.

Performance stability testing. Train your model multiple times with identical hyperparameters. Significant variations in results may indicate instability in your training process.
Slice-based evaluation. Test model performance across important data subgroups.
Invariance testing. Verify that model predictions remain stable when irrelevant features change.

For example, an image recognition model shouldn’t change its classification of a car because the background color changes.

Adversarial testing. Intentionally provide challenging inputs designed to cause model failures.

Layer 3: Business impact testing

The top layer of our pyramid connects model performance to actual business outcomes. Testing at this level ensures the ML system delivers real-world value.

This is often overlooked yet crucial—a technically “accurate” model that doesn’t improve business metrics is ultimately a failed project.

A/B testing new models against current production systems with real user traffic provides the most reliable measure of business impact. Set clear success metrics tied to business goals.
Shadow deployment runs new models alongside existing systems, logging what the new model would have done without actually affecting users.
Canary releases gradually roll out new models to increasing percentages of users, monitoring for issues before full deployment.

Testing lifecycle: From development to monitoring

Effective ML testing isn’t a one-time activity but a continuous process throughout the model lifecycle.

Pre-development: Setting the foundation

Before writing a single line of code, establish clear, measurable objectives for your ML system. Document both functional requirements (what the model should do) and performance requirements (how well it should do it).

Define acceptance criteria that bridge technical metrics and business outcomes. For a recommendation system, this might include:

Technical criteria: 85%+ precision@10, latency under 100ms
Business criteria: 5%+ increase in click-through rate, 3%+ increase in revenue per session

Development: Building with quality

During active development, implement automated testing at multiple levels:

Unit Tests → Component Tests → Integration Tests → System Tests

Unit tests verify individual functions and transformations.
Component tests validate distinct modules like data pipelines or training loops.
Integration tests check interactions between components.
System tests evaluate the end-to-end ML system.

Deployment: Validating in production

When transitioning to production, implement a staged deployment process:

Pre-flight checks: Verify model artifacts, configurations, and dependencies before deployment
Controlled rollout: Start with a small percentage of traffic, gradually increasing as confidence builds
Automated rollback: Establish thresholds for performance degradation that trigger automatic reversion to previous model versions

Post-Deployment: Continuous monitoring

Once in production, ML systems require continuous monitoring to detect issues:

Input monitoring tracks the distribution of incoming data, alerting when drift exceeds thresholds.
Output monitoring watches model predictions for unexpected patterns or shifts.
Performance monitoring tracks accuracy, latency, and resource usage over time.

A manufacturing company implemented comprehensive monitoring for their defect detection system. When a supplier changed their materials slightly, input monitoring detected the shift before quality problems occurred, allowing proactive model adjustment.

Cross-cutting testing concerns

Several testing practices apply across all stages of ML development.

Documentation as a testing tool

Treat documentation as an executable specification. Clear documentation of model inputs, outputs, constraints, and assumptions serves as both a guide for developers and a basis for test case generation.

Document known limitations explicitly. No model is perfect, and acknowledging edge cases where your model underperforms creates transparency and helps prevent misuse.

Data quality gates

Implement automated data quality checks that must pass before data enters your training pipelines:

## Example data quality check def validate_dataset(df): # Check for missing values missing = df.isnull().sum().sum() # Check for distribution anomalies numeric_columns = df.select_dtypes(include=[‘number’]).columns z_scores = df[numeric_columns].apply(stats.zscore) outliers = (z_scores > 3).sum().sum() # Check for class imbalance if ‘target’ in df.columns: class_counts = df[‘target’].value_counts() balance_ratio = class_counts.min() / class_counts.max() else: balance_ratio = 1.0 return { ‘missing_values’: missing < 100, # Threshold ‘outliers’: outliers < 500, # Threshold ‘class_balance’: balance_ratio > 0.2 # Threshold }

These gates prevent problematic data from corrupting your models and establish clear quality standards for data providers.

Reproducibility requirements

Make reproducibility a core testing requirement. Every model training run should be fully reproducible from the same inputs and random seeds.

Store all artifacts necessary for reproduction:

Training data (or references to immutable versions)
Model hyperparameters
Environment configurations
Random seeds
Feature transformation code
This allows proper debugging when issues arise and ensures consistent behavior from development to production.

Practical implementation roadmap

Implementing comprehensive ML testing doesn’t happen overnight. Follow this progressive approach to build testing maturity:

https://testfort.com/wp-content/uploads/2025/06/5-How-to-Test-AI-Applications-and-ML-Software_-Best-Practices-Guide-768x450.png

By gradually building your ML testing capabilities, you create a sustainable foundation for reliable AI applications that deliver consistent business value.

Evaluation Metrics for ML Models

Selecting the right metrics to evaluate machine learning models is critical to ensure they meet business objectives. Different ML applications require different evaluation approaches, and understanding these metrics helps teams make informed decisions about model deployment and improvement.

Classification model metrics

Classification models predict discrete categories (e.g., spam detection, fraud identification, customer churn). Key metrics include:

Accuracy. The percentage of correct predictions.

Accuracy = (True Positives + True Negatives) / All Predictions

While intuitive, accuracy can be misleading for imbalanced datasets where one class dominates. A fraud detection model that always predicts “not fraud” might achieve 99% accuracy if only 1% of transactions are fraudulent — but would be useless in practice.

Precision. The percentage of positive predictions that were actually correct.

Precision = True Positives / (True Positives + False Positives)

High precision means few false positives. This is essential when false positives are costly or disruptive, such as in spam filtering where legitimate emails incorrectly marked as spam create serious business problems.

Recall (Sensitivity). The percentage of actual positives correctly identified.

Recall = True Positives / (True Positives + False Negatives)

High recall means few false negatives. This is crucial when missing a positive case is expensive or dangerous, such as in cancer detection or security threat identification.

F1 Score. The harmonic mean of precision and recall, providing a balance between the two.

F1 Score = 2 * (Precision * Recall) / (Precision + Recall)

F1 score helps when you need to balance precision and recall, particularly with imbalanced data.

AUC-ROC (Area Under the Receiver Operating Characteristic Curve). Measures the model’s ability to distinguish between classes across different threshold settings.

Values range from 0.5 (random guessing) to 1.0 (perfect classification). A model with AUC-ROC of 0.85 or higher typically indicates good discriminative ability.

Confusion matrix. A table showing predicted vs. actual outcomes, providing a complete picture of model performance:

All classification metrics derive from these four fundamental values.

Regression model metrics

Regression models predict continuous values (e.g., price forecasting, demand prediction). Key metrics include:

Mean Absolute Error (MAE). The average of absolute differences between predicted and actual values.

MAE = (1/n) * Σ|actual – predicted|

MAE is intuitive and directly interpretable in the original units of the target variable, making it easy to explain to stakeholders.

Mean Squared Error (MSE). The average of squared differences between predicted and actual values.

MSE = (1/n) * Σ(actual – predicted)²

MSE penalizes larger errors more heavily than smaller ones, which is useful when large errors are particularly problematic.

Root Mean Squared Error (RMSE). The square root of MSE, bringing the metric back to the original units.

RMSE = √MSE

RMSE is widely used in forecasting and financial models where the magnitude of error can significantly impact business decisions.

R-squared (Coefficient of Determination). The proportion of variance in the dependent variable explained by the model.

R² = 1 – (Sum of Squared Residuals / Total Sum of Squares)

R² ranges from 0 to 1, with higher values indicating better fit. A value of 0.7 means the model explains 70% of the variance in the data.

NLP and text generation metrics

Natural language processing models require specialized metrics:

BLEU (Bilingual Evaluation Understudy): Measures the similarity between machine-generated text and reference text, commonly used for translation.

Scores range from 0 to 1, with 1 being perfect match.
A BLEU score above 0.3 indicates understandable text, above 0.5 indicates good quality.

ROUGE (Recall-Oriented Understudy for Gisting Evaluation): A set of metrics for evaluating automatic summarization.

ROUGE-N measures n-gram overlap.
ROUGE-L measures longest common subsequence.

Perplexity: Measures how well a language model predicts text.

Lower perplexity indicates better prediction.
Modern large language models aim for perplexity below 20 on standard benchmarks.

BERTScore: Computes similarity between generated and reference text using contextual embeddings.

Captures semantic similarity better than exact match metrics.
Correlates better with human judgment than traditional metrics.

Image and video generation metrics

For visual AI models, specialized metrics include:

FID (Fréchet Inception Distance): Measures similarity between generated and real images.

Lower FID scores indicate more realistic images.
State-of-the-art generative models typically achieve FID scores below 5.

SSIM (Structural Similarity Index): Measures perceived similarity between images.

Ranges from -1 to 1, with 1 indicating perfect similarity.
Captures structural information better than pixel-level comparisons.

PSNR (Peak Signal-to-Noise Ratio): Measures reconstruction quality in image compression.

Higher values indicate better quality.
Typically ranges from 20 to 40 dB for acceptable quality.

Fairness and bias metrics

Ethical AI requires evaluating model fairness across different demographic groups:

Demographic parity. Measures whether the positive prediction rate is the same across all protected groups.

|P(Ŷ=1|A=a) – P(Ŷ=1|A=b)| should be close to zero

Where A represents a protected attribute like gender or race.

Equal opportunity. Measures whether the true positive rate is the same across all protected groups.

|P(Ŷ=1|Y=1,A=a) – P(Ŷ=1|Y=1,A=b)| should be close to zero

Disparate impact. Ratio of the positive prediction rate for the unprivileged group to that of the privileged group.

P(Ŷ=1|A=unprivileged) / P(Ŷ=1|A=privileged)

The 80% rule in US law suggests this ratio should be at least 0.8 to avoid disparate impact.

Practical Implementation of ML Testing Metrics

When implementing evaluation metrics for ML models in production:

The right metrics differentiate academic exercises from business-driving AI applications. Teams testing machine learning models ensure that systems deliver measurable value by selecting metrics that reflect genuine business needs and stakeholder concerns.

Wrapping Up: Testing AI-Based and ML Solutions

The probabilistic nature of AI, its reliance on data quality, and its potential for unintended behaviors create testing challenges that standard QA approaches can’t address.

The cost of inadequate AI testing:

Compromised accuracy that erodes user trust;
Hidden biases that create legal and ethical problems;
Security vulnerabilities unique to AI architectures;
Compliance gaps that expose your business to regulatory penalties.

The organizations succeeding with AI aren’t necessarily those with the most advanced models, but those with the most reliable testing frameworks. They catch problems early, validate model performance across different scenarios, and monitor systems continuously in production.

The companies that invest in proper AI testing now will avoid the costly fixes, reputation damage, and regulatory penalties that come with AI failures.

Start with the basics:

Establish clear performance requirements tied to business outcomes;
Implement comprehensive data quality testing;
Validate model performance across diverse scenarios;
Monitor deployed models for drift and degradation;
Build fairness and ethical considerations into every testing stage.

The best AI isn’t the smartest or the fastest — it’s the one that consistently delivers value without unexpected failures. And that is what your testing process should focus on.

With the right testing approach, you can build AI systems that your business and customers can genuinely trust.

|P(Ŷ=1|Y=1,A=a) – P(Ŷ=1|Y=1,A=b)| should be close to zero

Test data management

Effective generative AI testing requires careful test data handling:

Representative prompt collections. Create diverse prompts covering various use cases, edge cases, and potential vulnerabilities

Golden dataset curation. Maintain reference outputs for critical prompts to detect regressions

Adversarial examples. Include prompts designed to challenge model limitations or trigger problematic behaviors

Version control. Track changes to test prompts and expected outputs alongside model versions

Measuring test coverage

Traditional code coverage metrics don’t apply well to generative AI. Instead, consider:

Prompt space coverage. How well do test prompts cover the expected input space?
Edge case coverage. Are boundary conditions and rare scenarios tested?
Behavioral coverage. Do tests verify all expected model capabilities?
Vulnerability coverage. Are known failure modes and risks tested?

The future of generative AI testing

As generative AI continues to evolve, testing frameworks are advancing to address emerging challenges:

Multi-modal testing. Integrated testing across text, image, audio, and video outputs
Self-testing models. Models that can evaluate and verify their own outputs
Explainability tools. Frameworks that help understand why models generate specific outputs
Standardized benchmarks. Industry-wide standards for generative AI quality and safety

ML Software Testing Best Practices

Three layers of ML testing maturity

Think of ML testing as a pyramid with three distinct layers, each building upon the last to create increasingly robust systems.

Layer 1: Foundation testing

At the base of our pyramid sits the fundamental infrastructure that supports ML operations. This layer focuses on testing the technical components that enable model operations.

Testing at this level ensures your data pipelines, training processes, and deployment mechanisms function correctly.

Data pipeline validation confirms data is flowing correctly from sources to training environments.
Environment consistency checks ensure your development, testing, and production environments process data identically.
Integration testing — API endpoints, data serialization/deserialization, and error handling — verifies that your model correctly interfaces with upstream and downstream systems.

QA Audit: A Shortcut to Software Quality and Customer Satisfaction

TestFort — Tue, 08 Jul 2025 18:19:45 +0000

Audit testing is a focused review of your QA processes, tools, and team. It identifies what’s working, what isn’t, and what needs to change to improve quality and efficiency.

An audit often covers standards and regulations and reveals potential risks your company faces — financial, reputational, and organizational.

Before we dive in, here’s one question to see if this article is worth your time:

Are you open to the idea that your QA process might be worse than you think?

If your answer is, “We’re fine, just looking for automation tips,” here’s an article on automation and a link to more resources we’ve written.

A QA audit only works if you’re ready to uncover issues you might not have noticed – and believe in the value of quality enough to face some uncomfortable truths.

Still with us? Let’s see how useful an audit can be.

