Forem: TestGrid

Software Localization Services for Global Product Success

TestGrid — Wed, 18 Mar 2026 18:45:20 +0000

Finding the right software localization services is essential for software teams planning to expand globally. Localization goes beyond simple translation — it adapts software to linguistic, cultural, and technical expectations so the product feels native to users in different regions.

When done correctly, localization improves usability, adoption, and overall product experience across markets.

Without proper localization, even well-built software can struggle to engage users internationally, limiting growth and adoption.

Understanding Software Localization

Software localization is not just translation.

It involves adapting a product to match:

Language and terminology
Cultural expectations
Regional formatting (date, currency, units)
UI layouts and text expansion
Technical standards and compliance

The goal is simple: users should feel like the software was built specifically for them.

Why Localization Matters for Software Companies

Localization has a direct impact on product growth and adoption.

When software feels native to users:

Engagement increases
Onboarding friction decreases
Trust improves
Market entry becomes easier

Business Impact

Effective localization can:

Increase revenue opportunities
Enable entry into new markets
Improve competitive positioning
Support long-term global expansion

Real-World Impact of Localization

Company Type	Localization Project	Result
Gaming company	Localized flagship game into 5 languages	120% user growth in 6 months
Productivity app	Added cultural UI adaptations	75% increase in regional downloads

Localization succeeds when linguistic accuracy and cultural relevance work together.

How to Choose a Software Localization Service

Before selecting a provider, identify what your development workflow actually needs.

Key Evaluation Criteria

A strong localization service should provide:

Native linguists familiar with regional culture
Integration with development pipelines
Continuous update support
Strong quality assurance processes
Industry-specific experience

Localization should fit into your CI/CD workflow — not slow it down.

Core Features of Modern Localization Services

The best solutions typically include:

Advanced translation technology for consistency
Cultural adaptation tools
Collaborative editing environments
Version control compatibility
Real-time updates across teams

Modern platforms integrate directly with repositories and development environments, enabling continuous localization alongside development.

Alternatives to Traditional Localization Services

Traditional agency models are no longer the only option.

1. Machine Translation + Human Post-Editing

Combines automation speed with human accuracy.

Best suited for:

Documentation
Large content volumes
Rapid releases

2. Freelance Translator Marketplaces

Provide flexible, on-demand localization resources.

Advantages:

Scalable costs
Specialized translators
Faster turnaround for smaller projects

3. Internationalization (i18n) and Pseudo-Localization

Instead of translating immediately, teams prepare software structurally for localization by:

Externalizing strings
Supporting layout expansion
Testing multilingual rendering early

This approach prevents expensive redesigns later.

Technologies Transforming Software Localization

Localization is evolving rapidly due to cloud and AI technologies.

Cloud-Based Localization Platforms

Enable:

Real-time collaboration
Distributed team workflows
Faster release cycles
Centralized translation management

Neural Machine Translation (NMT)

Modern translation engines provide:

Improved fluency
Context-aware translations
Faster adaptation for new languages

These systems significantly reduce manual effort while maintaining quality.

Automation and AI in Localization

Automation and AI now handle many repetitive localization tasks.

Examples include:

Automatic string extraction
Translation memory reuse
Continuous localization pipelines
Quality checks and validation

Benefits:

Faster releases
Reduced human error
Consistent terminology
Lower operational costs

AI systems also improve contextual understanding, making translations more natural for end users.

The Role of Machine Learning

Machine learning enhances localization by learning from corrections over time.

It works by:

Analyzing large multilingual datasets
Identifying linguistic patterns
Improving translations based on feedback

As models learn, localization becomes:

More accurate
Faster
Contextually relevant

This enables teams to scale global releases without proportional increases in effort.

Key Takeaways

Software localization is essential for global product adoption.
Translation alone is insufficient — cultural adaptation matters.
Choose services that integrate with development workflows.
AI and automation significantly improve localization efficiency.
Internationalization early in development reduces long-term costs.
Modern localization combines human expertise with intelligent automation.

A strategic localization approach ensures software resonates with international users and supports sustainable global growth.

For more details, readers may refer to Pairaphrase.

Understanding Regression Testing: Why It's Non-Negotiable in Modern QA

TestGrid — Thu, 07 Aug 2025 04:29:56 +0000

Testing is about finding bugs, but it’s also about identifying potential issues and regressions that might be overlooked by the tester and user alike during normal use of the application/system being tested (ease of use).

If a problem occurs, it should be reported as soon as possible (even if it’s “unexpected”) when the test was initially created, so it can be fixed before any further damage is done. This article will elaborate on the factors one should consider while performing regression testing.

What Do You Mean by Regression Testing?

Regression Testing (also known as Software Verification) is used to test whether the current version of an application works correctly or not after modifications have been made to it. Regression testing also includes tests of the original version of the application against a baseline test suite.

This can often be done automatically using Test Automation tools. This process takes time but is essential for a robust software development project. It should start at the planning stage of any new software development or re-write of existing systems that have been released and are now under maintenance or support activities with customers.

You can use various techniques to test for regressions, including comparing two versions or running the same tests on two separate branches (a baseline test). There are many reasons why organizations undertake regression testing of the product and improvement project.

However, when considering this activity, the exercise should always be stated upfront. For example, one may ask, Which factors should we consider during regression testing?
Some of the key factors include:

Time Window: When will the regression tests be executed during the cycle?
Pre-requisites: Are all dependencies and configurations in place?
Test Environment: Is the environment identical to production?
Test Data: Are the data sets representative of real use cases?
System Landscape Scope: Are all connected modules/systems included?
Prioritization of Risk Areas: Focus on areas where changes were made.
Automation: Which parts of the regression suite can be automated?
Script Maintenance: Are existing scripts updated for recent changes?
Full UI Regression: Does the UI behave consistently across versions?
Output Analysis: Is there a process to analyze and act on test results?

Additionally, during automation of UI test cases, especially when working with tools like Selenium, it’s crucial to understand how to find XPath in Chrome for stable and reliable element identification. This step often determines the success of your regression automation efforts.

What is the meaning of successful regression testing?

An essential part of guaranteeing software quality is regression testing. If executed successfully, the provided software will be more resilient and reliable, and its quality will continue to improve with each release.

Applying all the essential factors explained in the article will guarantee that every release of your product is as bug-free as possible.

Vital Factors to Consider During Regression Testing

Time Window

The regression test ensures your app works well enough for users before hitting the App Store or Google Play Store. While there is a period for the app to be on the app store, it will cost the developers and company in the long run. The time window is vital for regression testing and concluding the viability of the app. Each has its own set of considerations that must be considered.

Pre-requisites

Prerequisites are another vital factor to consider during regression testing, and it is solely considered by the people who perform the test. The whole point of considering this factor is so that we don’t have to guess what works and doesn’t. So it’s best to make sure everything is in place before running an entire test series instead of running after building your software. Prerequisite primarily considers finding out that a problem with some piece of hardware or software on a particular day caused a failure in the test run, and you missed it by seconds and hours.

Environment

Without considering the environment and reliability, the regression test can’t be undertaken. It’s a good idea to set up a build (test) that is not connected to the production environment live site and use it for these tests, as you can be sure that your code will work correctly before the customer sees it. This is where the smoke testing is vital. To ensure your application performs as expected, it is essential that you have a robust smoke test running before each release or when any changes are made to your application codebase; these are known as unit tests, integration tests, or functional tests (depending on the scope of testing).

Data

Test data is the prior requirement before performing a regression test. Standard data can also be utilized to find out the older defects in regression testing. While working on the test plans and test procedures, the data from these systems can then be used for regression testing and code reviews (which I think is the most useful way of doing it).

Landscape Scope

At some point of time during the test, there can be necessary use of Stubs or API calls to perform or mimic specific software and applications. This mimicking is necessary to grasp the responses of particular applications in case they are unavailable at the time of performing the test. It allows you to visually see where each condition fits within your project in terms of scope, priority, risk, etc., and what testing steps have already been completed on that requirement so that any future testing steps can be determined based on the current status of the project at hand.

Prioritization of Risk

After the successful execution of the Smoke test, the developer or tester must consider the sanity test. It will help them verify all the vital processes for the presence of any bugs or malware that can cause harm after the app’s release. If this is an internal application, will there be any negative impact on other parts of the business? Prioritization can be the factor that is essential for concluding all the responsible impositions before the introduction of the app. Therefore, it is essential to keep track of what has already been tested or proven, as well as what needs to be tested next.

The following areas which must be considered to be inspected are:

Most used processes.
Areas with recent changes.
Recent defects fixed.
A clean run and negative testing.
Historical regions of defect density.

Automation

It has an automation strategy that promises you on-timely test completion and value-for-money for your investment, rather than just keeping the tests running forever and not knowing what to do with them. When they reach their end of life or are no longer relevant to your business needs and requirements, it potentially causes a lot of manual testing, time wastage as your testers try to fix issues where none exist.

Script Maintenance

Script Maintenance is another factor to consider during regression testing. Suppose the automation can get manipulated even in the absence of impediments. Automation allows you to quickly identify any regressions and take remedial action without re-running the tests on every change to your codebase or manually testing every possible scenario yourself. But somehow, Automation can only make things easier for us but sometimes may fail to perform as per the mechanism. This is where script maintenance is necessary to cope with such a scenario.

Full UI Regression

The only good place to start is to test that the UI will render correctly on screen with a different resolution, font size, and DPI settings (to simulate real-world conditions). Once you have done this, you can move on to testing the various scenarios like testing the user interface when the application starts up – how do things look when it first starts? Does the correct information appear in the right places, if not, what happens? Are there any unexpected results? Is the data showing up? What about custom fonts or images, etc.

Output Analysis

Most of the time, Automation can take hours to execute the task. If you run the automation at night, the results can only be reviewed the next day. In such a case, a primary high-level pass/fail must be considered in the analysis. After that, look out for the exact location of the error and resolve it with appropriate steps.

The Bottom Line

Planning and Execution are a vital part of the regression test, which are most often conducted by developers during the early phases of a project. This is a stage when there is little or no information about the requirements of the product being developed. The final stage would be regression testing with the system under load to see how it responds to the expected workloads.

Source: For more details, readers may also refer to TestGrid.

Getting Started with Selenium Testing

TestGrid — Mon, 23 Jun 2025 06:35:53 +0000

Automation testing has become a non-negotiable part of modern software delivery. If you operate in this space, you may already feel pressure to roll out products and new features faster, maintain high quality, and meet user expectations.

Lucky for you—automating tests can significantly improve result accuracy (by up to 43%) and boost team agility (by 42%). But to achieve these benefits, you need the right tool that makes your job easier.

That’s where Selenium enters the picture.

It’s incredibly flexible, letting you work across different environments, browsers, and workflows. Basically, it adapts to your testing approach.

What Is Selenium?

Selenium is open-source software that comprises a range of tools and libraries for automating web browsers. It enables you to simulate user actions like clicking, typing, navigating, and interacting with web elements to test how your apps behave under different conditions.

On Selenium, you can author functional tests across modern web browsers like Chrome, Firefox, Safari, and Edge or run tests in a headless browser (e.g., Chrome Headless) for faster execution, especially in CI/CD pipelines. It also runs on Windows, macOS, and Linux.

Selenium provides a test domain-specific language called Selenese to write tests in many popular programming languages, including JavaScript (Node.js), Groovy, PHP, C#, Perl, Scala, Python, and Ruby.

You can start using it without incurring any licensing costs. Its active community regularly contributes fixes, plugins, and enhancements. You can also find extensive online documentation and tutorials for troubleshooting.

Components of Selenium

Selenium isn’t just one tool but a suite of tools, with each component serving a specific purpose. Together, they create a robust ecosystem for automation testing. Let’s break them all down in the following section.

1. Selenium IDE (Integrated Development Environment)

This browser extension on Chrome and Firefox allows you to record, edit, and replay test cases. With Selenium IDE, you don’t need to write any code to get started. It’s best for quick prototyping and creating simple test scripts without diving into programming.

2. Selenium WebDriver

This is the heart of modern Selenium automation. Selenium WebDriver is a robust tool that lets you directly interact with browsers using code. It can handle dynamic web elements and advanced interactions like drag-and-drop or file uploads, and manage browser-specific capabilities using driver executables. Selenium WebDriver is also a crucial component in mobile browser testing.

3. Selenium Grid

When your project scales and you need to run tests across different browsers, operating systems, or devices, Selenium Grid steps in.

It equips you to distribute your tests across multiple machines to speed up execution. Selenium Grid operates using a hub-and-node architecture, where the hub coordinates test execution requests, and the nodes handle running the tests on designated browsers or devices.

How Does Selenium Work?

When you start using this tool, you’ll first write a script using its libraries in your preferred programming language, such as Java, Python, or C#. Your script is like a set of instructions for the browser—it tells it what to do, such as opening a webpage or filling out forms.

Once your script is ready, Selenium’s WebDriver takes commands from it and sends them to the browser driver. The driver then translates the commands into a format the browser can understand. The W3C WebDriver Protocol is the standard used for communication, replacing the older JSON Wire Protocol.

In the next step, the browser driver converts those translated commands into actual actions within the browser. Whether you click a link or scroll a page, it executes these actions as if you were performing them manually.

After the browser completes each action, it responds to the browser driver.

This feedback is passed on to the WebDriver, which communicates it back to your script. This is how it knows whether the actions were successful if there were any errors, or if further steps need to be taken based on the browser’s current state.

Prerequisites for Automation Testing in Selenium

Let’s discuss what you’ll need to be fully prepared for Selenium software testing.

1. Understand programming fundamentals

First, you should be comfortable implementing design patterns like the Page Object Model (POM) or Factory Method. These will help you create modular, reusable test scripts that are easier to maintain.

It’s also critical to handle common issues effectively, like element-not-found exceptions or timeouts, so your tests remain reliable. If your app relies heavily on JavaScript, knowing how to manage asynchronous operations in Selenium will reduce flaky tests.

2. Gain expertise in asynchronous operations and locators

Since modern web apps often dynamically update the Document Object Model (DOM), traditional locators are unreliable. To target elements dynamically, you must master flexible XPath expressions and CSS selectors.

Take advantage of Selenium 4’s relative locators. They enable you to find elements based on their position relative to others, simplifying testing complex layouts.

3. Familiarize yourself with testing frameworks

You’ll also need to know popular testing frameworks like TestNG, JUnit, or PyTest. These help you organize and execute test suites effectively.

They come with essential features like parallel execution, dependency management, and detailed extent reporting, all of which make managing larger test environments much easier.

Integrating these frameworks with Selenium will make your tests scalable and maintainable, especially if you’re working in a collaborative or CI/CD-heavy environment.

4. Build knowledge of browser drivers and configurations

Let’s not forget browser drivers and their configurations. To avoid compatibility issues, use tools like Selenium Manager to automatically align browser and WebDriver versions.

If you’re testing across multiple browsers or platforms, tools like Docker can simplify the setup by providing consistent environments for execution.

5. Integrate Selenium with CI/CD pipelines

A big one is integrating Selenium tests into CI/CD pipelines. Tools like Jenkins, GitLab CI/CD, or GitHub Actions can automate your tests so that they run whenever code is updated. This ensures that issues are caught early, saving time and effort.

You can also leverage cloud testing platforms like TestGrid to run tests on a wide range of devices and browsers without managing the infrastructure yourself.

6. Develop debugging skills for efficient troubleshooting

Debugging is another critical skill. You should be familiar with browser developer tools for inspecting elements, analyzing console logs, and debugging network calls.

In addition, knowing how to use your IDE’s debugging tools for step-through testing and log analysis will make diagnosing issues in your scripts much faster and more efficient.

7. Expand testing skills with API and REST testing

Many modern applications rely heavily on APIs, and combining Selenium’s UI tests with API tests gives you broader coverage. Tools like Postman or RestAssured are great for this and can complement your Selenium expertise nicely.

How to Run Automation Testing in Selenium

With prerequisites out of the way, let’s walk through selenium automation testing step by step. We’ll also discuss some examples and practical insights to help you get started.

1. Finalize the testing environment

Before you do anything, set up your tools. Install Selenium WebDriver, browser drivers (like ChromeDriver for Chrome or GeckoDriver for Firefox), and any testing frameworks you plan to use, such as TestNG or PyTest.

Make sure your WebDriver version matches your browser version to avoid compatibility issues. You can use Selenium Manager to automatically ensure your WebDriver version matches your browser version.

Start small to ensure everything is working—like writing a script to open a webpage and print the title. Once confident, layer in additional functionality like interacting with elements or verifying page behavior.

Also, remember to keep your dependencies updated to avoid breaking changes.

2. Structure your scripts cleanly

A well-structured script saves you time when changes occur and makes collaboration easier. When you’re working in a team, others should be able to understand and extend your tests without untangling messy code quickly. Therefore, spend some time in script structuring.

Use POM to separate test logic from UI details. For instance, if you’re testing a login page, create a ‘LoginPage’ class to handle elements like the username field, password field, and login button. Then, call these methods from your test script instead of rewriting the locators every time.

3. Write effective test cases

Test cases should be clear, specific, and actionable. Suppose you’re testing an eCommerce site. In that case, one test might check if searching for “laptops” displays relevant products, and another could confirm that clicking “Add to Cart” actually adds an item.

Think about edge cases, too. What if a user enters “laptop123$$$” or a similar input that includes both alphanumeric and special characters? Will the search still work? Parameterizing your tests lets you cover multiple scenarios without duplicating code.

4. Run tests locally

Before you scale up, test locally, as it’s the easiest way to validate your script. Testing a password change feature? Run it on your local browser to confirm the inputs and outputs match expectations.

Local runs are also perfect for debugging. If something fails, you can pause execution, inspect the browser, and adjust your script in real-time. Use headless mode to skip UI rendering. It’s perfect when you don’t need to see the browser in action but want the results.

Once everything works fine locally, you can confidently run it on larger environments like CI/CD pipelines or distributed setups.

5. Run multiple tests in a parallel manner

Why? Because this saves time.

Here’s a scenario: if you need to test the search, login, and checkout features on Chrome and Firefox, you can configure Selenium Grid or use TestNG’s parallel execution.

Parallel execution also uncovers browser-specific issues faster. For example, a drop down might work in Chrome but behave oddly in Firefox. Fixing these discrepancies early saves you trouble down the road.

6. Handle dynamic elements

Dynamic elements can be tricky, but they’re everywhere in modern web apps. Imagine a page where a “Submit” button’s ID changes every time you reload. You could write a code snippet using Selenium to locate and interact with it.

Employ a flexible XPath expression to identify a button element whose text includes the word “Submit.” With this, the code ensures that the automation remains robust and adaptable to changes in the web page’s structure.

7. Debug and log periodically

When a test fails, logs can save you. For instance, if a login test fails, you can log the current URL and page source to understand what went wrong. Use logging libraries like Python’s built-in ‘logging’ module, Log4j in Java, or SLF4J.

In addition to logs, screenshots of failures can be invaluable. Configure your tests to capture screenshots automatically when something fails—it’s a quick way to identify issues like missing elements or unexpected UI changes.