Quick disclaimer from the Editor: We value your time more than search engine algorithms. This article focuses on actionable lists, not keyword-stuffed “audit philosophy” abstracts. We trust you don’t need a five-sentence explanation to understand terms like “team capability assessment.”

Key takeaways

Clear goals, measurable outcomes
**A QA audit is designed to improve workflows, reduce risks, and align QA processes with business objectives.
**Actionable insights
Deliverables include a detailed status report, gap analysis, risk assessment, and a prioritized action plan to address immediate and long-term issues.
Immediate and long-term benefits
Expect quick wins like fewer release delays and optimized testing workflows, along with scalable QA practices and improved customer satisfaction over time.
Tailored to your needs
From compliance testing to performance reviews, audits focus on your specific challenges, ensuring the solutions fit your team and product.
Team involvement is critical
Success depends on buy-in from all stakeholders. Starting small, focusing on quick wins, and addressing concerns early can make implementation smoother.
No one-size-fits-all
**Every quality assurance audit is unique, and the value comes from how effectively the findings are applied to your specific context.
**Real improvements take commitment
The audit identifies what needs fixing, but acting on the recommendations requires effort, resources, and ongoing collaboration.

What is Audit Testing

Audit testing evaluates your QA approach — processes, tools, documentation, and team capabilities. The goal is to find inefficiencies, gaps, and risks and then provide actionable recommendations. It’s all about assessing the system behind your quality assurance efforts.

Igor Kovalenko, QA Lead
“A well-executed QA audit delivers value in stages. Some benefits you’ll see within weeks, while others develop over time to create lasting impact.”

Benefits of QA audit for improving quality assurance process

A QA audit points out what’s holding your team back. Whether it’s outdated tools, messy workflows, or misaligned teams, the importance of quality audit is in helping you see what’s broken and how to improve it. Here’s what a software QA audit can help you achieve:

Discover process gaps. Many QA teams rely on outdated practices, such as maintaining unreliable test scripts or neglecting critical areas.
Improve test coverage. Identify which areas are over-tested, under-tested, or missing entirely.
Optimize tools. Evaluate whether current tools and frameworks are efficient or holding your team back.
Align stakeholders. Developers, QA engineers, and managers often view problems differently. An audit uncovers and reconciles these perspectives.
Save time and resources. Stop spending on redundant tests or processes that don’t add value.
Boost team efficiency. Clarify roles, workflows, and priorities to eliminate overlaps and bottlenecks.
Plan smarter. Before investing in automation or new proc esses, ensure your foundation is solid to avoid costly mistakes.

Audits deliver value in two stages. First, there are the quick wins — immediate fixes that save time, cut costs, and improve team alignment. Then, there are the long-term benefits, where you see lasting improvements in scalability, team morale, and release confidence.

Let’s explore both:

How is it different from regular QA?

Regular QA focuses on testing the software for bugs and verifying functionality. Audit testing looks at how the QA process itself is organized and executed:

Are your tools and processes effective?
Is your team equipped to handle the workload?
Are your workflows slowing down releases or causing issues?

It’s a higher-level review to make sure QA is supporting your business goals effectively.

Main components of testing quality audit

We’ll get into the specific types of audits later, but let’s start with the essentials. No matter the focus — compliance, tools, or processes — these components are what every software QA audit looks at. They’re the building blocks; if they’re broken, the rest doesn’t matter.

Process review. Examines how your testing processes are structured and whether they support consistent, repeatable results.
Documentation analysis. Evaluates test plans, reports, and other QA documents to check for completeness, accuracy, and usefulness.
Tool assessment. Reviews the tools used in your QA process to ensure they’re appropriate, fully utilized, and up-to-date.
Team capability evaluation. Assesses the skills, knowledge, and capacity of your QA team to handle current and future demands.

Workflow efficiency check. Identifies bottlenecks, redundancies, or unnecessary steps in your QA workflows.

Igor Kovalenko, QA Lead
“Every QA audit starts with a simple truth: most clients don’t really know their test process. They’re doing something, but it’s rarely standardized or structured. Our job is to change that.”

*Importance of Audit for Software Quality, QA Processes, and the Bottom Line
*

Every stakeholder from the executive level or the QA trenches has specific concerns.

A QA audit is a tool to answer critical questions about the state of your processes, the risks you’re taking, and the opportunities you’re missing.

By addressing these concerns, audits ensure alignment between quality goals and business objectives.

Let’s play “I need to know if…”

https://testfort.com/wp-content/uploads/2025/02/3-QA-Audit-1-1024x965.png

*How QA audit helps different stakeholders build a successful software
*
A successful quality audit addresses concerns across your organization’s leadership. Different stakeholders face distinct challenges that quality issues create in their areas of responsibility.

Executives

Quality directly affects revenue, reputation, and customer retention. CEOs and COOs need to know if quality risks cost them money or if their processes can scale with growth. For CTOs, top priorities are ensuring efficient use of resources and maintaining a sustainable tech stack.
**
QA Leads and Development Managers**

These roles are closer to the ground, managing the day-to-day challenges of delivering high-quality software. QA leads need audits to identify gaps in coverage, tools, and processes, while development managers rely on them to ensure testing doesn’t hinder development speed.

Product Managers

Delays caused by quality issues can disrupt the entire product roadmap. Audits clarify whether testing efforts align with user expectations and business goals, helping product managers prioritize features and timelines effectively.

Should we include customers in the picture?

Yes. While internal teams feel the operational impact of quality issues, the final customers or users experience the product firsthand. A QA audit indirectly benefits them by ensuring:

Fewer bugs and smoother functionality.
Consistent performance and reliability.
Features that meet their needs without delays or disruptions.

Happy customers lead to higher retention and better reviews, making them an important part of the quality conversation—even if they’re not directly involved in the audit.

With the key stakeholders covered, it’s time to explore whether a software testing audit fits your current situation.

Igor Kovalenko, QA Lead
“When clients come for a QA audit, they ask about their product’s survival. The shelf life of your product depends directly on its quality and usability — that’s what we’re measuring. Better finding bugs with more efficient tools secure your business future.”

Do You Need An Audit Team to Streamline Your Software Testing Process?

Whether you need a QA audit team often comes down to your current challenges. Growing too fast? Struggling with outdated processes? Facing high-pressure releases? These are all signs that your QA efforts might need outside help to stay on track.

Let’s look at common scenarios where a software testing audit proves its worth and how it addresses the issues you’re facing.

Taras Oleksyn, Head of the Test Automation Department
“Even if you have senior QA engineers, bring in an external auditor. Fresh eyes spot normalized problems your team has learned to live with.”

#1. Rapid growth or scaling

Your user base is growing, and you’re releasing features faster than ever. But with speed comes risk—bugs are slipping through, and the QA team is struggling to keep up.

Why software testing audit helps:

Evaluates whether your testing processes can handle increased demands;
Identifies gaps in test automation and regression coverage;
Ensures scalability without compromising quality.

#2. Undefined or outdated processes

Processes haven’t been documented, or they’re outdated and can’t keep up with modern development practices. Everyone does things “their way,” and inconsistencies create delays and defects.

How software QA audit helps:

Brings clarity and structure to workflows;
Highlights inefficiencies caused by manual work or outdated tools;
Creates a roadmap for aligning QA with current business needs.

#3. Increased customer expectations

Your users expect flawless performance, but recurring bugs or slow releases are affecting satisfaction and retention.

How software quality audit is useful here:

Improves coverage in critical areas like performance, usability, and security testing;
Aligns testing priorities with customer expectations;
Reduces defect leakage that impacts users.

#4. Regulatory or security pressures

You’re entering a regulated market (e.g., healthcare, fintech) or dealing with sensitive user data. Non-compliance or security breaches are no longer just risks—they’re liabilities.

How quality assurance audit may help:

Ensures compliance with industry standards like GDPR, HIPAA, or PCI DSS;
Validates the effectiveness of security testing practices;
Mitigates risks that could lead to fines or reputational damage.

#5. High technical debt

Your team is spending more time fixing issues than building features. Legacy code, duplicated test cases, and unorganized test environments are dragging everything down.

Why software testing audit comes in handy:

Identifies where technical debt is slowing down your QA processes;
Suggests strategies for cleaning up and optimizing test suites;
Creates a plan for sustainable quality improvements.

#6. New tools or practices

You’ve recently adopted new testing tools or methodologies but aren’t sure if they’re delivering results. Or you suspect your existing tools aren’t being used effectively.

Why rely on software QA audit:

Evaluates tool usage and their ROI;
Identifies opportunities to enhance automation or adopt better practices;
Ensures tools are aligned with your business and QA goals.

#7. Critical release pressure

You’re about to launch a major update, and the risks of defects are higher than ever. The team is overwhelmed, and last-minute testing is the norm.

Why QA testing audit may help:

Focuses on high-priority areas to reduce risks;
Optimizes the release certification process for faster, more reliable delivery;
Provides actionable insights to avoid last-minute surprises.

Now that you’ve seen how a QA audit can solve real-world problems, it’s time to explore the types of audits available. From process reviews to compliance testing, different audits focus on different priorities — let’s break them down and see what fits your nee
ds.

Igor Kovalenko, QA Lead
“Large companies don’t become large overnight, nor do their QA processes. What we often see are layers of legacy processes built up over time. For smaller companies, the challenge is different. Early teams often rely on makeshift processes, and as they grow, these inefficiencies scale with them. If you’re hiring new people, don’t let them cement bad habits — use an audit to reset and build better systems.”

Types of Audit for Software Testing and Quality Assurance
There is more than one type of software audit in terms of testing overall Quality Assurance and Quality Engineering. Be it an internal audit or an audit done by QA auditors from one of the top testing companies, you can define almost any analysis focus.

The most popular audit checks focus on processes and workflows. However, an audit is a comprehensive evaluation that can be done at a minimum and maximum, based on your needs and issues in the software testing phase.

#1. Quality assurance process audits: Analyzing workflows

This audit examines how testing processes are planned, executed, and documented. It looks for inefficiencies, bottlenecks, and unclear roles across areas such as:

Test planning, execution, and reporting workflows.
Bug tracking and prioritization.
Release certification processes.
Test environment and data management.
Documentation practices.
Example use case: If delays often occur before release, a process audit might reveal gaps like inefficient handoffs between teams or redundant manual tasks.

Key outcomes

Standardized workflows.
Clear roles and responsibilities.
Improved testing efficiency.

#2. Compliance audits: Meeting standards

For businesses in regulated industries like finance or healthcare, compliance testing audits focus specifically on validating that your QA testing procedures meet industry regulations (e.g., HIPAA, ISO, PCI DSS):

Testing coverage for regulatory requirements: Are your test cases specifically designed to validate compliance-related features (e.g., encryption, access controls)?
Audit trail validation: Are test results documented in a way that satisfies regulatory requirements?
Test documentation standards: Are test plans, execution logs, and defect reports clear and aligned with compliance needs?

A healthcare app ensures that its QA testing process includes thorough validation of encryption methods and role-based access controls required by HIPAA. Missing or incomplete tests are flagged during the audit, leading to targeted fixes before an external compliance review.

#3. Security testing audits: Identifying vulnerabilities

Security testing audits focus on how your QA enhances software’s ability to withstand attacks and protect data. They evaluate practices like:

Static and dynamic application security testing (SAST/DAST);
Penetration testing frequency;
Vulnerability scanning;
Security compliance testing.
Example use case

A SaaS company uses a security testing audit to uncover weaknesses in its user authentication process and patches it before a breach — software meets necessary standards, and reputation risks are avoided.

Key outcomes:

Security testing roadmap;
Risk mitigation plans;
Improved trust and data protection.

#4. Testing efficiency audits: Optimizing resources

It focuses on eliminating redundancies and optimizing resources in your testing process. Areas reviewed include:

Overlapping or missing test cases;
Underutilized automation frameworks;
Performance testing bottlenecks.
Example use case

A QA team running repeated manual tests automates key scenarios after an efficiency audit.

Key outcomes:

Leaner, faster workflows;
Reduced testing costs;
Better resource allocation.

#5. Technical testing audits: Evaluating practices and tools

This type reviews your actual testing practices and tools to identify coverage gaps and inefficiencies. Focus areas include:

Unit and integration test effectiveness;
End-to-end testing scenarios;
Automation framework usage;
Performance testing methods.
Example use case

A growing codebase reveals low unit test coverage, prompting the introduction of automated testing tools.

Key outcomes:

Identified testing gaps;
Enhanced test coverage;
Optimized tools and frameworks.

#6. Performance and load testing review

This audit focuses on how well your performance and load-testing processes ensure the system’s reliability under different conditions. It evaluates whether your testing scenarios, metrics, and methodologies are sufficient to identify bottlenecks and support scalability.

Load testing scenarios: Are they realistic and reflective of actual user behavior?
Stress testing approach: How does the system handle extreme conditions?
Performance metrics tracking: Are you consistently monitoring throughput, latency, and resource utilization?
Scalability testing: Does the system perform well as user numbers increase?
Response time benchmarks: Are your benchmarks aligned with user expectations and business needs?

Example use case

An eCommerce platform prepares for Black Friday by running load tests to simulate peak traffic. The audit reveals that the database queries are a bottleneck, allowing the team to optimize them ahead of time.

Key outcomes

Improved system reliability during peak loads.
Early identification of performance bottlenecks.
Scalability plans that match business growth.

Performance and load testing reviews ensure your software can handle real-world usage, reduce downtime, and deliver consistent experiences to users.

#7. Code quality audits: Focusing on development practices

Yes, this is not exactly part of testing in software quality assurance audit, but it may be necessary to extend the areas of improvement. It is also needed to allow for the early development of fixes to ensure the release of the final software is more of a gain than a pain.

Software quality audit evaluates the quality and maintainability of your codebase. It looks for technical debt, inconsistent practices, and areas prone to bugs. Key focus areas include:

Identifying duplicate code and inconsistencies;
Reviewing error-handling practices;
Highlighting areas with poor maintainability.