8. Run tests in CI/CD pipelines

Automate your tests with tools like Jenkins or GitHub Actions. For instance, every time your team pushes new code, your pipeline can automatically pull the latest changes, run tests, and report results. It’s like having an extra pair of eyes on your project 24/7. This setup also speeds up development by giving instant feedback.

Unique Challenges in Selenium Software Testing

While Selenium is a powerful tool for browser automation, it comes with its own set of roadblocks—some of which might not be immediately obvious. Here are the trickier aspects of Selenium software testing:

Although Selenium supports multiple browsers, slight differences in how browsers render pages can lead to inconsistent test results. Therefore, it’s vital to regularly validate your tests across all supported browsers to ensure accuracy.
Running Selenium tests at scale requires a robust infrastructure. Setting up and managing Selenium Grid or using cloud testing platforms can mitigate this, but they may introduce additional costs and expertise.
Selenium tests depend on the WebDriver matching the browser version. When a browser updates, the WebDriver often lags behind, causing compatibility issues. Use Selenium Manager (introduced in Selenium 4.6) to handle driver version management automatically.
Captchas and MFA often create problems that Selenium alone cannot bypass. Therefore, it’s best to consider test environments with Captchas disabled or employ tools like browser automation APIs that support CAPTCHA-solving services.
Updating locators and scripts for every small change can become time-consuming. Implement self-healing locators using AI-powered tools that adjust locators automatically when the UI changes.

The Future of Selenium Testing: What Lies Ahead?

As the web evolves, so does Selenium. But what’s next for it? What forward-looking trends and possibilities can shape Selenium testing?

For starters, the rise of visual regression tools has shown that traditional testing can’t always catch UI discrepancies. But not if Selenium integrates native support for visual testing.

Instead of asserting that a button is clickable, you can validate that it’s perfectly aligned, colored correctly, and looks the same across all devices.

A Selenium script might include commands like assertVisualConsistency(page, “baseline_screenshot”), and any UI drift would be flagged automatically.

Have you heard of a quantum leap with quantum computing? It may sound like science fiction now, but this could eventually impact Selenium testing.

Quantum algorithms can process massive test suites exponentially faster, enabling real-time testing across hundreds of environments. This could make large-scale cross-browser and cross-device testing instantaneous.

Lastly, picture a drag-and-drop interface where you define test scenarios visually, and Selenium generates the underlying code. While it’s traditionally a developer’s tool, the future could bring no-code or low-code extensions.

This would lower the barrier to entry for non-technical testers while maintaining Selenium’s flexibility for advanced users.

Conclusion

Selenium remains a cornerstone in the world of automation testing—versatile, widely adopted, and continuously evolving. From handling simple UI checks to supporting complex, cross-browser testing at scale, it empowers teams to deliver quality software faster and more reliably. But like any powerful tool, unlocking its full potential requires the right setup, skills, and strategies.

By understanding its architecture, mastering best practices, and staying updated with its growing ecosystem, testers, developers, and DevOps professionals can overcome real-world challenges with confidence. Whether you're just starting or scaling your test automation strategy, Selenium offers the flexibility and community support needed to keep up with modern software delivery demands.

As automation continues to advance—with AI, visual testing, and no-code tools on the horizon—Selenium is well-positioned to remain relevant and indispensable. Embrace it today, and future-proof your testing for tomorrow.

Source: This article was originally published on TestGrid.

AI-Powered Test Automation: Comparing the Best Tools for Smarter Testing

TestGrid — Wed, 02 Apr 2025 14:04:10 +0000

With tight deadlines to release software applications and the need to quickly test them before release, most software companies are resorting to automated processes instead of manual testing. Moreover, the rise of artificial intelligence has added another dimension to automation testing.

As per Forbes, the global AI market size is projected to reach $1,811.8 billion by 2030, highlighting the reach of AI in every industry, including software testing. AI in software testing helps testers execute tests at a rapid pace, significantly reducing testing time and costs.

With the right AI testing tools, you can take your software development to the next level, ensuring greater efficiency, superior products, and faster release to the market. Selecting the best tool from the huge number of tools saturated in the market is no easy task.

To make your process easier, we have listed the top five AI tools currently dominating the market and their key features. Before that, we will quickly examine the reasons why AI is benefitting the testing process, including advancements in codeless automation, which simplify complex testing workflows and enhance efficiency.

Benefits of AI Testing

AI can optimize your software testing in these ways:

Faster test execution: Allows you to run a large number of test cases in a short time
Easy test case creation: Generates many test cases for even highly complex test scenarios in minutes
Greater accuracy: Minimizes the possibility of human error, ensuring consistent and reliable results
Comprehensive testing: Let’s you cover multiple scenarios and even edge cases that are challenging to test through conventional methods
Detailed test reports: Generates comprehensive test reports quickly and easily pinpoints defects and lets you share feedback with developers
Self-healing test automation: Enables self-healing where test scripts adapt quickly to the changes in the application’s UI, such as IDs or element locators.

Top AI Automation Tools and Their Features

With these tools in your testing kit, you can make test automation less complicated and reap the benefits mentioned above:

1. TestGrid

This cloud-based, codeless, AI-powered platform elevates your testing experience in ways you may never have imagined. With its capabilities, you can experience a reduction of up to 60% in testing time and around 50% decrease in costs. It makes writing and running tests effortless and more efficient, whether you are testing mobile, web, or desktop applications.

Key features:

Generate test cases easily by writing them in English, CSV, and BDD and pasting them to the portal to generate test cases
Identify bugs early in the development phase and decrease debugging time significantly
Use the Record and Playback feature to record complex business-critical test scenarios and capture app/browser interactions automatically.
Auto-heal your codeless tests to prevent them from failing by automatically locating valid locators of broken objects.
Conduct multiple tests in parallel across different platforms, browsers, and environments to save precious time.
Perform effortless visual testing and identify even minor variations in appearance to achieve pixel-perfect precision.
Easily integrates with popular CI/CD tools like Jenkins, Circle CI etc., for streamlined automated testing.

2. iHarmony

It is an AI-powered open-source tool that is popularly used to test mobile and web applications. You can use its machine-learning algorithms to generate and optimize test cases intelligently.

Key features:

Creates test cases based on code patterns and previous test results
Adapts and enhances its test coverage with its self-learning capabilities
Supports testing on both mobile and web applications
Offers self-healing feature that makes test maintenance easier

3. Selenium

It’s a robust and versatile tool that is the preferred testing solution for many developers. Its recently added AI features are designed to add flexibility to execute tests. Being open-source, it is compatible with operating systems like Linux, Windows, and MacOS.

Key features:

Supports various browsers, including Safari, Firefox, and Chrome
Enables smooth test management and generation of reports
Supports multiple programming languages like Python, Java, and many more to create test scripts
Once you prepare test cases, you can run them on any operating system of your choice

4. Sauce Labs

It is a testing tool suitable for both web and mobile testing, offering a variety of low-code testing features. It supports multiple test automation frameworks, like Selenium, Espresso, Cypress, and Appium.

Key features:

Offers a stable and reliable test infrastructure for your testing needs.
Integrates easily with your existing CI/CD pipelines.
Allows you to run tests in parallel, speeding up your testing and increasing productivity.
Provides features like videos, screenshots, and HTML logs that help you identify issues faster.

5. Tricentis Tosca

It is a robust AI-powered tool that accelerates your testing efforts and removes the bottlenecks and risks related to software releases. It offers a modern cloud infrastructure that lets you scale your testing efforts as and when needed.

Key features

Supports all the testing you need, from API and UI testing to system integration testing.
Its Vision AI lets you automate the most complex apps, like those on remote desktops.
Supports DevOps and agile workflows.
With Tosca Copilot, optimize your test suite to identify duplicates, unlinked assets, unused test cases, and more.

Conclusion

AI-powered testing opens unlimited possibilities in software testing that no one could have imagined a few years back. AI technology is here to stay as it has simplified and elevated the testing process and helped developers and testers overcome the limitations that were part of traditional testing tools.

As the impact of AI in software testing continues to increase worldwide, more organizations are resorting to AI to ensure they deliver secure and perfect software faster to the market.

By choosing the best one from the above list of AI automation tools, you also can gain a competitive edge and secure your foothold in the software marketplace.

Source: This article was originally published at Apollo Technical

Healthcare Application Testing: A Step-by-Step Guide for QA Teams

TestGrid — Mon, 24 Mar 2025 19:13:10 +0000

Software testing in healthcare differs from traditional testing methodologies. Consider an example: a bug in an eCommerce app may cause a payment failure. But a bug in a healthcare app? That can delay an illness diagnosis and compromise patient safety. The risks and consequences are significantly higher. Key focus areas of healthcare application testing include:

Adhering to GDPR, HIPAA, FDA, and other industry-specific standards
Validating communication between hospital systems, medical devices, and third-party APIs
Protecting sensitive Patient Health Information (PHI) against breaches and unauthorized access
Testing for performance in high-pressure medical environments with varying network conditions

Healthcare Application Testing Strategies To Apply in 2025

If you want testing to be successful for you, it needs to be comprehensive, risk-based, and adaptable to the unique challenges of healthcare workflows. Let’s explore six components of healthcare software testing services.

1. Functional testing

From patient registration to prescription management, the app must perform all functions flawlessly. Validate critical areas like:

Can the system accurately display patient history across multiple sessions?
Does it catch essential user errors, like a doctor mistakenly entering an incorrect dosage?
Are lab results, imaging scans, and prescriptions getting updated across all connected systems in real-time?

Functional app issues aren’t always obvious, especially in edge cases like emergency scenarios or rare medical conditions. Therefore, test for unexpected edge cases that could cause system failures at critical moments.

For instance, if a patient enters a heart rate of 300 bpm or a blood pressure reading of 250/180, the app should be able to process such values, alert medical professionals in time, and not crash or reject the data.

2. Globalization testing

This one is important to undertake for apps targeted at a global audience. Given the differences in healthcare regulations worldwide, globalization testing takes into account variations in the local version of your healthcare software.

Therefore, it includes language support, region-specific integrations, and marketing approaches.

Healthcare application testing should also review whether the platform fits the language and culture of specific countries or geographies.

For instance, double-check the currency, data, and time formats. Ensure the content translation doesn’t change the meaning of the text.

3. UI/UX and usability testing

Healthcare apps cater to a diverse audience, including:

Hospital staff working under pressure (who need real-time updates and efficient workflows)
Doctors rushing through emergencies (who want to be able to pull up patient records in seconds)
Elderly patients with vision impairments (who require voice navigation, high-contrast themes, and screen readers for accessibility)
Whatever it is, it’s not a calm, controlled environment.

Therefore, optimize the app UI for clarity and ease of use.

Perform checks with real clinicians in the picture to understand whether it’s intuitive, minimizes cognitive load, and allows one to access health records quickly when every second matters.

You don’t want an app where critical information gets buried under clunky menus and unnecessary clicks. In addition, accessibility features like voice navigation, high-contrast themes, and screen readers should be implemented.

4. Data security and compliance testing

With PHI being a prime target for cybercriminals, application security testing for healthcare is non-negotiable. Imagine a telehealth system that allows video consultations between doctors and patients.

If session tokens aren’t properly managed, one can hijack an active session, exposing confidential medical discussions. Therefore, simulate a real-world attack to uncover vulnerabilities before they get exploited.

Check if access controls are configured correctly, ensuring only authorized personnel can modify prescriptions. Conduct periodic compliance audits to meet HIPAA, GDPR, and FDA standards.

5. Automated software testing in healthcare

For instance, automated scripts are great for regression testing. They ensure that core functionalities like appointment scheduling, billing, and data retrieval work consistently. Automation can also detect UI bugs faster than manual testers.

However, some aspects of healthcare app testing demand human oversight.

For instance, if your app uses AI for diagnostics, you need medical experts to verify it provides accurate recommendations. No automation can catch the nuances of misleading results. You also can’t rely on scripts to determine if the app makes the right call in a life-or-death situation.

Regarding EHR interoperability and data integrity testing, automation might confirm that data moves from Point A to Point B. But it won’t catch subtle errors like a misaligned patient history or a misformatted medication list that could cause real harm.

Supplement automation with structured data validation, edge case testing, and domain-expert review to detect misattributed records and formatting inconsistencies.

6. Device compatibility and interoperability testing

Unlike standard web or mobile apps, healthcare solutions often interact with many hardware devices via APIs or middleware.

Smart hospital monitors, EHR terminals, diagnostic machines, and medical-grade tablets have different operating systems, firmware versions, and data transmission protocols. That means any integration failure can disrupt patient care.

What if a minor communication glitch prevents an insulin pump from receiving real-time blood sugar data, leading to incorrect dosage adjustments?

Health data must conform to industry standards, like FHIR, HL7, and DICOM, to achieve true interoperability. This will ensure that a patient’s lab test results are updated in the hospital system the moment they’re available, preventing discrepancies in medical records.

Perform network resilience testing under various conditions (low bandwidth, intermittent connectivity, etc.) and simulate high-traffic emergency room scenarios to test performance under load.

Challenges in Healthcare Application Testing

What roadblocks put a dent in healthcare software testing? Let’s find out.

1. Data migration and legacy system integration

Many healthcare organizations still use legacy Electronic Health Record (EHR) systems and outdated infrastructure. This makes migrating patient data challenging as:

Data duplication may arise due to multiple sources with differing structures
Records might be in different formats
Critical fields could be missing
Poor integration can result in inaccessible medical histories or incorrect prescriptions, which can be life-threatening.

2. AI and Machine Learning (ML) validation

AI models power diagnostics, medical imaging, and personalized treatment recommendations in healthcare application testing. However, they’re prone to biases and errors, resulting in potential misdiagnoses or incorrect treatment suggestions.

3. Ethical and consent management issues

Healthcare apps collect vast amounts of patient data and must comply with certain data retention and consent management policies.

For instance, can patients easily understand what they agree to when sharing their data? Do they have the option to control or delete it if they choose? How are the sensitive details being used, shared, and retained over time within the app?

Improper data handling can lead to legal and ethical issues, causing apps to risk losing credibility with the people they’re supposed to serve. It’s important to ensure healthcare apps adhere to ethical standards. Otherwise, they risk losing credibility with the people they’re supposed to serve.

4. Real-world environmental testing

Most software testing happens in controlled environments. However, a different approach is needed since healthcare apps don’t function under ideal conditions. For example, a telemedicine platform with high-speed internet may work flawlessly in an urban hospital.

But what about in rural areas with weak connectivity? Can the healthcare app handle network interruptions, whether it’s being used when multiple critical processes are running simultaneously, or it’s being operated on older hospital hardware?

App performance failure in a controlled environment is frustrating. But failure in an emergency room? Catastrophic.

Best Practices for Healthcare Application Testing

Now that we’ve covered a lot of ground in testing healthcare software, let’s uncover the most effective tips for making this a success.

1. Implement a risk-based testing approach

Not all features in a healthcare app carry the same level of risk. UI customization is essential, but it isn’t mission-critical. On the other hand, features like patient data processing, medical billing, or EHR integrations must run smoothly.

A risk-based testing approach enables you to allocate resources to efforts where failures can significantly impact patient care, compliance, and financial integrity.

2. Use realistic, anonymized test data

Many healthcare apps fail because the testing is carried out with incomplete or unrealistic data. This hinders the simulation of true clinical workflows and prevents defects from being uncovered before they reach production.

Therefore, use anonymized real data whenever possible. With permission, extract real patient data from hospital records and then de-identify and scramble personal identifiers while maintaining logical relationships between conditions, treatments, and test results.

For example, if a patient was diagnosed with diabetes and prescribed insulin, their de-identified record should still reflect that condition-to-prescription relationship.

If real data is unavailable, generate high-quality synthetic data. Tools like FHIR Test Data Generator and Synthea can help create structured test datasets that reflect realistic demographics, treatments, and conditions.

Lastly, remember to encrypt all test data during storage and transmission. Mask sensitive fields like SSNs, insurance IDs, and contact details before running automated tests.

3. Simulate the app for life-and-death situations

You need to test your healthcare app beyond expected user behavior. What happens when a doctor accidentally prescribes 100 mg instead of 10mg? Does the system catch the mistake or let it slip through?

Imagine a patient entering a heart rate of 10 bpm while their blood pressure reads 200/180 on their wearable — does the connected app flag it as a medical emergency, or does it crash because it wasn’t built to handle extreme values?

You must test the little things and consider all possible scenarios — the good and the bad.

4. Establish a traceability matrix for complete coverage

A traceability matrix is a technique that ensures every requirement, test case, and compliance rule is accounted for. In healthcare application testing, missing even a single requirement, like a medication dosage calculation, a HIPAA compliance check, or an EHR data validation, can result in serious risks, such as regulatory fines or patient harm.

Therefore, to deploy a traceability matrix:

Map every test case to a requirement
Ensure bidirectional tracking so you can trace a test back to its source requirement and vice versa
Regularly update the matrix as new requirements or regulations emerge

Put AI to Work in Healthcare Application Testing

Harness the power of HIPAA- and GxP-enabled data sets, healthcare software testing tools, and frameworks that make patient care successful.

Get support in workflow optimization with comprehensive IT solutions and services. More importantly, make better point-of-care decisions with testing solutions for the healthcare system.

Source: For more details, refer to TestGrid.

Cypress Parallel Testing Made Easy: Step-by-Step Guide with Practical Code Examples

TestGrid — Tue, 11 Mar 2025 12:20:12 +0000

Cypress is a popular tool that is used for front-end testing in a simple manner, enabling easy execution of the tests. One feature of Cypress that can help you enhance the speed and efficiency of your test execution is running tests in parallel. In this article, we will dive into the details of executing tests in parallel using Cypress. We will first discuss parallel testing and then talk about how we can implement parallel testing in our Cypress automation tests.

What is Parallel Testing?

Parallel testing is a technique to execute the tests in parallel across multiple threads or processes. This technique allows for the concurrent execution of tests, in a distributed manner, rather than the sequential run. Parallel execution can be achieved by splitting the tests in multiple batches. These batches are assigned to different threads so that they can execute concurrently in isolation from each other. By following the approach, the overall test execution time is divided by the number of threads. More the number of threads, faster the execution.

Benefits of Cypress Parallel Testing

Increase in speed of execution by magnitudes, reducing the execution time from hours to minutes.
Increases regression coverage by running more tests concurrently.
Resource utilisation is improved by ensuring that idle time is not wasted.
Enables you to execute multiple tests in reasonable time.
Runs test in complete isolation by default.

Configuring Parallel Tests in Cypress

We will first create different tests and execute them normally, in sequence to see the execution time for the tests. To do so, we’ll write three tests, two to perform google search, and one to perform image search. Below is the code snippet for each of these tests.