Example use case

A legacy codebase with poorly written functions gets a cleanup plan through a code quality audit.

Key outcomes:

Cleaner, maintainable code;
Fewer defects and faster releases;
Easier scalability for future development.

How does the process actually work? Each audit, regardless of its focus, follows a structured approach to deliver actionable results. Take a closer look at the typical roadmap for a QA audit and what each phase involves.

How it goes: QA Audit Roadmap Example

The QA audit process is straightforward but thorough. It breaks your current setup into manageable pieces, uncovers what’s holding you back, and builds a realistic plan to fix it. While every audit adapts to your needs, the roadmap typically looks like this:

Initial assessment phase

Document review
Team interviews
Process observation

Analysis phase

Gap identification
Risk assessment
Improvement opportunities

Recommendation phase

Action plan
Priority setting
Resource planning

Implementation phase

Quick wins
Long-term improvements
Team training

But what’s the endgame? Let’s move on to the Key Objectives, where we’ll connect the dots between streamlined processes and customer satisfaction.

Key objectives: From Testing Processes to Customer Satisfaction

A QA audit has clear goals: improve workflows, reduce risks, and deliver better software. Whether cutting delays, increasing efficiency, or making your customers happier, the audit’s value lies in measurable outcomes that impact your team and your product.

#1. Process standardization

Your QA processes should be predictable and repeatable, not reinvented for every release. A good audit identifies inconsistencies and provides a roadmap for creating unified testing workflows across your team.

Ensures smoother handoffs between teams.
Makes scaling QA efforts easier.

#2. Quality metrics improvement

You can’t improve what you don’t measure. A software quality audit defines which metrics matter (e.g., defect density, test coverage) and helps establish benchmarks.

Provides clarity on what “good quality” means for your product.
Tracks progress over time, making improvements visible.

#3. Release cycle optimization

Delays kill momentum. A QA audit streamlines testing processes to reduce bottlenecks and unnecessary steps.

Speeds up time-to-market.
Reduces stress on teams by creating a predictable release cadence.

#4. Bug reduction

Finding bugs earlier isn’t just about testing more—it’s about testing smarter. A QA audit highlights areas where your testing is weak or missing entirely.

Prevents high-impact defects from reaching production.
Saves time and money on post-release fixes.

#5. Team efficiency increase

Disorganized processes and unclear roles waste time and energy. An audit clarifies who does what and where workflows can improve.

Reduces duplicated efforts.
Gives your team more time to focus on strategic tasks.

Mykhailo Tomara, QA Lead
“An external auditor brings more than expertise — they bring objectivity. They’re not tied to internal politics or historical decisions. They can see what others might miss or hesitate to mention.”

#6. Customer satisfaction growth

At the end of the day, all the metrics and improvements lead to this: happy customers. A QA audit ensures your product meets user expectations for quality and reliability.

Fewer bugs and smoother functionality mean better user experiences.
Happy customers are loyal customers, driving retention and positive reviews.

#7. Cost optimization

A QA audit helps you get more value out of your resources by reducing inefficiencies and eliminating unnecessary spending.

Cuts costs on redundant testing and underused tools.
Maximizes ROI on QA investments.

#8. Risk reduction

From compliance issues to production failures, risks are everywhere. A QA audit pinpoints vulnerabilities before they become costly problems.

Reduces the chance of legal or regulatory trouble.
Prevents reputational damage from public-facing bugs.

Reaching these objectives starts with solid, actionable deliverables. Look at what a QA audit provides and how each deliverable contributes to real improvements in your testing process.

Possible Deliverables of Audit in Software Testing

A QA audit produces specific, actionable outputs that help improve your testing process.

In the end, every part of the report will be focused on the following:

What’s working well;
What’s broken or inefficient;
What needs immediate attention?

But knowing the results is only half the job. The real value comes from using these deliverables effectively. Here’s what you’ll get and how to make the most of it:

Detailed Status Report

Current testing quality metrics;
Critical process bottlenecks;
Tool usage analysis;
Team capability assessment;
Compliance status,

Don’t just review the numbers — use them to pinpoint immediate action areas. For example:

Low test automation coverage (e.g., 45% vs. industry standard 70%) signals where to prioritize automation tools.
Delayed releases (e.g., 35% of releases delayed) highlight bottlenecks to streamline first.
Underutilized tools (e.g., only using 30% of features) mean optimizing your existing investments before buying new tools is time.

Gap analysis

Identifies the gaps between where your QA efforts are now and where they need to be.

Test coverage;
Automation levels;
Security testing;
Performance testing;
Team skills.

Focus on the gaps with the highest business impact. Those may be:

Missing critical tests (e.g., for API endpoints) could lead to costly post-release issues.
Gaps in automation provide a clear starting point for automation roadmap planning.
Team skill gaps inform training plans and hiring strategies.

Risk assessment

Understanding risks helps you address high-impact areas first and avoid costly surprises.

Example of critical risks identified:

Missing security tests for payment processing;
No automated regression testing;
Incomplete API test coverage.

Address risks based on severity and potential impact. For instance:

Fix missing security tests first to avoid compliance fines or breaches.
Build an automated regression suite to catch recurring issues early.
Document API testing gaps for immediate coverage improvements.

Action plan with priorities

When you just receive your QA audit report, you may feel overwhelmed, overjoyed, or motivated to do everything at once. Both don’t help. To ensure the software quality testing actually improves, you need to set priorities. A good software quality audit or QA review provider should help you with these.

Short-term fixes (1-2 months):

Configure existing tools properly;
Fix critical security gaps;
Train team on automation basics.

Medium-term improvements (2-6 months):

Implement automated regression suite;
Set up continuous testing pipeline;
Standardize test documentation.

Taras Oleksyn, Head of the Test Automation Department
“Teams typically spend more time living with problems than it would take to fix them. A focused two-week audit can save months of emergency fixes and weekend work.”

Resource requirements

Helps you plan budgets and allocate resources effectively.

Team needs:

Number of QA engineers needed;
Required technical skills;
Training requirements.

Tools and infrastructure:

Required testing tools;
Environment upgrades;
Automation framework needs.

Here is one of the approaches to execute the action plan effectively:

Map team needs (e.g., additional QA engineers or training) to project timelines.
Ensure tools and infrastructure align with the roadmap (e.g., upgrading test environments or adopting a scalable automation framework).
Use this to justify budget increases or resource reallocation with clear ROI projections.

Timeline for improvements

A timeline helps set expectations and keeps everyone accountable. It gives a realistic schedule for implementing changes and seeing results.

Short-term wins to build momentum.
Long-term plans for sustainable quality improvements.

Provide a realistic schedule for delivering results without overwhelming your team:

Use quick wins to build momentum (e.g., within the first month).
Assign accountability for critical fixes (1-3 months).
Build long-term goals (6+ months) into regular sprints or development cycles to ensure they stay on track.
Review progress regularly to adjust priorities based on outcomes.

While these deliverables provide a clear path forward, it’s not a quick fix or a one-size-fits-all solution. Explore what a QA audit won’t do — and why understanding its limitations is just as important as leveraging its strengths.

Quality Audit: What You Should Not Expect

Many teams expect quick fixes or perfect solutions. Reality is different. A quality audit is the start of improvement, not an instant solution. Understanding these limitations helps plan better and achieve tangible results, preferably high-quality software products.

Immediate quality improvement

An audit won’t make your software product better overnight. It highlights what’s broken or inefficient, but fixing it takes time, effort, and the right resources. Think of it as a starting point, not the end solution.

There are some fast results you get, but you can’t stop there if you want your audit process to truly pay off and improve the quality of the product in the long term.

Zero defects

Bugs aren’t going away completely, no matter how good your testing procedures are. The goal of an audit is to reduce the big issues and give you a QA in the development process that catches most problems earlier. But there will always be some defects — it’s a reality in the software development lifecycle. Even a strong software quality audit can’t ensure you never face another defect in the development or production cycles.

Complete automation or a roadmap to it

Automation isn’t the answer to everything. Some things still need manual testing; even automated processes require maintenance and skilled people to manage them. The audit process helps identify areas for improvement where automation adds the most value. But it won’t “make everything automatic” (this is a real request, we swear).

A balance of automation and manual testing techniques is essential to ensuring reliable software.

No resource investment

You’ll need to put in work — whether it’s time, money, or retraining your QA team. An audit might show inefficiencies, but addressing them could require new tools, better testing procedures, or hiring additional team members.

The effort of the QA team and the speed of implementing these changes will determine how soon you see results. Be ready to commit if you want to enhance the overall quality and achieve high-quality software delivery.

Instant team buy-in

Not everyone will love the idea of change. If the audit suggests overhauling outdated workflows, tools, or testing approaches, expect pushback. Convincing your team takes more than just audit findings — it takes clear communication and a focus on how these changes improve software solutions and quality control for both the team and the product.

One-size-fits-all solutions

There’s no universal fix for every QA and development process. Your problems are unique, so are the solutions. A QA audit gives you specific recommendations tailored to your software development process, aligning them with best practices and industry standards rather than relying on generic strategies.

Taras Oleksyn, Head of the Test Automation Department
“Want your QA audit to succeed? Start small, prove the value, then scale. One successful team can become your best case study for company-wide transformation.”

Even if your approach to quality assurance audit is totally realistic, there are still issues you may need to deal with first within the team before you see real changes.

QA Audit Potential Problems Despite the Best Practices in Place

It seems simple. The goal of quality assurance is to collect data on how the software works, show blind spots, and offer improvements based on testing and analysis.

Quality software control and QA audits analyze the efficiency of this process, what it lacks, what it needs, what works, and what costs more money than it should. Everyone should be happy to get this information, right? Unfortunately, not always.

We live and work in the real world, where not all the aspects of software quality assurance are rational and bias-free.

Why QA Audit gets pushed back

“Sounds great, but not now” – we hear this a lot.

And it’s rarely because teams don’t care about quality. It’s usually a mix of competing priorities, limited resources, and fear of shaking things up. Here’s what’s really happening:

The “If It’s Not Broken” syndrome

Teams living with known inefficiencies because “that’s how we’ve always done it”;
Unstable tests kept running “just in case they catch something”;

Manual processes that “only take a few hours” (those hours add up).

Mykhailo Tomara, QA Lead
“During audits, we talk to everyone involved — developers, QA specialists, and managers. Each team sees different issues or interprets them differently. Understanding all these perspectives is the key to real improvements.”

The resistance triangle

Resistance to QA audits often comes from different corners of the organization, each with its own pressures and priorities. Again, it’s all about competing demands and limited bandwidth.

Business Side

Everything needed “for yesterday”;
Features prioritized over infrastructure;
“We can’t slow down for process improvements”.

Development Team

Comfortable with current workflows;
“Our bugs aren’t that bad”;
Too busy fixing today’s problems to prevent tomorrow’s

QA Team

Overwhelmed with daily testing;
No bandwidth for process improvement;
Fear of exposing team weaknesses.

https://testfort.com/wp-content/uploads/2025/02/5-QA-Audit-3-1024x576.png

Each team has valid concerns. Business needs features delivered. Developers want to keep coding. QA feels overwhelmed. But delaying quality improvements costs more than facing them.

*Hidden costs of delay *

https://testfort.com/wp-content/uploads/2025/02/6-QA-Audit-2-1024x750.png

Get Software Quality Assurance Audit Buy-In from Your Team

You may have the most thorough audit possible, but you can’t improve software quality and customer satisfaction if your team doesn’t want to get involved or even feels threatened by the changes.

We share some tricks with our audit clients to ensure that the software QA audit results are implemented without too much pushback.

Assemble supporters

It’s best if the audit requests come from within. For example, you are doing some damage control, and during a retrospective session, someone says:

“We must ensure the software is error-free and meets the desired quality standards. We have some blind spots now, so let’s invite an external auditor.”

It may happen, but it’s rare and normally only caused by some painful errors you would actually like to avoid in the first place.

So, the more realistic way is to gather a few supporters with a certain level of authority across the teams. This way, you will have a) people ready to talk the truth to auditors b) advocates of change for when the audit results are ready.

Start small

Work on one critical flow instead of everything “that’s gone wrong.”
Start with a small task or separate team to ensure a global buy-in based on the results.
Focus on quick wins that don’t disrupt current work.
Show value fast (within 2 weeks).

Make it pain-free

Work around team schedules;
Use existing documentation first;
Minimize meeting overhead.

Focus on benefits

Faster releases;
Less weekend work;
Fewer emergency fixes;
Better work-life balance.

You may still get pushed back, even by people who value your software’s success. Change is hard, and it may feel like a personal threat in the recession market. Still, if you follow some steps above, you have a better chance of having a truly good ROI for that audit.

Igor Kovalenko, QA Lead
“The most successful QA transformations start with allies inside the company. Find the people with reputation and authority who believe in quality — they’ll help reduce resistance to change and champion the improvements

Wrapping Up

A QA audit highlights what’s slowing your team down and provides actionable steps to improve. It’s about identifying gaps, fixing inefficiencies, and aligning your QA processes with your business goals.

Whether you’re tackling immediate issues like redundant workflows or setting up a scalable QA strategy for the future, the success of an audit depends on what you do next. Prioritize quick wins to build momentum, involve your team early, and focus on delivering real value.

Better processes, fewer bugs, and more confident releases aren’t out of reach. With a clear plan and commitment, a QA audit can turn quality into a competitive advantage for your business.

Top 18 Test Automation Trends to Look Out for in 2025 and Beyond

TestFort — Thu, 20 Feb 2025 18:35:32 +0000

There are two misconceptions when it comes to testing. One of them is that developers alone can test products to reduce costs. And second is that nothing much has changed since automated testing came to play.

The truth is? The testing industry is evolving by leaps and bounds, and if you don’t invest in testing, the cost of glitches can be extremely high. You may have heard about Bangladesh Bank that was hacked and $81 million was stolen. However, they could have easily prevented this if not for a system glitch that interrupted the printing process and made it impossible to detect suspicious transactions in time.

This is just one example of how cutting testing costs can have devastating consequences, but the idea is clear: testing isn’t the area that should be curtailed. Vice versa. It’s important to stay up to date with what’s happening in the world of testing and invest in the latest trends.

In this article, we’ve compiled some of the most prominent automation testing trends for 2024 that have taken off this year and are set to continue. Be sure to explore the benefits of each and see how they can fit your testing strategies.

Key Takeaways

Testing evolves by leaps and bounds, demanding QA teams to stay ahead of the curve. Keeping pace with trends in automation testing is crucial for ensuring that testing strategies are effective, scalable, and aligned with the latest technological advancements.
One of the latest trends in automation testing is quantum computing. Although still in its early stages, quantum computing promises faster, more precise testing, tackling complex systems that traditional methods struggle with.
Shift-left testing doesn’t lose its relevance. It continues to evolve, pushing testing even earlier in the development cycle, which reduces costs and accelerates bug detection before major issues arise.
Along with shift-left testing, scriptless test automation and codeless automation are becoming more popular, allowing a wide range of professionals regardless of their technical expertise to create and run tests without writing a code.
The future of testing automation looks promising. As technologies continue to advance, testing practices are becoming smarter and more efficient, ensuring faster releases and higher quality of software products.

18 Automation Testing Trends for 2024

Some trends are just the flash in the pan. In theory, they are full of promise but come real-case tasks, they often reveal their limitations and impracticality. We’ve hand-picked test automation trends for 2024 that seem both realistic to implement and have the potential to truly make a difference in how we assure quality.

QAOps Is Gaining Popularity

QAOps is a booming trend in 2024. As the name suggests, this methodology combines a Quality Assurance (QA) approach and IT operations to speed up testing. In other words, it integrates quality assurance and testing into the DevOps process, introducing thorough testing at every stage of the software development cycle, not just at the end.

To elaborate, QAOps framework allows developers and testers to work together. Through this close-knit collaboration, companies can pinpoint the different scenarios that real users might encounter when they start interacting with the app, thereby improving the testing process.

Some companies have given this approach their own twist. They organize so-called “testing parties”, inviting all interested team members to take part in testing. By doing so, they not only ensure there’s no bias in testing, but they also get the opportunity to test their products from different perspectives, allowing them to ultimately achieve better outcomes.

It’s like building a house where developers build the foundation, walls, and the roof, and testers ensure from the beginning that all of these components are made strong. Later, when the house is ready for exploitation, no major rework will have to be done.

Benefits

Improved collaboration between testers, developers, and IT operations teams.
Boosted productivity of the team through ongoing knowledge sharing.
Faster release of new products and features due to the quick detection of bugs.
Enhanced customer experience achieved by delivering software products of a high-quality standard.

Extensive adoption of robotic process automation

Another automated testing trend that is growing in popularity is the use of Robotic Process Automation (RPA). RPA, also known as software robotics, has the ability to mirror testers’ interactions with software applications. By recording actions performed by testers and learning testing sequences, it can imitate the same process, saving you lots of time performing repetitive tasks.

RPA has become widely popular due to the ease and speed of its implementation. Compared to traditional automation, which requires specialized hardware and software to automate repetitive tasks, RPA uses bots. Often run by humans, these bots can be easily programmed using off-the-shelf software and trained in a matter of hours or a couple of days, making RPA a more cost-effective testing solution for most companies.

RPA testing techniques are being used across various industries and will become even more widespread in the near future. According to Statista, the RPA market will grow to $13.39 billion by 2030, which is a huge jump from $3.17 billion in 2022.

Moreover, RPA will become much more intelligent thanks to the advances in artificial intelligence and machine learning soon. This means that RPA will not only copy human actions but also understand information, learn from experience, and proactively address issues. Ultimately, this will allow it to execute more complex decision-making processes without much guidance on the tester’s part.

Benefits

Reduced labor costs due to the automation of repetitive test scenarios.
Less of a risk for human error, as RPA accurately replicates testers’ interactions.
Significant time and resource savings due to RPA bots’ ability to execute tests across multiple systems and applications.
Scalability that allows organizations to perform small, large, or even enterprise-level tests on demand.

Active Use of AI and ML Testing

AI and ML tools are making a splash in the testing industry. With their ability to automate virtually every aspect of testing, from test case creation and test execution to test maintenance, they come indispensable for QA teams.

The practical applications of AI and ML in automated testing are numerous. From identifying features for testing, creating test cases without manual test scripts, and running thousands of tests virtually in minutes, it can do all that and much more without human support.

As we progress in the future, the capabilities of AI and ML are expected to only evolve. In the near term, it will be possible to digitize testing processes by creating AI-powered avatars of famous testers, such as Bret Pettichord, Cem Karner, Tariq, you name it. By imparting their knowledge and expertise to bots, organizations will be able to create strong virtual teams of testers that can execute all types of testing and adequately measure the quality of a project based on input derivable.

Other examples of using AI and ML in testing include:

Natural language processing. NLP algorithms can extract and analyze requirements from natural language documents, helping with test case creation and ensuring that tests align with the project’s goals.
Visual testing with computer vision. AI-driven computer vision systems can automatically compare UI elements and screens to identify visual defects or inconsistencies in applications.
Automated bug triaging. ML models can help prioritize and categorize incoming bug reports.
Behavior-driven testing with AI. AI can understand user behavior patterns and generate test scenarios that mimic real-world user interactions, enabling QA teams to cover more test cases.
Security testing. AI can simulate cyberattacks and identify vulnerabilities in software.

Aside from that, AI excels at predicting outcomes. By analyzing historical data and patterns, it can identify with a high probability what bugs and issues are likely to occur and help prevent them before they become major issues.

“Remember, AI tools are just that — tools. They are there to augment our abilities, not to replace testers. So, whether you’re looking to leverage AI automation to improve workflows or optimize testing approaches, the resolution of these issues requires human insight and ingenuity.”
Taras Oleksyn, QA Lead

Benefits

Faster release cycles;
Quick test case generation;
Automatic test maintenance;
Predicting the likelihood of bugs and testing outcomes based on historical data;
An extensive test coverage across various devices ensures that the software is thoroughly tested and no bugs slip through the cracks.

Explainable AI is taking traction

While AI is confidently working its way in testing helping QA teams improve the accuracy of the results and mitigate errors, the main thing that makes this testing useful is transparency. QA engineers need to be able to understand why this or that decision was made and act on them accordingly. That’s where explainable AI plays a pivotal role.

In layman’s terms, explainable AI (often referred to as “explainability”) is the ability of testers to determine what exactly accuracy means in each particular case and predict future decisions based on derived results. With clear definitions of AI results, they can prevent bugs, defects, and biases, ensure their products are regulatory compliant, and build trust with key stakeholders.

In the coming years, explainable AI will become an integral part of testing. We will see the emergence of many new AI frameworks and tools like SHAP, TensorFlow, Lime, and similar, which will speed up the adoption of explainable AI in software testing.

Benefits

Early detection of bugs, reducing the cost and time to fix them.
Higher quality software as testing is performed from the start of development.
Shorter development cycles due to faster feedback on code changes.
Reduced risk of late-stage failures that can delay product releases.

Focus on ethical testing will increase

In 2024, the focus on ethical testing will increase even more, as organizations recognize the importance of responsible software development. Ethical testing embraces more than just technicalities of software functionality. First and foremost, it’s about ensuring that technology is safe and fair and understanding how it has arrived at this or that outcome.

The main concern with AI is that it’s been operating as a “black box” for a long time. It generates results, but why and how it has made these decisions is not always clear, often leading to biases and discriminatory outcomes. Ethical testing addresses these concerns. With ethical testing, QA teams focus on mitigating biases early in development, ensuring that algorithms are fair, transparent and do not perpetuate discrimination related to race, gender, or socioeconomic factors.

Seeing how much attention is paid to AI ethics today, ethical testing is moving from a nice-to-have to a must-have practice and becoming one of the significant trends in automation testing for 2024 and beyond.

Benefits

Provides clear insights into how AI systems make decisions.
Helps identify and address potential biases early in development.
Ensures alignment with global regulations and standards.
Builds confidence among users and stakeholders.

A shift to self-healing tools

One of the biggest hurdles in automated testing is flakiness. When tests show different results after each run, knowing which one of them is accurate becomes a challenge. Self-healing tools aim to address this challenge. By automatically adjusting test scripts when changes occur in the application under test, they can ensure that tests remain stable and QA teams have less supporting tests to do.

The best thing is, self-healing tools constantly learn. With each run, they process more data and patterns and identify recurring issues, becoming more efficient at creating tests. This along with their ability to process volumes of data going through developer’s pipelines makes them essential for modern QA processes requiring both accuracy and reduced downtime.

Benefits

Proactive issue detection, minimizing downtime.
Seamless integration with CI/CD pipelines, ensuring up-to-date and reliable test scripts.
AI-driven prioritization of test cases.
Continuous adaptation to application changes, requiring minimal manual intervention.

Blockchain testing is becoming a hot trend

While you most definitely have heard of blockchain, you might not have stumbled upon the possibilities blockchain testing brings to the table. Blockchain testing can be used for a variety of applications. From securing transactions to ensuring the integrity of supply chains and even verifying the authenticity of digital assets, it renders itself useful in a wide range of industries and scenarios that need accurate data validation.

If you’re looking to ensure peak Decentralized Applications (DAPPs) performance, definitely consider blockchain testing. Blockchain provides seamless scalability to DAPPs, ensuring they can efficiently handle increased volumes of data and transactions. Moreover, conducting comprehensive tests helps identify potential vulnerabilities, inefficiencies, and bottlenecks that may hinder the smooth operation of DAPPs.

However, it’s worth noting that blockchain testing is conceptually different from other types. It’s made of components such as smart contracts, nodes, blocks, consensus mechanisms, transactions, and wallets, which need to be thoroughly tested and require a relevant stack.

Benefits

No risk of unauthorized access, fraud, and data breaches thanks to robust security protocols.
Quick removal of roadblocks that could become a costly problem in the production environment.
Compliance with industry regulations and data protection laws, which can be particularly important for industries like finance and healthcare.
Confidence that a blockchain is thoroughly tested and safe to use.

More Organizations Will Leverage Shift-Left Testing

Many organizations are embracing Shift-Left Testing. This practice isn’t new, but it has evolved over time and shifted even further towards the left in the pipeline in 2024. Instead of waiting for code to be developed, now teams write unit tests before the coding phase begins.

Involving testers early in the development cycle offers some undeniable advantages. And one of the most important of them is cost reduction. By checking the validity of the code early on, teams can identify and fix bugs while the cost to fix them is still rather low, rather than letting them escalate to later stages, where it can reach $7,600 and more.

The Shift-Left approach also encourages the use of intelligent analytics. The Shift-Left approach also encourages the use of intelligent analytics. Testers can gauge customer satisfaction by monitoring their interactions with the software. If they find that the software needs some changes, they can introduce them during the initial stages of software development when they won’t cost much.

It’s worth noting that despite the benefits shift-left testing offers, it’s not always appropriate to involve testers early in the development process. The decision to involve QA testing teams should be made based on the state of a project.

“If a project is in its initial stages and the functionality is constantly changing, it wouldn’t make sense to start automation. The project has to reach a certain level of stability before it’s appropriate to automate.”
Taras Oleksyn, QA Lead

Benefits

Reduced development costs through early detection and correction of bugs and errors.
The possibility to automate test cases earlier and streamline the entire testing process.
Faster time to market thanks to the optimized QA process.

The Demand for Cloud-Based Cross Browser Testing Is Increasing
Among the growing trends in test automation adopted by organizations this year, cloud-based cross-browser testing stands out. As the variety of devices increases yearly, it’s become essential for companies to thoroughly test their solutions across all of them.

However, in practice, achieving such extensive coverage is often out of reach for many small companies because it is expensive to build such an extensive testing infrastructure. As a result, an increasing number of companies are turning to third-party providers that offer access to cloud technologies and thousands of virtual environments for testing.

Sure enough, with the emergence of cloud testing platforms, the market has also witnessed a rise in cloud-based testing tools. These tools provide support for all popular browsers and devices, enabling QA teams to create and run cross-compatible tests.

The global cloud application market’s value is expected to rise from $171 billion in 2020 to $365 billion by 2025.

Benefits

No need for costly in-house testing infrastructure, which means companies pay only for the resources they use.
An extensive testing coverage, including a wide range of devices, browsers, operating systems, and screen sizes.
The ability to scale testing resources up or down based on the project’s needs.
Support for parallel testing across most cloud-based platforms, which significantly reduces testing timelines and speeds time to market.

Exploratory Testing Will Become Inevitable

Exploratory testing has emerged as a practice that veers away from rigid test cases and scripts. Instead, it gives testers the freedom to explore and test software intuitively. This element of randomness not only allows QA teams to uncover unique use cases that haven’t been described by scripted testing but also find issues in areas where they wouldn’t typically look.

Benefits

Eliminating the need to document test cases or features speeds up testing.
The ability to catch issues and bugs that other testing methods and techniques might miss.
Quick start due to the lack of extensive test case preparation. This is especially important in situations where there’s limited documentation or time for test case creation.

Microservices Testing Rapidly Evolves

The popularity of microservices architecture has given rise to microservices testing. This testing approach is aimed at testing the software as a suite of small, individual functional pieces rather than the entire architecture and monitoring closely the ongoing performance.

Seeing how microservice-based applications rapidly appear on the market, microservices testing will continue to evolve, and the skill of testing microservices will be in high demand.