GoogleSearch.cy.js

describe('Google Search',() => {
   it('should be searching keyword on google',() => {
       //Navigate to google search page
       cy.visit('https://www.google.com/');
       //Adding wait time to load the page
       cy.wait(500);
       //Entering search value in the textbox and performing th eenter key simulation
       cy.get('.gLFyf').type('Automation Testing Cypress{enter}');
   })
})

GoogleSearch1.cy.js

describe('Google Search 1',() => {
   it('should be searching keyword on google',() => {
       //Navigate to google search page
       cy.visit('https://www.google.com/');
       //Adding wait time to load the page
       cy.wait(500);
       //Entering search value in the textbox and performing the enter key simulation
       cy.get('.gLFyf').type('TestGrid{enter}');
   })
})

GoogleImage.cy.js

describe('Google Image Search',() => {
   it('should be searching image on google',() => {
       //Navigate to google search page
       cy.visit('https://www.google.com/');
    //Adding wait time to load the page
       cy.wait(500);
       //Clicking on Images link on the google search page
       cy.contains('Images').click();
       //Entering search value in the textbox and performing th eenter key simulation
       cy.get('.gLFyf').type('Tesgrid automation{enter}');
   })
})

Note that I am keeping these three test specs in a folder parallel-tests as can be seen in the snapshot below:

Let us execute these tests using the command below:

npx cypress run --spec "cypress/e2e/parallel-tests/*.js"

As you can see, the execution time is 49 seconds for the tests, we will see how we can decrease the execution time by using cypress-plugin.

Using cypress-parallel plugin

The first step in implementation of the parallel test feature is to install the cypress-parallel plugin. To install it, execute the below command:

npm i cypress-parallel

After installation of the plugin, you will need to update the package.json file by adding below lines of code in the scripts section:

"cy:run": "cypress run",
"cy:parallel": "cypress-parallel -s cy:run -t 3 -d 
'cypress/e2e/parallel-tests/*.js'"

Here, -t denotes the number of threads that you would want to invoke, and -d specifies the spec file path.

Once this is done, the package.json file would look like below:

{
 "name": "paralleltest",
 "version": "1.0.0",
 "description": "",
 "main": "index.js",
 "scripts": {
   "cy:run": "cypress run",
   "cy:parallel": "cypress-parallel -s cy:run -t 3 -d 'cypress/e2e/parallel-tests/*.js'"
 },
 "author": "",
 "license": "ISC",
 "devDependencies": {
   "cypress": "^13.5.0",
   "cypress-image-diff-js": "^1.32.0"
 },
 "dependencies": {
   "cypress-parallel": "^0.13.0"
 }
}

Next step is to execute the parallel tests using below command:

npm run cy:parallel

Upon execution you will notice the execution time has been decreased by a few seconds.

Though you won’t be able to see a huge difference above, but, when you execute a large number of tests you will see a visible decline in execution time.

Using CI parallel integration

Another way to implement parallel test execution is through the CI tool that you must be using. You can refer to the CI tool documentation to set up multiple machines in your CI environment. You can use these multiple machines to execute tests in parallel. Due to the limitation of setting up a CI environment now, we will simply go through the steps that we can follow to make the desired settings.

After machine set up execute the below command:

cypress run --record --key=abc123 --parallel
Here, run tells Cypress to execute all the tests in the spec folder, in headless mode by default. The record option enables the recording of the test run to record the screenshots and the videos. The key attribute is your personal cypress authentication key that can be fetched from the Cypress dashboard under the record keys. And the parallel attribute enables parallelization of tests to distribute across multiple CPU cores and threads.

You can further split your tests in separate files to enable parallelization of tests and can refer to the cypress documentation for details.

Best Practices for Cypress Parallel Testing

It is always better to keep tests focused, isolated and concise.
Try to group tests that take longer to execute separately.
Re-execute tests that fail sequentially to debug the error in a better way.
For flaky tests, add retries.
When using repeatable data sets, use a data-driven approach.
Try to eliminate test execution dependency by using random seeds.

Cypress Parallel Testing — Drawbacks

There might be some pitfalls too in using cypress parallel testing which you might consider while building your framework.

Tests may encounter race conditions due to shared data. To overcome this, try to implement a reset state between tests.
If there are tests that modify global data, there may be data inconsistency. To mitigate this, either avoid using global data or create a reset state between tests.
Some APIs might have limitations of concurrent users, because of which in parallel execution some APIs may get blocked or fail. To avoid such a situation, limit parallelism based on server capacity.
There might be CI failures that are hard to replicate, hence try to match CI parallelism as much as possible.
There might be file lock conflicts between the runner and parallelization tools. You should use only one test runner to prevent clashes.

Conclusion

Automation testing can be vast and is best utilized when you can run a large number of tests and increase your overall test coverage.
With increase in the number of tests, the execution time also increases. Cypress parallel testing offers a way to reduce execution time and execute tests faster with the same results.
Cypress offers using the cypress-parallel plugin to execute tests in parallel mode, by providing the number of threads you would want to execute in parallel.
You can also integrate cypress with CI tools to scale test execution in a shorter duration of time.
Cypress parallel testing when implemented by considering factors like server limit, environment reset state, data-driven approach, etc., one can efficiently scale test execution by not compromising on the overall coverage and efficiency of the framework.

Source: This article was originally published at TestGrid.

CoTester vs Other Agentic AI Platforms for Testing

TestGrid — Mon, 10 Mar 2025 12:16:29 +0000

Here’s a fact: software testing has evolved over the years.

Gone are the days when you’d painstakingly write scripts for every scenario or settle for incomplete coverage due to resource constraints. Testing cycles are tighter today, apps are more complex, and demand for quality is greater than ever.

Naturally, AI tools have stepped up massively in the last few years. Gartner reports that 60% of companies automate software testing to improve product quality, while 41% do so to optimize departmental agility.

Moreover, 57% of IT and software engineering leaders believe Generative AI will be widely used in software testing, especially for predicting common issues or bugs, by 2026.

What if we told you there’s an Agentic AI that’s onboardable, trainable, and taskable? It can easily integrate into your team, adapt to your workflows, and test your software like an experienced human tester.
Meet CoTester by TestGrid.

It’s built to go beyond ChatGPT’s capabilities. It can write test cases for your web app or website, assist with real-time debugging, and execute them on various browsers.

How, you ask?

Based on the testing scenarios and user stories you provided. What’s more—there will be no extensive retraining or process overhauls.

But what are CoTester’s clear strengths, and where does it fit in the testing landscape? How does it measure up against other AI test automation platforms? While they bring advantages, let’s do a comparative analysis in this blog post.

CoTester by TestGrid

CoTester by TestGrid is the world’s first AI software tester pre-trained on advanced software testing fundamentals and Software Development Life Cycle (SDLC). It frees valuable time for your team and enables them to focus on more complex testing scenarios.

CoTester relies on predefined commands even when using natural language. This means it uses true AI to understand user intent without rigid syntax constraints, unlike other AI testers who are primarily syntax-driven.

You can interact with it using plain language without a predefined script, making it simple and intuitive. Just type “Hi” into CoTester’s chat interface to start a conversation.

Train CoTester on your testing approach in two ways: file uploads and URL pasting.

You can upload user stories in various formats, such as PDF, Word, and CSV for file uploads. These files build a knowledge base that CoTester uses to generate accurate test cases. The more detailed the user story, the better the results.

With URL pasting, simply enter a link, and CoTester will analyze the website in the background. This approach is great for testing specific web pages or forms, as it uses the site’s content to generate relevant test cases.

All data uploaded to CoTester remains secure and isolated within your organization’s instance. Your proprietary information is not shared or used to train the tool, ensuring complete confidentiality.

Features

Maintain a centralized repository for all files and assets, simplifying organization and retrieval; add or remove files using the Document icon and the “+Add More” button without overwriting existing data
Receive detailed test case descriptions and a step-by-step editor for automation workflows, showcasing the sequence of interactions with elements like web forms and using placeholder data for missing inputs
Use the Debug button to execute AI-generated test cases, verify workflows, and adjust steps as needed; view execution logs, screenshots, and results to identify and resolve issues
Manually edit generated test cases by adding or removing steps, saving time on refining workflows; use the chat interface for precise instructions to tweak test cases

Industry-specific applications
CoTester is a domain-specific expert system for testing websites and web apps in various industries, including BFSI, eCommerce, telco, healthcare, and travel.

Other Agentic AI Tools for Testing

1. Testim Copilot

Testim Copilot is an AI-driven coding assistant.

It leverages Generative AI to automatically create JavaScript code for custom testing steps, such as handling complex business logic or dynamic data interactions. Select any JavaScript code and ask Testim Copilot to fix it. It also provides a detailed interpretation of existing test code, facilitating better documentation and reuse.

Features

Empower less-technical testers to ensure quality of complex apps
Debug existing code fast by identifying issues and suggested solutions
Train Machine Learning (ML) mobile locators to work across different device platforms
Locate documentation, videos, and best-practice examples with the help of the AI assistant

How CoTester Stands Out From Testim Copilot
While Testim Copilot focuses on generating JavaScript code for testing, CoTester goes further by offering scriptless test case creation and a conversational interface that requires no coding expertise, making it accessible to testers of all skill levels.

2. Mabl AI

Mabl AI is an AI test automation tool.

Built with the intention to transform software testing, it uses computer vision to detect unexpected UI changes, ensuring and maintaining a flawless user experience. Mabl AI proactively identifies potential flakiness by requesting context to improve test stability.

Utilizing clustering, it organizes accessibility issues by application page, helping to prioritize which ones to address first.

Features

Track page load and test run time with clustering; detect potential regression early
Generate JavaScript snippets to handle complex testing scenarios leveraging natural language
Automatically adapt tests to selector/attribute changes using its expert systems and probabilistic models
Adjust tests for faster and more reliable execution in any environment by letting it learn your app’s timing dynamically

How CoTester Stands Out From Mabl AI
Unlike Mabl AI, which specializes in visual regression and clustering accessibility issues, CoTester supports dynamic test case editing and debugging through its real-time chat interface, allowing greater flexibility and control in testing workflows.