Benefits:

The ability to test individual components and changes in one microservice without affecting others.
Faster development cycles due to the option to release and iterate right on microservices.
Faster project delivery thanks to the ability to work on multiple microservices at the same time.
Optimized usage of resources, which leads to cost savings.

In-Sprint Automation Is Expected to Grow

According to Marketsplash, 71% of organizations are adopting agile methodologies, and more companies are considering embracing agile in the coming years. This trend has driven the growth of in-spirit automation.

In-sprint automation refers to the integration of test automation efforts within each sprint or iteration of the agile development process. By following this approach, companies can significantly speed up their release cycles, consolidating all the fundamental functions of testing in short increments.

Benefits:

In-sprint automation, which allows for faster and more frequent releases.
Automated tests run continuously during development, enabling early detection of defects and issues.
Real-time feedback on the quality of the software, which allows for more accurate project planning, better resource allocation, and improved forecasting of project timelines.
Automated tests created during one sprint can be reused in subsequent sprints for regression testing.
Quick validation of new requirements and ability to tailor the testing approach accordingly.

Integration of Crowdsourced Testing

Crowdsourced testing is a progressive approach to software QA that engages a diverse community of testers, often from around the world, to test products under real-world conditions on real devices.

The great thing about crowdsourcing is that it allows companies to avoid resource constraints. They don’t have to worry about whether the tester has the right test automation tools or skills. Instead, tasks are distributed according to the resources that the tester already has, which greatly speeds up the time to market.

Crowdsourced testing is often used to accelerate automation, in particular when the company is on the brink of a product release and/or is looking to extend its reach to global markets. In the future, it will be used more extensively as companies realize the positive impact of involving end users in the testing process.

Benefits

The ability to tap a diverse testing experience.
An extended test coverage comprising an array of devices, operating systems, browsers, and resolutions.
An easy way to scale up or down testing capacity depending on the project’s needs.
Involving end users in the process contributes to the delivery of a well-received product.

Scriptless Test Automation

This testing practice implies using automation testing tools to evaluate software quality without traditional scripts or code.

The concept is simple. The tools record the actions that testers take while navigating through the software and then, based on the results, generate the most likely use cases for different scenarios. Scriptless test automation platforms are designed to perform all types of testing, including UI/UX testing, functional testing, etc., making them suitable for many different projects.

However, similar to other tools, scriptless test automation platforms have limitations when it comes to customization. While this limitation may not be an issue for most projects with straightforward requirements, it can pose constraints for highly complex applications. In such cases, go for traditional script-based automation.

Benefits

Speeding up the product delivery process.
Higher ROI due to reduced automation costs.
Flexibility in reusing automation scripts in various scenarios.

Codeless automation goes mainstream

One of the other test automation trends that goes mainstream in 2024 is codeless automation. Just as the name suggests, this type of testing involves no coding, making it accessible to a wide range of professionals regardless of their technical background. QA engineers, business analysts, or even non-technical team members can create and run automated tests without needing to write code. Aside from that, codeless automation reduces the time needed for the test creation, making the process of writing test scripts less time-taking and laborious, ultimately speeding the development cycle.

Although not without limitations, especially when it comes to handling complex or highly customized applications requiring specific testing scenarios, codeless automation is proving to be a game-changer for projects with simple workflows. No wonder low-code and no-code platforms are gaining traction so rapidly. In fact, its market is expected to bloom by 2027 with an estimated value of around $65 billion globally, showing the growing demand for simpler automation solutions.

Benefits

Expands test automation capabilities to non-technical team members.
Reduces the time and effort required to create and maintain test scripts.
Speeds up the development and release cycles, supporting faster delivery of high-quality products.
Lowers dependency on highly specialized automation engineers, allowing teams to be more self-sufficient.

Continuous Testing Will Simplify Build Releases

Continuous testing (CT) helps businesses to evaluate risks associated with software launch, ensuring informed decisions about whether to proceed or make adjustments. CT is performed after each product change and can be integrated into the CI/CD pipeline.

As businesses increasingly recognize the benefits of CT, its demand and adoption are expected to continue growing in the software testing industry. According to Report and Data, 21% of QA testers have already incorporated CT into their processes to accelerate code releases, while the rest are keen on doing so in the near future.

Benefits

Early detection of defects and issues in the software development life cycle, which helps reduce the cost and effort required to fix them.
Adaptability to various development methodologies, including Agile, DevOps, and Waterfall.
Fast delivery of high quality software.
Ability to access testing reports at any point.

Mobile Automation Comes at the Frontier

The recent surge in mobile production has brought about the importance of mobile test automation.

In 2024 and the upcoming years, as the number of devices continues to grow, mobile app testing will become even more widespread. Companies will exponentially invest in robust mobile automation tools to stay competitive and will be looking for testers with relevant experience.

Benefits

Reliable app functioning in all parts of the world.
Faster deployment times due to streamlined testing activities.
Meticulous functioning of the app, including its UI and UX.
100% test coverage.

Quantum computing emerges as a game-changer

One of the latest trends in automation testing is the integration of quantum computing. While still in its early stages, quantum computing can enable computational capabilities far beyond those of traditional systems, allowing it to process massive datasets in parallel. It’s already been implemented in banking to help with fraud and risk management, but the potential applications extend much further.

In testing, the emergence of quantum computing opens up a new niche that demands new specialized skills and expertise. To keep up with the pace, QA engineers will need to develop a deep understanding of quantum algorithms, qubit operations, and quantum data processing. This automation testing trend is expected to lead to the creation of entirely new testing frameworks designed specifically for quantum-powered systems.

Benefits

Ability to test complex systems more quickly and efficiently than traditional methods.
Ability to perform highly complex calculations with greater precision.
Simulation of complex test scenarios that traditional systems struggle with, such as multi-variable simulations or optimization problems.

The Driving Force Behind Test Automation Trends

As you can see, there are a lot of things going on in the market, and while not all trends are long-lasting, some may be too costly to miss. Therefore, it’s important to stay abreast of the latest trends in order not to miss out on opportunities that can amp up your testing practices.

Now, the question is: what is driving the change in test automation? And is there a way to predict the trends that are looming on the horizon? Well, you can definitely anticipate certain trends if you keep up with the shifts in the technology world.

Here are some of the things that have driven transformation in recent years and are going to have a big impact on the testing landscape in 2024 and beyond.

Technological advancements. The ongoing evolution of technology, such as AI and machine learning, is a major driver. These technologies enable smarter and more efficient testing practices.
Changing user expectations. As users demand more seamless and user-friendly software experiences, testing practices always evolve to ensure software meets these expectations.
Agile and DevOps adoption. The widespread adoption of Agile and DevOps methodologies has not gone unnoticed in the testing industries. With the rising number of teams embracing agile and combining dev and operational processes, CT and integration have become the new norm.
Security concerns. As the amount of data exchanged over the Internet grows rapidly, security has become paramount. Testing practices address these concerns by focusing more on identifying vulnerabilities, ensuring robust security measures, and safeguarding sensitive data.
Market dynamics. Of course, competition and market demands play a crucial role in driving trends as well. Products need to be high quality and delivered quickly, which pushes organizations to adopt and adapt to the latest testing practices.
Evolving software architecture. Changes in software architecture, such as microservices and cloud-based solutions, require new testing approaches to ensure compatibility and reliability.
Remote work. The rise of remote work has a direct impact on collaboration, testing environments, and tools used in testing.

These driving forces, along with emerging technologies and methodologies, shape the future of test automation. By keeping a close eye on these factors, organizations can anticipate upcoming trends and make informed decisions to strengthen their testing practices.

Conclusion

As we’ve covered so many test automation trends, you may be wondering if all of them are going to still be relevant beyond 2024. The answer is — not necessarily, although they are all very popular these days. Looking ahead, we can make an assumption that scriptless and codeless automation testing won’t last too long due to their restrictions. At the same time, trends like AL, ML, RPA, and blockchain will gain more strength as websites and applications become more sophisticated. What’s also definite is that software test automation will not go anywhere anytime soon. It will become a big thing, and the sooner you adapt and start using the technology — the better.

POS Testing in Retail: How to Test Point of Sale Systems

TestFort — Thu, 20 Feb 2025 18:14:04 +0000

A seemingly small but truly indispensable POS system is at the heart of any retail or hospitality business. POS terminals don’t just handle transactions — they are a vital element of the sale and contribute greatly to the customer’s shopping experience. However, with the growing complexity of POS solutions, which typically combine hardware, software, and third-party integrations, Point of Sale testing becomes the only viable way to ensure the stability, security, and spotless functionality of the system.

POS application testing is a comprehensive process that concerns every component of the solution, from the user interface and the ability to handle an increased load to compliance with local regulations and the system’s impenetrable security. POS hardware and app testing is what can take your solution to the next level and help you win the trust of customers. Find out how to test a POS terminal, what to focus on within the POS solution, which testing challenges you can encounter along the way, and what role POS testing plays in the contemporary retail landscape.

Key Takeaways

A POS, or Point of Sale, system is a hardware and software unit that is used to manage sales transactions, as well as employed for other retail functions, such as inventory management and analytics.
Point of Sale terminals are complex systems with a sophisticated architecture. This may include the client-side layer, the application layer, the server-side layer, the networking and communication layer, as well as connected cloud services.
Testing POS solutions is the only way to ensure their complete functionality, user-friendliness, uninterrupted performance, compliance with local regulations, and security of all components.
POS testing has numerous additional benefits for the business, including better system reliability, enhanced customer experience, seamless integrations across all platforms, higher operational efficiency, improved scalability, and better decision-making.
Some of the types of testing commonly used to evaluate POS systems include functional, performance, usability, integration, compatibility, security, localization, and regression testing. Offline testing is also crucial for the overall quality of the system.
In addition to the software part of the POS system, there are also physical components that also need to be tested. These include the POS terminal itself, the card reader, the receipt printer, the cash drawer, the customer-facing display, and the network components.
In the process of testing POS solutions, teams can encounter certain challenges, including the growing complexity of hardware and software integrations, increasing security threats, cross-platform compatibility, and testing under real-world conditions.
Key trends in POS software testing include paying more attention to security and compliance, implementation of the shift-left approach to testing, reliance on exploratory testing for better usability, and increasingly common use of AI and machine learning.

What Is a POS System?

A POS, or Point of Sale, system is a combination of hardware and software used to manage sales transactions in retail, hospitality, and other customer-facing businesses. It serves as the central hub where customers complete purchases, typically at a checkout counter, kiosk, or online.

POS solutions are essential in the modern retail environment, offering much more than basic transaction processing. POS systems are used across the entire retail and hospitality landscape to streamline operations, enhance customer experiences, and provide actionable business insights.

Considering the important role of POS in the operation of a retail business and how much is riding on the usability, stability, and security of a POS system, it’s not really a question of whether you should perform POS testing — it’s a question of designing a strategy for comprehensive POS testing that covers every way your business and your customers use POS systems.

POS System Architecture

The architecture of a POS solution refers to the components included in the system and the way they interact with each other. Here is the typical architecture of a Point of Sale solution.

1. Client-Side Layer
This is the interface used by cashiers, sales staff, and customers to interact with the POS system. It primarily consists of hardware devices and the client-side application. The client-side, or front-end, layer enables the execution of sales transactions, order management, and payment processing. It communicates with the back-end systems to retrieve product details, update inventory, and complete sales operations. This POS part usually includes the following components:

POS terminal or device (tablet, desktop, mobile, or dedicated terminal)
Input devices (barcode scanner, card reader, PIN pad, touchscreen, or keyboard)
Output devices (receipt printer, customer display, and monitors)
Peripheral devices (cash drawer, digital scale, and self-service kiosks)

2. Application Layer
This layer contains the software that powers the POS system, typically consisting of business logic and workflows. This layer manages core functions, including item scanning, pricing, discounts, tax calculations, payment processing, order management, and receipt generation. For cloud-based systems, the application layer may be split between the client and the cloud server. These are the typical components of the application layer of a POS device:

POS software installed on the terminal or accessed via a web browser (for cloud-based systems).
Middleware to manage communication between front-end devices and back-end systems.

3. Server-Side Layer
The back-end, or server-side, layer handles the heavy lifting of data management and business operations, serving as the foundation of the POS system. This layer manages data synchronization between all client devices, maintains records of transactions, and ensures business logic is executed correctly. It also facilitates integrations with external systems and provides analytics and reporting capabilities. Usually, the server-side layer contains the following components:

Centralized database (on-premises or in the cloud).
Inventory management system.
Customer relationship management (CRM) system.
Accounting and financial reporting tools.
APIs for integration with third-party systems (ERP, payment gateways, loyalty programs).

4. Networking and Communication Layer
The networking layer connects the various components of the POS system, whether they are on-premises or cloud-based. This layer enables communication between terminals and the back-end server or cloud, ensuring smooth data transfer. It also supports integrations with online services, such as payment gateways and eCommerce platforms. These are the components typically included in this layer:

Local Area Network (LAN) for on-premises systems.
Internet connectivity for cloud-based systems.
Routers, switches, and network cables for communication infrastructure.

5. Cloud Services
For cloud-based and hybrid POS systems, cloud services enable centralized data management, multi-location support, and access to real-time insights from any device with internet connectivity. Cloud-based POS systems leverage cloud computing for data storage, processing, and software deployment. These are the components usually found in this part:

Cloud servers for storing transaction data, inventory, and customer information.
Web-based dashboards for real-time analytics and reporting.
APIs for integration with cloud-based third-party tools.

What Is POS Testing?

POS testing is the process of verifying the functionality, reliability, and performance of a POS system to ensure it meets business and user requirements. This type of testing focuses on both software and hardware, covering areas like transaction processing, payment integration, and peripheral device interactions.