3. aqua AI

aqua AI is a smart, AI-driven test management tool that helps bring your QA ideas to life faster than you could ever write them yourself. With it, you can generate comprehensive test data in seconds, achieving better coverage and enabling superior results.

aqua AI features a chatbot that allows you to interact directly with it—whether you’re seeking QA suggestions, sharing your test drafts, or effortlessly completing entire test case requirements.

Features

Refresh entire items or individual sections in your test script
Upload images, PDFs, or UML diagrams for contextual test case generation
Collaborate with global teams in multiple languages, including Italian, Polish, German, and French
Translate ideas into comprehensive documents by simply speaking or giving a few tips to the AI Copilot

How CoTester Stands Out From aqua AI
While aqua AI emphasizes generating test cases from images or diagrams, CoTester excels in using detailed user stories or live website scraping to create precise, real-world test cases tailored for end-to-end workflows.

4. Kane AI

Kane AI is an AI-powered software testing agent.

Developed on Large Language Models (LLMs), it creates, debugs, and evolves end-to-end tests using natural language. You can generate tests with high-level objectives—on both web and mobile devices, and bank on its built-in auto-heal capabilities. Kane AI tags can be integrated and used within tools like JIRA, Slack, and GitHub to label or organize issues.

Features

Convert your automated tests in all major languages and frameworks, including Selenium, Java, Cypress, and Playwright
Focus on one view while automatically syncing the other with the 2-way test editing feature
Generate bullet-proof tests by turning your actions into natural language instructions
Run generated tests across 3000+ combinations of browsers, OS, and real devices

How CoTester Stands Out From Kane AI
Kane AI focuses on auto-healing tests and mobile compatibility, but CoTester provides deeper integration into organizational processes, such as using business requirement documents and Jira-based user stories for context-driven testing.

5. Tosca Copilot

Tosca Copilot is a Generative AI-powered assistant that helps you quickly find, understand, and optimize test assets in your portfolio through a chat interface. Integrated within Tosca, it boosts app quality and accelerates user onboarding.

With Tosca Copilot, you can easily find unused test cases, unlinked assets, and duplicates. It also enables you to remove unneeded items or perform mass changes.

Features

Get concise summaries of complex test cases and test steps
Seek support from built-in help assistants and 24/7 guides in case you hit a roadblock
Locate unused test cases, unlinked assets, and duplicates for efficient portfolio management
Translate plain language inputs into complex Tosca Query Language (TQL) searches to perform mass changes

How CoTester Stands Out From Tosca Copilot
While Tosca Copilot is limited to optimizing test portfolios within the Tosca ecosystem, CoTester’s independence from rigid platforms allows it to work with diverse tech stacks and offer broader functionality, including URL-based test case generation.

6. Testsigma Copilot

Testsigma Copilot is a Generative AI-driven assistant. It’s built into Testsigma’s own low-code software test automation platform. Using LLMs, it empowers you to create quick test cases from various sources, such as requirements documents, user stories, or even plain text descriptions.

Plus, on Testsigma Copilot, you can eliminate flaky tests by detecting issues with elements and automatically remapping them for reliable results.

Features

Provide test coverage suggestions using minimal input, such as user stories from tools like Jira, design screenshots, or additional context
Generate API test cases directly from JSON inputs; it understands your API structure to create tests that cover all critical scenarios
Receive AI-generated test data suggestions or use custom prompts to create personalized Test Data Profiles
Detect issues with elements and automatically remapping them for reliable results

How CoTester Stands Out From Testsigma Copilot
Testsigma Copilot integrates deeply with its parent platform, while CoTester offers unparalleled versatility with file uploads, URL scraping, and cross-browser compatibility while maintaining strict security and privacy standards.

7. Copado Robotic Testing

Copado Robotic Testing is an AI test automation platform that enables software delivery across web, mobile, UI, API, and desktop.

It takes care of everything, from automatically creating tests based on manual efforts, exploratory sessions, or even existing Selenium tests to guiding testing best practices. You don’t need to install software or maintain hardware. Copado Robotic Testing reduces total cost of ownership without compromising test quality or coverage.

Features

Use out-of-the-box keyword libraries (QWords) tailored to nCino, CPQ, Salesforce, ServiceMax, Field Service, and more
Author test scripts across no-code, low-code, and pro-code capabilities
Capture, report, and transform manual test cases into automated scripts
Pinpoint regressions and share test results across QA and dev teams

How CoTester Stands Out From Copado Robotic Testing
Copado Robotic Testing focuses heavily on Salesforce and nCino ecosystems. In contrast, CoTester provides a domain-agnostic solution with user-friendly test case workflows, making it a versatile choice for a wide range of industries and applications.

What to Consider When Choosing an Automation Tool for Software Testing

Before deciding, understand how a platform fits into your testing strategy. Here are four key areas to evaluate:

Cost-efficiency: Consider the total cost of ownership. Look beyond the license fee—calculate how much time the tool saves, how it reduces manual effort, and the support you’ll need to get it running smoothly.
Industry use cases: Check if the platform has proven applications in areas like healthcare, finance, or retail. If so, then it’s a sign that it can handle specific challenges like high-volume transactions or regulatory compliance.
Tech stack compatibility: Just because you’re adding a new tool to your ecosystem doesn’t mean your existing systems need an overhaul. It should integrate easily with your framework, CI/CD pipelines, and reporting systems.
Customization flexibility: Can the tool be personalized based on your testing strategy and business requirements? If yes, does it come with a steep learning curve? Find out if it supports designing and modifying tests to match your edge cases and priorities.

Ensure Software Reliability, Security, and Usability With Automation

Software testing is most effective when it’s continuous, from design to deployment. Supplement that with an AI tool, and you can ensure it’s done efficiently and accurately regardless of the phase.

CoTester by TestGrid accelerates test case execution and generation by up to 50% and minimizes testing costs by 60%. Plus, you gain a partner that guarantees the best user experience and supports the required delivery speed.

Source: For more details,readers may refer to TestGrid.

Scalable & Efficient: The Advantages of Cloud Automation Testing

TestGrid — Wed, 05 Mar 2025 11:35:44 +0000

Cloud technologies have taken the world by storm, allowing people to save their resources and time significantly for computing needs. They eliminate the need to spend money, time, and effort on setting up and maintaining a physical infrastructure. As a result, most development companies are shifting to cloud technologies to improve their overall efficiency.

This rapid increase in the shift to cloud technologies has stimulated the adoption of automation testing on the cloud. It refers to utilizing cloud computing environments to test software solutions and their individual components for the intended functionality. The integration of AI for software testing has further enhanced cloud-based testing by enabling smarter test automation, self-healing scripts, and predictive analytics for bug detection.

Cloud-based automation testing accelerates the software development process, allowing companies to shorten products’ time to market.

In this article, we aim to introduce you to automation testing on the cloud and its types, benefits, different tools, and major challenges.

Automation Testing on Cloud: Definition, Types, Process, Benefits, & Tools

Let’s discuss all about cloud-based test automation here. Starting with the definition:

What Is Automation Testing on Cloud?
Cloud-based test automation is a software development method that uses cloud technology to accelerate testing and software releases.

This is accomplished by ‘spinning up’ test environments on-demand, allowing your software development teams to continuously develop, test, and release digital products without the need for new infrastructure. As a result, more test environments equal faster test.

This is an excellent method for modernizing your software development, but it is not a one-size-fits-all solution.

If you’re testing your software on-premises and releasing it quickly enough to keep up with user demand, that’s fantastic; keep it up if your existing infrastructure provides enough test environments for your teams to fix bugs while also releasing new features. So that’s how you were.

Cloud-based test automation is ideal for teams looking to reduce testing cycles and increase release velocity. It has obvious advantages, but it can also present some difficulties.

Whether you move all of your testing to the cloud, combine it with your existing infrastructure, or keep all testing on-premises, it must be considered as part of your company’s software development strategy.

Whatever strategy you choose, you must ensure that keeping the lights on does not come at the expense of innovating and future-proofing your organization.

Cloud testing focuses on essential components such as:

Application: testing includes functionality testing, end-to-end business workflows, data security, browser compatibility, and so on.
Network: This includes testing different network bandwidths, protocols, and the successful transfer of data over networks.
Infrastructure: This section includes disaster recovery testing, backups, secure connections, and storage policies. It will help if you ensure that you validate infrastructure for regulatory compliance.

Type of Automation Testing On Cloud:

Cloud computing offers unique possibilities for test automation, extending coverage beyond individual applications to the very platform itself. Here are four distinct approaches to automation testing on cloud:

1. Cloud Infrastructure Testing:

This approach assesses the overall functionality and performance of the underlying cloud platform, including scalability, elasticity, and resource management. Cloud vendors and large enterprises often undertake this type of testing to ensure the reliability and efficiency of their cloud infrastructure.

2. Cloud-hosted Testing:

This refers to utilizing cloud resources to execute traditional test automation, such as web UI or API testing. Leveraging the cloud’s scalability and elasticity allows you to run tests on multiple, geographically distributed virtual machines, improving execution speed and parallel testing capabilities.

3. Cloud-based SaaS Testing:

For applications specifically delivered as Software-as-a-Service (SaaS), automated testing focuses on both functional and non-functional aspects, including:

Functional testing: Validating core functionalities and user workflows
Non-functional testing: Evaluating performance, scalability, security, and compliance with relevant regulations.
Security testing: Identifying and mitigating vulnerabilities in the application and its cloud environment.

4. Hybrid Testing:

This combines elements of the previously mentioned approaches. For example, you might use cloud-hosted testing for web UI automation while simultaneously performing performance testing on the underlying cloud infrastructure. Hybrid testing allows you to optimize your testing strategy based on specific needs and resources.

How to do Automation Testing On Cloud?