The goal here is to identify and resolve defects that could disrupt operations or impact the customer experience. Unlike traditional software testing, Point of Sale software testing includes unique challenges such as hardware compatibility and environmental factors. It also requires validating how the system performs under real-world conditions, like high-traffic periods or network outages.

Why Is It Important to Test POS Software and Hardware?

Testing POS systems is not just an important part of retail software testing efforts — it’s one of the most essential stages of testing retail solutions. A POS transaction may only take seconds, but its effect on the overall shopping process cannot be overrated. Here is why testing POS hardware and software is so integral for your business success.

1. Improved System Reliability
POS software testing ensures the system operates reliably under real-world conditions, reducing the risk of downtime or errors during critical business operations. By identifying and addressing bugs, compatibility issues, and performance bottlenecks, testing minimizes transaction failures, ensuring smooth operations and better customer satisfaction. Reliable systems also help staff work more efficiently, reducing frustration and errors.

2. Enhanced Customer Experience
A well-tested POS system provides faster transactions, accurate billing, and secure payments, contributing to a positive customer experience. Testing ensures features like discounts, loyalty programs, and payment methods function correctly, making transactions seamless and increasing customer trust and satisfaction.

3. Data Security and Compliance
POS testing helps ensure compliance with security standards like PCI-DSS (Payment Card Industry Data Security Standard), protecting sensitive customer data such as payment information. It identifies vulnerabilities in data encryption, authentication, and storage mechanisms, reducing the risk of fraud or breaches. Compliance also enhances customer confidence and reduces potential legal and financial penalties.

4. Seamless Integration with Other Systems
Testing ensures the POS system integrates smoothly with other business systems, such as inventory management, accounting, and CRM. Proper integration reduces operational burden, streamlines workflows, and ensures that data flows correctly between systems, improving overall business efficiency and accuracy in reporting.

5. Higher Operational Efficiency
Point of Sale software testing optimizes performance by identifying and resolving issues that could slow down operations, such as transaction delays or device malfunctions. It also ensures that features like offline mode work correctly, allowing businesses to maintain functionality even during network disruptions. This leads to better resource utilization and smoother operations.

6. Scalability and Future Readiness
Testing prepares the POS system to handle increased loads as the business grows. By assessing scalability, testing ensures the system can support more users, transactions, or integrated components without compromising performance. It also evaluates compatibility with future software or hardware upgrades, ensuring longevity.

7. Reduced Costs from Failures
By identifying and fixing issues during the development or deployment phase, testing reduces the likelihood of costly failures in production. It minimizes risks of transaction errors, device downtime, and customer dissatisfaction, saving money in repairs, lost sales, and reputation management.

8. Better Decision-Making
Testing ensures the accuracy of data collected by the POS system, such as sales reports, inventory levels, and customer insights. Accurate data allows businesses to make informed decisions about pricing, inventory, staffing, and marketing strategies, ultimately improving operational outcomes and profitability.

Types of Testing for Retail POS Solutions

POS application testing stands out from other testing tasks for many reasons: mainly because, unlike most products being tested, a POS system is a combination of hardware and software, which needs to be taken into account when designing a test strategy. At the same time, a POS system’s performance, security, and compatibility can be tested using a variety of well-familiar types of testing and quality assurance. These are the types of POS testing teams use most often for comprehensive testing.

1. Functional Testing
Functional testing ensures that all POS system features and workflows perform as expected. This includes verifying core functionalities like item scanning, pricing, discount application, payment processing, and receipt generation. It also covers peripheral device interactions, such as barcode scanning, card swiping, and printing. The goal is to validate the system’s accuracy in handling transactions and other business operations.

“Making sure a POS system performs its functions is only a first step. Next one is to verify it is protected from errors. Even rare user scenarios that might result in failures shouldn’t be overlooked: if the daily numbers of users interacting with your POS terminal hits thousands, such failures are very likely to occur, sooner or later.”
Michael Tomara, QA Lead, TestFort

2. Integration Testing
Integration testing examines how different components of the POS system work together. This includes ensuring seamless communication between the POS terminal, peripheral devices, payment gateways, inventory systems, and other third-party integrations like accounting or CRM software. Testing identifies issues with data exchange, such as synchronization errors, incorrect API calls, or misaligned workflows between integrated systems.

3. Performance Testing
Performance testing evaluates the POS system’s response time, stability, and scalability under varying loads. It simulates real-world scenarios, such as high transaction volumes during peak hours, to ensure the system maintains performance standards. This type of testing identifies bottlenecks that could slow down transactions or impact the user experience.

4. Security Testing
Security testing ensures the POS system is resistant to cyber threats and complies with regulations like PCI-DSS. It evaluates the system’s encryption protocols, authentication mechanisms, and data storage practices. These tests simulate potential security breaches, such as unauthorized access or network vulnerabilities, to identify weaknesses and ensure customer data is protected.

5. Usability Testing
Usability testing focuses on the system’s ease of use for cashiers, managers, and customers. It makes sure the POS interface is intuitive and friendly, evaluating the layout, navigation, and overall design to ensure the POS is usable and efficient. This testing activity helps optimize workflows, reduce training time, and improve the end-user experience.

6. Compatibility Testing
Compatibility testing ensures that the POS system works effectively across different hardware setups, operating systems, and peripheral devices. It also validates compatibility with various payment methods, including credit cards, debit cards, and contactless payment technologies. Testing ensures the system can adapt to diverse environments and configurations.

7. Load and Stress Testing
Load testing evaluates how the POS system performs under expected transaction volumes, while stress testing pushes the system beyond its normal limits to identify breaking points. These tests ensure the system can handle surges in activity, such as holiday sales or large-scale events, without crashing or slowing down.

8. End-to-End Testing
End-to-end testing simulates complete workflows, from adding items to the cart to processing payments and updating inventory. It validates the entire transaction lifecycle to ensure all components work together seamlessly. This type of testing is essential for identifying gaps or inconsistencies in the overall system.

9. Localization Testing
Localization testing ensures the POS system accommodates regional requirements, such as local currencies, tax regulations, languages, and date formats. It validates that the system is adaptable to different geographic and cultural contexts, ensuring compliance and usability across global markets.

10. User Acceptance Testing
User acceptance testing ensures the POS system meets business requirements and the software is ready for deployment. Real users, such as store staff or managers, test the system in a simulated or live environment. UAT validates that the system aligns with operational needs and expectations.

11. Regression Testing
Regression testing of a POS software application verifies that new updates, bug fixes, or feature additions do not disrupt existing functionalities. It involves re-running test cases on previously tested components to ensure no unintended issues arise due to changes in the system. This testing is critical for maintaining system stability over time.

12. Offline Testing
Offline testing of retail POS systems verifies the proper functionality during network outages. It ensures the system can process transactions, record sales, and manage inventory locally. Once connectivity is restored, testing ensures proper synchronization of offline data with the central database.

Physical Components of a POS Terminal

One of the ways Point of Sale testing is different from other testing projects is that, unlike traditional software solutions, a POS has a physical side as well, and a very significant one at that. POS system testing is as much about the software as it is about the physical components. Here are the key physical parts of a POS and what you should know about testing them.

1. POS Terminal or Computer
The terminal is the central hub of a POS system that is used to process transactions. This can be a dedicated POS terminal, a desktop computer, tablet, or smartphone. Testing a terminal involves ensuring compatibility with connected parts like barcode scanners, receipt printers, and cash drawers.

There is also performance testing, which focuses on speed and responsiveness under various loads, while compatibility testing ensures the POS software runs smoothly across different operating systems. Finally, stress testing simulates heavy usage scenarios to identify any potential failures.

2. Card Reader
The card reader, represented by EMV, magnetic stripe, or contactless payment element, processes payments via credit or debit cards, including chip-based and contactless methods. This stage of testing ensures compatibility with all types of cards, from magnetic stripe to NFC payments, and involves checking security features such as encryption and compliance with PCI-DSS standards.

Subsequently, transaction accuracy testing verifies that payment amounts are processed correctly without double charges, while fault tolerance testing ensures the system handles card reading errors effectively.

3. Barcode Scanner
The barcode scanner inputs product details into the system by reading barcodes. Testing ensures the scanner accurately recognizes barcodes, even those that are damaged or partially visible. It must be compatible with the POS software and database to fetch correct product information. Efficiency tests assess the scanner’s speed during repeated use, and additional tests examine its performance under varied lighting conditions.

4. Receipt Printer
The receipt printer generates transaction receipts for customers and business records. Testing of the printer aims at assessing print quality under different conditions, such as low ink or toner levels, and verifying printing speed during peak transaction times. Connectivity testing ensures a stable connection between the printer and POS system, while error-handling tests simulate issues like paper jams or low ink levels to ensure the system provides appropriate alerts.

5. Cash Drawer
The cash drawer securely stores cash and operates in sync with the POS system. Testing involves verifying that the drawer opens only during authorized transactions and ensuring it remains locked otherwise. Security tests include simulations of unauthorized access attempts. Durability is assessed by testing repeated opening and closing cycles, while power tests confirm reliable operation in both electric and manual configurations.

6. Customer-Facing Display
The customer display shows transaction details, such as item prices and totals, for customer to review. Testing ensures the display is easily readable under various lighting conditions and updates transaction details in real-time. Additional tests verify the use of correct language settings, character encoding, and currency symbols for localized setups.

7. Network Components
Network components — router, modem, and cables — provide internet connectivity for cloud-based POS systems and system integrations. Testing helps ensure stable connectivity and the system’s ability to recover from network interruptions. Speed and latency tests confirm the system performs well during data-intensive operations. Offline functionality is tested by simulating network outages to verify the POS system’s ability to operate offline and sync data once the connection is restored. Security testing identifies vulnerabilities in network connections.

8. Peripheral Devices
Peripheral devices, such as digital scales and PIN pads, add specialized functionality to a POS system. Testing ensures scales measure weights accurately and keypads register input reliably. Integration testing checks whether these devices communicate seamlessly with the POS terminal. Durability testing simulates heavy usage over time to ensure consistent performance.

What to Focus on in Point of Sale Software Testing? POS Test Case Examples

Every POS application testing project is different because POS terminals and their software themselves are different. This is why every POS system testing project requires a unique test plan and strategy, as well as a selection of test cases based on the specifics of the system. At the same time, there are certain focus areas that are present in nearly all Point of Sale testing projects. Here are the sample cases you can use to test the POS system for your retail business.

1. Functional Test Cases

Verify that the system processes transactions for various payment methods (cash, credit card, debit card, NFC).
Confirm the correct application of discounts, coupons, and promotional codes during checkout.
Test the addition and removal of items from the cart.
Validate that the system calculates taxes correctly based on location and applicable rates.
Check if receipts are generated with accurate details, including transaction ID, items, quantities, prices, discounts, taxes, and totals.
Test for proper handling of item returns, refunds, and exchanges.
Verify that the system allows manual price overrides by authorized personnel.

2. Integration Test Cases

Ensure the POS system synchronizes inventory data with the central inventory management system in real-time.
Verify integration with accounting software for proper financial reporting.
Test the seamless connection between the POS terminal and peripheral devices (barcode scanner, receipt printer, card reader).
Check API integrations for payment gateways to process transactions securely.
Validate the system’s ability to communicate with loyalty or rewards programs.

3. Performance and Load Test Cases

Test system performance during high transaction volumes, such as holiday sales periods.
Assess response times for processing transactions with multiple items in the cart.
Simulate concurrent usage of multiple terminals and evaluate overall system stability.
Test the system’s performance when executing resource-intensive operations, such as generating reports or large-scale inventory updates.

4. Security Test Cases

Verify compliance with PCI-DSS standards for card payment processing.
Test for proper encryption of sensitive data during transmission and storage.
Simulate unauthorized access attempts and validate the system’s ability to deny access.
Check whether users are automatically logged out after a specified period of inactivity.
Test for vulnerabilities in network communications between the terminal and the server.

5. Usability Test Cases

Validate that the user interface is intuitive and easy to navigate for cashiers and managers.
Test the readability of on-screen text and proper alignment of elements under different screen resolutions.
Observe the ease of performing frequent tasks, such as voiding items, applying discounts, and closing the register.
Gather feedback from users to identify pain points or inefficiencies in the workflow.

6. Compatibility Test Cases

Confirm that the system operates correctly across different POS terminals (tablets, desktops, dedicated devices).
Test for compatibility with various operating systems (Windows, Android, iOS).
Verify the functionality of peripheral devices across multiple hardware setups.
Assess performance under different network conditions (wired, Wi-Fi, 4G/5G).

7. Regression Test Cases

Verify that new software updates or patches do not disrupt core functionalities, such as transaction processing or report generation.
Retest workflows that previously contained bugs to ensure they are resolved.
Test backward compatibility with older peripheral device models after a system update.

8. End-to-End Test Cases

Simulate a customer shopping experience from adding items to the cart to completing a payment and receiving a receipt.
Test the end-to-end process for item returns and refunds, including stock updates and financial reporting.
Validate the entire workflow for generating and exporting end-of-day financial reports.

9. Offline and Data Synchronization Test Cases

Test the system’s ability to process transactions while offline.
Validate data caching during offline mode and confirm that it synchronizes accurately with the central database once connectivity is restored.
Check the system’s handling of offline payments, ensuring no duplicate transactions occur after syncing.

10. Localization and Compliance Test Cases

Verify that the system displays regional currencies, date formats, and tax calculations correctly.
Ensure compliance with local regulations for receipt content and record-keeping.
Test the system’s language settings for multilingual support.

“Don’t forget about negative testing! This includes edge cases too. How will your POS terminal react to a bank card that expires today? And do you have a clear requirement whether the system should accept such cards? This is when QA engineers should try to not only ‘break’ the POS system, but to test the consistency of its requirements as well.”
Michael Tomara, QA Lead, TestFort

How to Do Point of Sale Testing: Step by Step Guide

Testing a POS system involves a comprehensive approach to ensure it operates reliably, securely, and efficiently. The process includes functional and non-functional testing, covering both hardware and software components. Here are the steps required to do Point of Sale testing correctly.

1. Define Testing Objectives
Clearly outline the goals of testing, such as verifying core functionalities, ensuring data security, or evaluating system performance under peak loads. Identify key business scenarios and prioritize testing critical workflows like transactions, payments, and inventory updates.

2. Prepare a Test Environment
Set up a testing environment that mirrors the production setup. Include hardware components like POS terminals, barcode scanners, receipt printers, card readers, and cash drawers. Connect the system to backend systems such as inventory management, CRM, and payment gateways, ensuring all integrations are operational.

3. Perform Functional Testing
The functionality of the POS solution is what allows it to serve customers and the business in the first place, so testing it is integral for a successful release. Here, you will need to validate core features such as:

Scanning and adding items to a cart.
Applying discounts, taxes, and promotions.
Processing payments across various methods (cash, card, digital wallets).

4. Conduct Hardware Testing
Test interactions between the POS software and hardware peripherals. For example, verify that barcode scanners correctly scan items, receipt printers print legibly, and card readers process payments without errors. Earlier in this article, we have listed the key hardware components of POS terminals and their testing specifics, so make sure to check those.

“Another important aspect is testing system updates. This is about maintaining numerous devices, so you may want to ensure that updating is smooth and that patches can be rolled back whenever needed. Sometimes it might be necessary to make limited updates for specific terminals — then, it is crucial to double-check these limited updates don’t break the entire system.”
Michael Tomara, QA Lead, TestFort

5. Execute Integration Testing
Ensure the POS system integrates seamlessly with other business systems. Test the flow of data between the POS, inventory management, payment gateways, and accounting systems. Verify data consistency and accuracy, such as inventory updates after a sale.

6. Conduct Performance Testing
Evaluate how the POS system handles expected and peak transaction loads. Simulate high-traffic scenarios to test response times and stability. Measure system performance during offline mode and validate synchronization upon reconnecting to the network.

7. Perform Security Testing
Ensure the POS system complies with security standards like PCI-DSS to protect sensitive payment and customer data. Identify vulnerabilities in data encryption, authentication mechanisms, and network security to prevent unauthorized access and breaches.

8. Test the Usability
Evaluate the system’s user interface for ease of use. Test workflows to ensure they are intuitive for cashiers and managers. Identify areas where training may be required or where the interface could be optimized.

9. Perform Regression Testing
Whenever updates or fixes are applied, run regression tests to ensure that new changes do not disrupt existing functionalities. Re-test critical workflows to verify system stability.

10. Conduct End-to-End Testing
Simulate complete workflows, from scanning an item to finalizing a payment and updating inventory. This validates the entire transaction lifecycle and ensures all components of the POS system work together seamlessly.

11. Test for Localization
For systems deployed in different regions, verify that they support local currencies, tax regulations, languages, and date formats. Ensure the system complies with regional standards and compliance requirements, as failure to do so can result in financial and reputational losses, as well as legal trouble.

12. Perform User Acceptance Testing
Involve end-users such as store staff or managers to test the system in a simulated or real environment. Gather feedback on usability, functionality, and performance to ensure the system meets business needs.

POS Test Automation: How and Why to Automate Retail POS Testing

Automated testing of POS solutions can often seem like a daunting task to testing teams, given how integral hardware is to POS operation and how difficult it can be to automate testing when it involves hardware. However, POS test automation brings a range of significant benefits to the quality assurance process, and these are the ones to consider in the first place:

Improved efficiency and speed. Automation accelerates the testing process by running test cases faster than manual testing. This is especially beneficial for regression testing during frequent updates, allowing companies to release new features or fixes faster.
Enhanced accuracy. Automated testing eliminates human errors that may occur during manual testing. It ensures consistent execution of test cases, improving reliability and the ability to reproduce results.
Cost savings in the long term. Though automation has an initial setup cost, it reduces the time and resources required for repetitive tests over time. This results in significant cost savings, particularly for businesses with complex POS systems requiring frequent testing.
Scalability for large systems. Automation is ideal for businesses with multiple POS terminals, locations, or integrated systems. It allows testing at scale, ensuring all instances of the POS system work as intended.
Focus on complex testing. By automating repetitive tasks, testers can focus on exploratory testing, usability testing, and addressing edge cases that require human insight, improving overall quality assurance.

Now let’s look at how exactly you can implement automation in your POS software testing project:

Identify test cases for automation. Focus on repetitive, high-volume, and critical test cases for automation, such as functional tests, regression tests, performance tests, and API integration tests. Avoid automating scenarios involving physical hardware interaction unless specialized tools or simulators are available.
Select test automation tools and frameworks. Choose automation tools that support POS-specific testing requirements. Popular automated testing tools include Selenium for UI and web-based Point of Sale software testing, Appium for testing mobile-based POS applications, Postman or SoapUI for API testing, and LoadRunner or JMeter for performance testing. For hardware simulation and testing, you may consider developing custom tools and solutions.
Set up the test environment. Create a testing environment that mirrors the production setup. Typically, it includes POS terminals and peripherals (scanners, printers, and card readers), backend solutions (inventory management and payment gateways), and simulated or real networks for connectivity testing.
Develop automation scripts. Write scripts using the chosen tools to execute the identified test cases. Ensure the scripts are modular and reusable to be able to quickly adapt to changes in the POS system.
Integrate with CI/CD pipelines. Integrate automation into Continuous Integration/Continuous Deployment pipelines. This ensures tests are automatically triggered during software builds, enabling faster identification of issues.
Validate and maintain automation scripts. Regularly review and update scripts to align with software changes, new features, or updates to testing requirements. Proper maintenance ensures the effectiveness of the automation suite.

Our Experience with POS System Testing

As a testing company with over two decades of experience, testing POS solutions is something we have hands-on experience with. One of our recent projects was testing POS hardware and a mobile application that belonged to the same POS system for a European mobile banking service provider. Preparing to launch the business internationally, the company contacted us to perform comprehensive testing of its mobile POS solution.

To make sure the product was ready for launch, we developed an all-encompassing testing strategy focusing on every component of the solution, including its usability, stability, compatibility with hardware, etc. We had to set up the testing process from scratch and get a deep understanding of the product’s inner workings before we could move on to testing.

The scope of QA activities performed on the project included functional testing, UI/UX testing, compatibility testing on 12 different devices, as well as smoke and regression testing after every update. Our team also prepared extensive documentation to help onboard new users and make the testing process more effective and transparent. Throughout our cooperation, we helped our client dramatically improve their software quality and release the solution with confidence. Find out more about the project below.

Challenges in POS Testing

POS software testing is a resource-intensive task that can have a critical amount of impact on the normal operation of a retail store or establishment. Even with the most careful planning, testing a POS solution can encounter certain challenges. Those challenges can be both similar to conventional software testing and completely unique due to the one-of-a-kind nature of the POS system. Here are the most common challenges of testing POS solutions.

1. Complex Hardware-Software Integration
POS systems are usually a combination of various hardware components (card readers, barcode scanners, receipt printers, etc.) and software that must work together seamlessly despite the endless number of possible hardware and software configurations. Ensuring compatibility and smooth communication between hardware components and software requires extensive testing, especially as new hardware or software versions are released.

2. Ensuring Security and Compliance
POS systems handle sensitive customer information, making them prime targets for security threats. Compliance with standards like PCI-DSS is mandatory, but maintaining security while meeting these standards can be difficult. Testing must thoroughly cover all security aspects, including encryption, data storage, and data transmission, to protect against breaches and ensure compliance, adding time and cost to the testing process.

3. Testing Under Real-World Conditions
POS systems often perform under diverse conditions, including varied transaction volumes, network reliability, and user interactions. Simulating these real-world environments in a testing lab can be difficult and costly, yet it is crucial to identify potential issues that could affect performance, reliability, and user experience.

4. Omni-Channel Integration Testing
Modern POS systems must integrate with other channels (e.g., online stores, mobile apps) to provide a unified customer experience. Ensuring that POS systems can handle cross-channel transactions and interact effectively with other systems requires comprehensive integration testing, which can be complex and time-intensive.

5. Cross-Platform Compatibility
POS systems often need to work across various devices, including desktop terminals, tablets, and mobile phones. Testing for compatibility across multiple platforms and OS versions requires significant resources to cover all possible device and environment combinations, making this a resource-intensive process.

6. Frequent Software and Hardware Updates
POS solutions are updated frequently to add new features, fix bugs, or improve security, especially as technology and customer expectations evolve. Every update needs to be tested for compatibility with existing hardware, other software modules, and configurations. Continuous regression testing is needed to ensure these updates do not introduce new issues, adding to testing demands.

7. High Dependency on Network Reliability
POS systems often rely on stable network connections for real-time processing and data syncing. Unstable or unreliable network conditions can affect POS system performance. Testing must account for varying network conditions to ensure smooth operations even during disruptions, which adds complexity to the testing scenarios.

8. Usability and User Experience Challenges
POS solutions are used by employees with varying levels of technical expertise, requiring the systems to be intuitive and easy to use. Ensuring a simple, efficient user interface across different transaction scenarios is essential, requiring usability testing with real users, which can be time-consuming and requires specialized testing approaches.

Key Trends in Testing Solutions for POS Systems

There are plenty of testing methods and types used to check the performance, security, and usability of POS solutions. At the same time, the industry is constantly moving forward. There are always new trends, technologies, and approaches to testing a POS application to look out for. Here are the key trends that will shape the Point of Sale testing industry of the upcoming years.

1. Emphasis on Security and Compliance Testing
As POS systems handle sensitive customer data, security testing is increasingly prioritized. Compliance with standards like PCI-DSS is mandatory for systems processing card transactions. Comprehensive security testing helps prevent breaches and ensures compliance, building customer trust and avoiding regulatory penalties.

2. Implementation of Shift-Left Testing
Shift-left testing has become more prominent, with testing activities moving earlier in the development lifecycle. Teams aim to detect and fix issues before they reach the later stages of development. By identifying and addressing issues early, shift-left testing reduces the costs associated with defect resolution and improves software quality, making it ideal for POS systems where downtime and malfunctions directly impact sales.

3. Exploratory Testing for Enhanced Usability
Exploratory testing is gaining popularity to identify usability issues, especially for retail environments where user experience is critical. This includes hands-on testing by QA specialists to uncover areas of improvement in real-world usage. Exploratory testing ensures that POS systems are intuitive and user-friendly, reducing training time for staff and improving transaction speed, which directly impacts customer satisfaction.

4. Integration Testing with Cloud and Mobile Systems
The rise of cloud-based POS solutions and mobile POS (mPOS) devices demands rigorous integration testing to ensure they work smoothly across various devices, software environments, and networks. Integration testing ensures seamless operation with other systems like inventory management, CRM, and ERP, especially as many businesses shift to cloud and mobile-enabled POS systems.

5. Use of AI and Machine Learning for Enhanced Test Coverage
AI and machine learning are being integrated into POS testing strategies, particularly for predictive analytics, anomaly detection, and identifying potential vulnerabilities. AI helps optimize test coverage by analyzing historical data to predict high-risk areas, providing deeper insights and streamlining testing efforts, particularly for large-scale POS implementations.

6. Increased Testing for Contactless Payments and Digital Wallets
Contactless payments and digital wallet integration have become standard in many reliable POS systems, especially following the demand for low-touch transactions. Testing for contactless payment compatibility ensures that POS systems can reliably handle these transactions, providing secure, fast, and convenient checkout experiences that meet customer expectations.

7. Data Analytics Testing for POS Insights
POS systems are increasingly incorporating analytics to provide business insights, tracking trends like customer purchasing behavior, popular products, and peak sales times. Ensuring the accuracy and performance of data analytics within POS systems allows businesses to make data-driven decisions and enhances the strategic value of POS systems beyond just transaction handling.

8. Adoption for Edge Computing in POS Systems
Edge computing is becoming more common, where data processing occurs closer to where it is generated — in most cases, directly on the POS device or nearby — instead of relying on cloud servers. Testing for edge computing in POS systems ensures reduced latency and enhanced reliability, which is especially valuable for offline capabilities, ensuring uninterrupted transactions even without an internet connection.

Final Thoughts

POS systems are more than just tools for processing transactions — they are integral to efficient operations, customer satisfaction, and business growth. As these systems grow more and more sophisticated, integrating diverse hardware, software, and payment solutions, the need for comprehensive testing becomes increasingly critical.

Testing addresses a myriad of issues, from hardware compatibility and software integrations to the way users interact with the solution and how it impacts the sales process. Whether you choose to do your testing in-house or use POS testing services from a reliable vendor, rigorous testing is the only way to ensure that your solution is ready to see the world.

AI in Software Testing: Wins and Risks of Artificial Intelligence in QA

TestFort — Mon, 08 Jul 2024 15:27:19 +0000

AI in QA is a topic you can cover once a week and still miss some novelties. A year ago, we released an article on what ChatGPT can do for software test automation, and it seemed like a big deal.

Now, AI for software testing is a separate business, tech, and expert territory, with serious players, loud failures, and, all in all, big promise.

Let’s talk about

Latest stats of AI QA testing (not everything is so bright there, by the way);
How AI is already used to improve software quality;
How AI and machine learning will/may be used to optimize testing;
How to use the power of AI in testing software and reduce risks along the way.

Just to take it off the chest, we have just partnered with Viruoso AI, a top-of-the-game company that uses AI automation testing tools. It means two things:

We are excited enough to mention it about 10 times in one article;
We write about AI testing services from experience. We use AI to automate tests, we incorporate AI in planning manual test roadmaps, and we know how exactly tools can help software testers in the upcoming 12-18 months. We don’t plan further; this AI market advancement is crazy.

Where Do We Stand with AI Software Testing in 2024

Numbers are forgettable and often boring without context.