Cloud-based automation testing has become a game-changer in the modern software development landscape. It offers benefits like increased test coverage, faster test execution, and access to a wider range of devices and browsers, ultimately leading to higher quality software releases. So, how do you jump into the exciting world of cloud testing? Let’s break it down step-by-step:

1. Define your testing needs:

Before diving into tools and platforms, clearly define your automation goals. What software components need testing? What types of tests (functional, performance, security) are required? Understanding your needs will help you choose the right cloud-testing solution.

2. Choose your cloud testing platform:

Research and compare popular cloud testing platforms like AWS Device Farm, Testgrid, , etc. Consider factors like platform features, supported devices and browsers, pricing plans, and integration capabilities with your existing testing framework.

3. Set up your test environment:

Once you’ve chosen a platform, create a test environment within the cloud. This typically involves uploading your application under test, configuring necessary settings (browser versions, device types), and defining test scripts.

4. Develop your test scripts:

Choose your automation framework (Selenium, Cypress, etc.) and write automated test scripts to cover your chosen functionalities. Utilize features like page object model and data-driven testing for maintainability and scalability.

5. Execute and monitor your tests:

Trigger your test scripts on the cloud platform. Most platforms offer parallel execution capabilities to speed up your testing process. Monitor test results in real-time, analyze logs and reports, and identify any bugs or errors.

6. Integrate with CI/CD pipeline:

For continuous integration and delivery (CI/CD), integrate your cloud testing tool with your CI/CD pipeline. This will automate the testing process as part of your development workflow, catching bugs early and ensuring faster releases.

Benefits of Automation Testing On Cloud:

1. Scalability

Cloud computing has always supported quickly scaling up IT resources as demand changes with minimal disruption. The same is true for cloud-based test automation.

As a result, organizations can increase or decrease their testing capabilities as test requirements change by automating the scaling of test execution using cloud infrastructure.

This solves a long-standing issue in software testing where conflicts arise when teams compete for limited resources.

What’s more important: releasing a much-desired feature or fixing a bug that’s causing problems with an existing version? This is an all-too-common issue when you have a limited number of test environments.

Teams can now spin up test environments for any story branch at the touch of a button and create whatever is required to test a new feature or fix a bug.

Your teams now have instant access to testing infrastructure without the limitations or delays associated with physical hardware or local installation.

2. Improved collaboration

Cloud technology enables teams to collaborate on common goals, whether your coworkers are down the hall or halfway around the world.

By automating test execution in the cloud, your distributed Dev and QA teams can collaborate on the same products in real time, eliminating bottlenecks that have previously defined siloed teams.

Cloud-based test automation provides faster feedback loops, giving your teams better visibility across the CI/CD pipeline as well as actionable insights into how your SUTs perform.

This speeds up software deployment, giving your company a competitive advantage by bringing digital products to market faster than competitors.

3. Future-proofing your business

Future-proofing your business is dependent on your employees having the time and energy to think of new ways to provide superior products to your customers. People waste too much time on repetitive tasks that they do not believe can be effectively automated.

Humans have traditionally been tasked with testing user journeys outside of the ‘happy paths’ that your test scripts can automate, as in exploratory testing. AI-guided exploratory testing vastly improves the test coverage of your app without requiring human intervention.

The increased levels of test automation enabled by automation testing on the cloud as it allow your teams to focus on other, more human-centric testing.

Accessibility and usability can be prioritized to ensure that your applications reach their full potential. You can also devote time to resilience testing, which will allow you to understand how well your applications and systems respond to and recover from stresses and shocks.

4. Reduced costs

Another advantage of moving your test automation to the cloud is that it saves you money. Buying servers, data centers, and other physical hardware on your own is costly, not to mention the associated maintenance and labor costs to keep everything running.

And, if your company is rapidly expanding, the cost of provisioning new servers to meet ever-increasing testing requirements skyrockets.

All of the above are eliminated in cloud environments. In addition, your cloud service provider will cover all hardware, infrastructure, and maintenance costs, removing prohibitive upfront costs.

Because you can create numerous on-demand test environments in the cloud, you can dynamically adjust your testing capacity to meet your needs. In addition, the shortened release cycles enabled by cloud-based test automation boost your teams’ efficiency.

5. Parallelization

Cloud-based test automation improves test execution scalability. Parallelization allows testing teams to run multiple pre-configured test environments simultaneously, simulating various testing parameters of your systems under test (SUT).

This method enables multiple virtual machines to run different software versions of the same application.

Alternatively, the same software can be used on various devices, browsers, and operating systems. When tests in these environments are finished, the results are reported back, and the machines can be spun down and destroyed with no user data retained.

Automation Testing Tools For Cloud

TestGrid
TestGrid is an AI-powered end-to-end cloud and on-premises testing platform. Ideal for high-velocity teams, it significantly improves testing efficiency with no-code automation. Whether it is mobile app testing or API testing, TestGrid is a one-stop testing solution.

Furthermore, the platform provides access to real browsers and devices for testing. Besides websites and applications, it supports scriptless automation testing for voice-enabled devices, like Google Home, Alexa, etc.

TestGrid provides a free Freemium plan, which includes 200 minutes per month for manual testing and an additional 200 minutes for scriptless automation.

Features

Browser and Mobile devices for scalability
Remote access browsers
Remote access to mobile devices for manual and automation testing.
Virtual device USB
On-Prem and Hosted labs
Multi-OS and Multi-Browser testing
Higher Device Coverage Index
24×7 device availability

Benefits

Multiple teams can leverage the lab at the same time
Up to 40% less cost than your existing provider
Much less latency compared to other popular infra providers.
Turn-Key
Parallel execution of test scenarios on multiple devices at the same time
Adding or removing devices is easy and cost-effective per test cycle
Significant Savings on Capital Expenses over a while

2. SOASTA CloudTest

Startups, agencies, and small to medium-sized businesses benefit the most from this solution.

CloudTest is available for free for 30 days. You can request a quote for pricing information.

SOASTA created CloudTest. It is a software testing tool that runs in the cloud. It tests the load and performance of mobile and web applications. It is possible to host it on one or more physical servers or in the cloud.

3. Cloudsleuth

CloudSleuth is best for businesses. It is a Spring Cloud-compatible distributed tracing solution that will aid you in the capture of data in logs.

Spring cloud sleuth will function by incorporating two types of IDs: trace ID and span ID. The Span ID is used for the basic unit of work, such as sending an HTTP request.

Features

From a given trace, you will be able to extract all of the logs.
It will provide you with an abstraction for the most common distributed tracing data models.
Implements Spring applications’ common ingress and egress points

4. AppPerfect

Ideal for small to medium-sized businesses.

AppPerfect is a cloud-based software testing tool for Cloud Load Testing, Cloud Hosted Testing, and Cloud Security Testing. This cloud-testing framework will assist you in testing web applications on various browsers, hardware, and operating system combinations.

Features

It includes features for designing and recording test scripts, distributed testing, scheduling test execution over a cloud environment, viewing and exporting test results, and comprehensive reporting for Cloud Load testing.
It offers fully managed, on-demand, and scalable Cloud Hosted Testing.
It includes features for designing and recording test scripts, scheduling test execution in a cloud environment, viewing and exporting test results, comprehensive reporting, and more.
Cloud Security Testing includes Cloud Security Compliance, Encryption, Business Continuity, and Disaster Recovery capabilities.

5. Nessus

Best for security professionals.

Nessus provides a free trial, Nessus Pro is $4236 for a single year, $8260 for two years, and $12073 for three years.

A vulnerability assessment solution is Nessus Professional. It can provide visibility for your AWS, Azure, and Google Cloud Platform accounts. In addition, it will give comprehensive vulnerability coverage.

Features

Plugins will be automatically and in real-time updated.
It includes pre-configured policies and templates.
The reports can be customized.
Offline vulnerability assessment.

Challenges in Cloud Testing

1. Data Protection and Privacy

Because cloud applications are multi-tenant in nature, the risk of data theft is always present. Therefore, suppliers should provide users with assurances about their data security as part of a cloud testing strategy.

2. Short period of notice

Existing customers are given a short notice period of (1–2 weeks) about upgrades from cloud providers. This is a significant issue when manually validating changes to your SaaS application.

3. Checking for interface compatibility

When a cloud service provider upgrades, the external interface is sometimes upgraded, which can be difficult for some subscribers who are used to the older interface.

Cloud (SaaS) subscribers must ensure that users can select the interface version they want to work with.

4. Data Transfer

Data migration from one Cloud provider to another is a massive challenge because the database schemas of both providers may be different, and it takes a lot of effort to understand the data fields, relationships, and how they are mapped across SaaS applications.

5. Integration of Enterprise Applications

Data integration validation of both outbound and inbound data from the client network to the SaaS application and vice versa is required for enterprise application integration.

Data privacy necessitates extensive validation to reassure SaaS subscribers about data security and privacy.

6. Simulating real-world upgrade testing

The most difficult challenge in cloud testing is ensuring that live upgrades do not impact existing connected SaaS users.

7. Private cloud

Private cloud networks are used by some businesses to strike a balance between the scalability of cloud technology and the security of on-premises solutions.

For example, your company could own and manage a private cloud, or it could exist off-site in a data center where you pay for dedicated machines.

This enables you to use parallelization and containerization to achieve faster software test automation while maintaining greater control over security protocols.

8. Ramp-up time

To get the most out of cloud-based test automation, your teams will need to adjust their workflows, just like with any new technology.

That is why it is critical to properly plan your technology deployment, including ensuring that your people are adequately trained.

You’ll also need to consider how cloud testing will interact with on-premises testing to avoid and plan around potential bottlenecks.