But they help you to see the trends, especially when they come from reliable sources. When ISTBQ, Gartner, or British Department for Science, Innovation and Technology (DSIT) cover the impact and the future of software testing with AI — you take notice.

So we give just a few numbers summarized from few research results and surveys to help you realise one thing — traditional software testing industry is living its last years.

Industry Insights and Statistics

AI-driven testing can increase test coverage by up to 85%; Organizations using AI-driven testing reported 30% reduction in testing costs and 25% increase in testing efficiency;
By 2025, 50% of all new software development projects will include AI-powered testing tools;
47% of current AI users had no specific cyber security practices in place for AI (not everything is so shiny, right?).

Real-World Proved Benefits of AI Testing
Just a brief case to show how artificial intelligence testing tools can help at any stage of QA process. They are not required for testing, true. But maybe they already should be.

We worked with a company offering to create consoles for their clients. User interface testing is paramount for such companies, but not only that. When we entered the project, we realized there were problems with bug triage, test coverage, bug report creation, requirements testing, and report creation. Using AI in software testing was new to us, but we decided to try and never regretted it. Check the numbers.

Bug Triage

Problem. Duplicated issues and inefficiencies in assigning bugs due to multiple authors logging defects.
Solution. Implemented DeepTriage to automate and streamline the bug triage process.
Results. 80% decrease in analysis time and bug report creation.

Test Coverage

Problem. Limited documentation time, predominantly covering only positive scenarios.
Solution. Used ChatGPT to generate comprehensive test cases from requirements, ensuring better coverage.
Results. 80% faster test case creation and a 40% increase in edge case coverage.

Bug Reports Creation

Problem. Customer feedback needed conversion into a formal bug report format.
Solution. Used ChatGPT to analyze and structure customer reviews into detailed bug reports.
Results. 90% reduction in detectability and improved communication of issues.

Requirements Testing

Problem. Need for structured user stories and consistent software requirements.
Solution. Applied ChatGPT and Grammarly to analyze, restructure, and ensure consistency in software requirements.
Results. 500% reduction in requirement testing time and a 50% increase in spelling mistake corrections.

Report Creation

Problem. Time-consuming data integration from various sources during regression testing.
Solution. Utilized Microsoft Power BI for efficient data integration and AI-driven insights.
Results. 30% improvement in data representation and a 50% reduction in report creation time.

Our experience in implementing AI in software testing have skyrocketed since then, but it was a great start that allows us to truly believe in benefits of using AI in small and eneterprise-level projects.

https://testfort.com/wp-content/uploads/2023/04/2-AI-in-Software-Testing.png

How AI Can Be Used to Improve Software Testing

Even the best manual testers are limited by time and scope. AI is changing that. With machine learning and predictive analytics, AI enhances traditional manual testing processes. From test planning to execution, AI-driven tools bring precision and efficiency, making manual testing smarter and more effective.

Importantly, AI doesn’t eliminate the need for human testers; it helps them work more efficiently and focus on complex issues.

Test Planning and Design

Test case generation allows to analyze historical data and user stories to generate comprehensive test cases. AI is used to increase the overall coverage of the testing process (yes, large number of tests doesn’t necessarily means quality, but we still rely on human intelligence to filter trash out).

Risk-based testing relies on machine learning algorithms to prioritize test cases based on potential risk and impact.

Defect prediction is based on using AI and ML predictive models to identify areas of the application most likely to contain defects.

Test Execution and Management

Test data management will be easier with automating the creation and maintenance of test data sets using AI-driven tools.

Test environment optimization uses AI systems to manage and optimize test environments, ensuring they are representative of production.

Visual Testing is all about employing AI-powered visual validation tools (like Vision AI) to detect UI anomalies that human testers might miss.

Collaboration and Reporting

AI-powered reporting allows generation of detailed and actionable test reports with insights and recommendations using natural language processing.

Collaboration tools cover integrating AI with collaborative tools to streamline communication between testers, developers, and other stakeholders.

And now, to the most exciting part. End-to-end automated testing done right with AI-based test automation tools. It’s a mouthful, but it is exactly what you need to be thinking about it 2024.

Artificial Intelligence in Software Test Automation

Integrating AI into software testing helps get the most from automation testing frameworks. Right now, there is hardly an automated test scenario that cannot be somehow enhanced with tools for AI QA.

Self-Healing Scripts

Self-healing scripts use AI algorithms to automatically detect and adapt to changes in the application under test, reducing the need for manual script maintenance.

Dynamic element handling allows AI to recognize UI elements even if their attributes change, ensuring tests continue to run smoothly. As UI testing becomes essential to any minor and major launch, AI can assist immensely.

Intelligent Test Case Prioritization

Risk-based prioritization relies on AI to analyze code changes, recent defects, and user behavior to dynamically prioritize test cases.
Optimized testing ensures critical paths are tested first, improving overall test efficiency.

AI-Driven Regression Testing

Automated selection uses AI tools to automatically select relevant regression test cases based on code changes and historical test results.
Efficient execution speeds up the regression testing process, allowing for faster feedback and quicker releases.

Continuous Integration and Continuous Delivery (CI/CD)

Automated code analysis employs AI tools to perform static and dynamic code analysis, identifying potential issues early in the development cycle.

AI-powered deployment verification involves using AI to verify deployments by automatically executing relevant test cases and analyzing results.

Performance testing leverages AI to simulate user behavior and load conditions, identifying performance bottlenecks and scalability issues.

AI in Test Maintenance and Evolution

Adaptive test case generation uses AI to continuously generate and evolve test cases based on application usage data and user feedback.

Predictive maintenance applies machine learning to predict and address test script failures before they impact the CI/CD pipeline.

Automated test refactoring utilizes AI to refactor test scripts, ensuring they remain effective and efficient as the application evolves.

Continuous Testing

Seamless integration ensures AI integrates with CI/CD pipelines, enabling continuous testing and faster feedback.

Real-time insights provided by AI offer immediate feedback on testing results, helping teams make informed decisions quickly.

By incorporating AI into automated testing, teams can achieve higher efficiency, better test coverage, and faster time-to-market. AI-driven tools make automated testing smarter, more reliable, and more adaptable to the ever-changing software landscape.

As you can see AI in software testing takes many forms: generative AI for test scripts, natural language processing for, vision and even audio processing, machine learning, data science, etc. These are all mixed. The good news is that testing using artificial intelligence doesn't require you to have deep understanding of algorythms, and tech, and types of ML learning. You just need to choose the right AI testing tools... and not fall for the lies.

AI Tools: Optimize Testing but Don’t Believe Everything They Promise

We’ve been in the market for AI tools for over a year, searching for a partner that truly enhances our automated testing on both front and back ends. Many tools we encountered used AI as a buzzword without offering real value. It was frustrating to see flashy promises without substance.

Then we found Virtuoso AI. It stood out from the rest.

“With Virtuoso, our trained professionals create test suites effortlessly. These are structured logically, maintaining reusability and being user-centric. Once we establish a baseline, maintaining test suites becomes straightforward, even as new releases come in. Regression suites run quickly and efficiently.”

Bruce Mason, UK and Delivery Director

Key areas of Virtuoso’s AI product include

Codeless automation. We can set up tests just by describing what they need to do. No coding necessary, which means quicker setup and easier changes.

Functional UI and end-to-end testing. It covers everything from button clicks to complete user flows. This ensures your app works well in real-world scenarios, not just in theory.

AI and ML integration. AI learns from your tests. It gets smarter over time, improving test accuracy and reducing manual adjustments.

Cross-browser testing and API integration. With this tool we can test how your app works across different web browsers and integrates API checks. This means thorough testing in diverse environments – a must for consistent user experience.

Other AI Tools for Testing

Besides Virtuoso AI, here are a few other notable artificial intelligence software testing tools available on the market:

Applitools. Specializes in visual AI testing, offering tools for automated visual validation and visual UI testing.
Testim. Uses machine learning to speed up the creation, execution, and maintenance of automated tests.
Mabl. Provides an AI-driven testing platform that integrates with CI/CD pipelines, focusing on end-to-end testing.
Functionize: Combines natural language processing and machine learning to create and maintain test cases with minimal human intervention.
Sealights: Focuses on quality analytics and continuous testing, offering insights into test coverage and potential risk areas.

When evaluating these tools and testing activities they cover, remember to check their true AI capabilities, scalability, integration, and support systems to ensure they meet your needs.

But let’s not ignore the broader market. There are many AI tools available, each with its own strengths and weaknesses. Here’s what to consider when evaluating them:

True AI capabilities. Look beyond the buzzwords. Ensure the tool offers genuine AI-driven features, not just automated scripts rebranded as AI.
Scalability. Can the tool handle large-scale projects? It should adapt to your growing needs without performance issues.
Integration. Check how well the tool integrates with your existing systems and workflows. Seamless integration is crucial for efficiency.
Support and Community. A strong support system and an active user community can make a significant difference. Look for tools with responsive support teams and extensive documentation.

Choosing the right AI tool for testing is critical. It’s easy to get caught up in marketing hype. Stay focused on what truly matters: real, impactful features that improve your testing process. Our experience with Virtuoso has been positive, but it’s essential to do your own research and find the best fit for your needs.

In summary, while AI tools can optimize testing, be cautious and discerning. Not all tools deliver on their promises. Seek out those that offer genuine innovation and practical benefits.

What are the Disadvantages of AI in Software Testing?
If you feel like the previous part confirms that you may be out of work… soon, don’t sell yourself short, at least for now. Here are the limitations AI has and will have for a considerable amount of time.

1) Lacks creativity. AI for software testing algorithms experience big problems generating test cases that consider edge cases or unexpected scenarios. They need help with inconsistencies and corner situations.
2) Depends on training data. Don’t forget — artificial intelligence is nothing else but an algorithm, a mathematical model being fed data to operate. It is not a force of nature or a subject for natural development. Thus, the quality of test cases generated by AI depends on the quality of the data used to train the algorithms, which can be limited or biased.
3) Needs “perfect conditions.” I bet you’ve been there — the project documentation is next to none, use cases are vague and unrealistic, and you just squeeze information out of your client. AI can’t do that. The quality of its work will be exactly as good or bad as the quality of the input and context turned into quantifiable data. Do you receive lots of that at the beginning of your QA projects?
4) Has limited understanding of the software. We tend to bestow superpowers on AI and its understanding of the world. In fact, it is truly very limited for now. May not have a deep understanding of the software being tested, which could result in missing important scenarios or defects.
5) Requires skilled professionals to operate. For example, integrating a testing strategy with AI-powered CI/CD pipelines can be complex to set up, maintain, and troubleshoot, as it requires advanced technical skills and knowledge. Tried and true methods we use now may, for years, stay much cheaper and easier to maintain.

How AI-Based Software Testing Threatens Users and Your Business

There is a difference between what AI can’t do well and what can go wrong even if it does its job perfectly. Let’s dig into the threats related to testing artificial intelligence can take over.

Bias in prioritization and lack of transparency. It is increasingly difficult to comprehend how algorithms are making prioritization decisions, which makes it difficult to ensure that tests are being prioritized in an ethical and fair manner. Biases can influence artificial intelligence models/tools in the data used to train them, which could result in skewed test prioritization.

Example. Suppose the training data contains a bias, such as a disproportionate number of test cases from a particular demographic group. In that case, the algorithm may prioritize tests in a way that unfairly favors or disadvantages certain groups. For example, the training data contains more test cases from men than women. The AI tool may assume that men are the primary users of the software and women are secondary users. This could result in unfair or discriminatory prioritization of tests, which could negatively impact the quality of the software for underrepresented groups.

Overreliance on artificial intelligence in software testing. Lack of human decision-making reduces creativity in testing approaches, pushes edge cases aside, and, in the end, may cause more harm than good. Lack of human oversight can result in incorrect test results and missed bugs. Increased human oversight may lead to maintenance overheads.

Example. If the team relies solely on AI-powered test automation tools, they may miss important defects that could have significant impacts on the software’s functionality and user experience. The human eye catches inconsistencies using the entire background of using similar solutions. Artificial intelligence only relies on limited data and mathematical models. The more advanced this tech gets, the more difficult it is to check the results’ validity, and the riskier is overreliance. This overreliance can lead to a false sense of security and result in software releases with unanticipated defects and issues.

Data security-related risks. Test data often contains sensitive personal, confidential, and proprietary information. Using AI for test data management may increase the risk of data breaches or privacy violations.

Example. Amazon changed the rules it’s coders and testers should follow when using AI-generated prompts because of the alleged data security breach. It is stipulated that ChatGPT has responded in a way suggesting it had access to internal Amazon data and shared it with users worldwide upon request.

So, What Will Happen to AI in Testing?

What is the future of software testing with AI?

We don’t know.

You don’t know.

Our partners at Virtuoso AI don’t know.

We can guess the general direction —

Manual testers will get more into prompting and generate test scripts that will allow more coverage with fewer motions; Expert manual testers will also be more valued for human touch and human eye checking after AI testing tools;
Test automation frameworks will be almost 100% driven by AI;
Continuous testing will become more affordable than ever;
“We needa large number of test cases” trend will be overrun by priritization in testing and monitoring;
soon there will be tools for almost any testing needs, but only the most efficient and affordable solutions will survive the competition. AI is transforming how we do software development and testing.

AI is transforming how we do software development and testing.

If you are a manual QA beginner — you better hurry and invest in your skills. The less expert and the easier to automate tasks you do now, the faster algorithms will come after your job. In the end, here is what Chat GPT thinks of it:

In our company, we started to apply AI-based tools for test automation back in 2022 and continue adopting new tech with new partners — Virtuoso, Google, Amazon, etc.

Will it be enough to stay relevant and efficient?

We definitely hope so. AI can help, but the software testing process is much more complex than just applying new tricks.