The Current State and Future of Cloud-Based Automation Testing
Recently, automation testing in the cloud has gained immense popularity and adoption by organizations. They are significantly shifting from on-premises testing environments to cloud-based testing environments. The reason is cost-effectiveness, scalability, and flexibility.

Before we move on to shedding light on the future of cloud-based automation testing, let us briefly discuss its current state.

Current State

Various automation testing tools, like TestGrid, Selenium, Appium, etc., are available on the cloud. This means they have web-compatible versions. You can access them across different browsers and their versions.
Parallel test execution significantly improves test execution speed and enhances scalability. This helps organizations reduce apps’ or websites’ time to market and meet the needs of dynamic development.
Cloud-based testing has enabled improved collaboration and remote testing between distributed teams. Any team member can access the testing environment across different locations.
The tight integration of the cloud with CI/CD pipelines ensures the automatic execution of automated tests at every stage of the development lifecycle.

Future of Cloud-Based Automation

Serverless Testing Environments
Another future trend to expect in cloud-based automation testing is serverless testing environments. These platforms can dynamically allocate resources on the cloud. This, in turn, results in reduced cost and overhead for infrastructure management.

Security Testing
With the increasing risk of data breaches and cyber crimes, cloud-based automation testing is more likely to shift its focus to security testing. We can expect the integration of security tools and services with cloud testing environments to detect and mitigate potential risks efficiently.

Blockchain Integration
Automation testing on the cloud may integrate Blockchain technology in various testing processes. This will lead to enhanced security and transparency. Such integration is useful for developing applications for industries where data needs to be safe and tamper-proof.

Sustainability and Green Computing
With the rising importance of sustainability, cloud-based automation testing is expected to focus on optimizing resource utilization. This will significantly reduce environmental impact.

End-to-End Solutions
End-to-end solutions in the testing landscape are expected to incorporate smart orchestration capabilities. Artificial intelligence (AI) and machine learning (ML) will play a significant role in organizing and putting together testing processes, tools, and environments smartly. This integration will undoubtedly lead to efficient cloud-based testing processes.

Conclusion

There are tons of benefits of automation testing on the cloud. Today, we are in a world where everything from gaming to storing data is moving to the cloud.

The cloud is the future, and the combination of automation testing and cloud is the best combination for faster and performing automation testing at scale.

Source: This article was originally published at TestGrid.

A Clearer Future: Trending Screen Resolutions to Watch in 2025

TestGrid — Tue, 25 Feb 2025 14:15:25 +0000

You’ve built an app or a website. It looks great on your screen. But what about your users? They’re definitely accessing it on different devices—phones, desktops, laptops, and tablets. Some work with budget screens, while others have high-resolution displays.

Some use compact laptops; others have ultra-wide monitors. If your app or website isn’t optimized for all of them, you will lose users.

That’s why testing for screen resolutions or sizes is necessary. Mobile app testing ensures that applications function seamlessly across various devices, screen sizes, and resolutions. By thoroughly testing how an app responds to different screen configurations, you can identify UI inconsistencies, performance issues, and layout misalignments before they affect users.

In this blog post, we’ll discuss common screen resolutions and the best ways to test for them.

Common Screen Resolutions in 2025

Before you start testing, you should know all the standard screen dimensions.

Mobile
Mobile screens are largely varied and come in a mix of mini, tall, and foldable displays. The most common phone screen dimensions include:

Standard phones: 1080×2400, 1440×3200, 720×1600
Compact and mini phones: 1080×2340 or smaller
Foldables: 1812×2176 to 2208×1768, while cover screens typically range from 904×2316 to 1080×2520

Tip: Test for narrow and wide mobile views. Ensure your app or website adapts to portrait and landscape orientations, especially foldable ones.

Tablet
A step above mobile, tablets also come in diverse aspect ratios.

Standard tablets: 1200×1920, 1600×2560, 2048×2732
Hybrid devices (Mostly seen in Windows): 1920×1280 to 2880×1920

Tip: In GUI testing, consider how tablet UI elements scale on larger screens. Just like mobile, check for both portrait and landscape orientations.

Desktop and laptops
Desktop dimensions are wide and varied depending on the manufacturer.

Standard laptop: 1920×1080, 2256×1504, 2560×1600, 2880×1800
High-res monitors: 2560×1440 (Quad HD), 3440×1440 (ultrawide), 5120×2160 (5K ultrawide)
Widescreen monitors: 3840×2160 (4K), 6016×3384 (Apple’s Pro Display XDR-level screens)

Tip: Test for high-res and standard-resolution sizes. Avoid stretched layouts.

History of Common Screen Resolutions

Remember the first Macintosh 128K, introduced in 1984? Its screen size was 512 × 342. The most common phone screen size of the first generation of mobile devices (introduced in 1992) was between 3 and 4 inches. Imagine trying to browse a site or watch a video on that!

Screen resolutions have come a long way over the years.

But it has been a phased growth—starting with screen sizes and then higher resolutions with sharper text and better graphics. The 2000s pushed things even further, with 1920×1080 (Full HD) becoming the norm for TVs and desktops. This made screens crisper and more immersive.

In the 2010s, 3840×2160 (4K UHD) took over. Mobile display resolutions, however, followed a different trajectory.

Although 4K never became mainstream on mobile due to diminishing returns in sharpness, battery concerns, and power efficiency, it did manage to hit resolutions like Full HD (1920×1080), QHD (2560×1440), and QHD+ (3200×1440).

Even phone screen sizes have seen their fair share of diversity. In the 2020s, we have 5K, 6K, and even 8K displays packed with so many pixels that we can see details we’ve never noticed before—for example, the fine texture of an image.

In addition, foldable and rollable screens are changing how we interact with devices, while OLED and microLED advancements are boosting clarity, contrast, and power consumption.

Why Screen Resolution Tests Matter

Today’s users prefer a seamless user experience across devices. They want to be able to access apps or browse through websites in the same manner. They also want the visuals to be crisp and high-def. Testing helps you catch such issues early. Let’s take a look at four reasons:

1. User experience
When you test screen resolutions and sizes, you can ensure the content remains accessible, functional, and visually consistent. If the text is too small to read, the buttons are too close together, or the images overflow, users won’t wait for a fix. They’ll simply bounce off the site or avoid using your app.

2. Device diversity
Whether you have an iPhone, MacBook, Samsung, or Dell laptop, you can’t assume a single layout will work everywhere. There are way too many devices, each in several sizes and resolutions. Such testing helps you prepare your site or app for real-world application.

3. Accessibility
People with disabilities will also use your app or website. Some may rely on zooming in and adjusting text sizes, and some might use assistive technologies like screen readers or speech recognition software to read. If your design isn’t flexible, users may struggle to navigate or complete basic actions.

Screen resolution tests ensure text scales properly, interactive elements remain accessible, and layouts stay structured even with custom settings.

Testing Tools and Methods for Common Screen Resolutions

Cover all your bases with both automated and manual testing:

1. Emulators and simulators
You need a more realistic test environment to ensure successful mobile and tablet testing. That’s where emulators and simulators can come in handy. They allow you to test across resolutions and behaviors.

For example, the Android Emulator in Android Studio helps simulate various Android devices.

On the other hand, the Xcode Simulator helps test iPhones and iPads. Microsoft’s Windows Device Emulator (for legacy Windows Mobile) isn’t suitable for modern Windows tablets and 2-in-1 devices. Instead, use Windows Subsystem for Android (WSA) or real devices.

2. Browser DevTools
Most modern browsers have built-in functionalities for testing different resolutions.

Firefox Responsive Design Mode, for instance, enables you to switch between predefined screen sizes and see what’s working and what’s not. Safari Developer Tools provides support for macOS and iOS-specific testing.

Chrome DevTools works similarly. You can open DevTools (‘F12’ or ‘Ctrl+Shift+I’), go to the “Device Toolbar” (‘Ctrl+Shift+M’), and test the app or site’s UX on different screens.

3. Real device testing
While emulators and simulators offer a controlled testing environment, real-device testing is important for accuracy. For example, start by checking how budget, high-end, and foldable devices perform.

Include a standard and high-res tablet to validate its layout adaptability.

For desktop testing, use a laptop and, if possible, an external monitor to see if the scaling works fine. If you test on physical devices, you can certainly spot and fix input responsive problems and any unexpected layout shifts that might not appear in simulated environments.

Best Practices for Ensuring Responsive Design in Common Screen Resolutions

While testing is one part of the process, there are several must-haves you need to keep in mind when it comes to optimizing for common screen resolution sizes.

1. Keep a fluid layout
Use a CSS Grid or Flexbox instead of fixed pixel layouts. The two options allow the content to adjust dynamically to different screen sizes without breaking the design. You can avoid fixed positioning for key UI elements and use relative units like ‘em,’ ‘rem,’ ‘vh,’ and ‘vw.’

2. Prioritize mobile-first design
The key is to design for the smallest screens first and then expand to larger ones, as mobile users make up a significant portion of internet traffic. This will allow you to focus on the most essential functionality first, ensuring a good user experience on the most constrained devices.

3. Optimize for performance
Larger screens tend to load heavier assets, while smaller devices may have limited processing power and slower connections. A well-designed design on a high-end desktop monitor may slow down a budget phone.

Screen resolution testing helps you find the balance—ensuring images, videos, and other elements load efficiently without sacrificing quality.

Moreover, not all users have the latest high-end devices. Many rely on budget phones, older tablets, or slow connections. Here’s how to optimize for them:

Minify files and avoid unnecessary scripts to enhance performance
Serve lower-resolution images for smaller screens to save bandwidth
Compress images and use modern formats like WebP to improve loading speed
Use emulators or real budget phones to identify lag, rendering delays, or UI issues
Use CSS transitions and GPU-accelerated animations (e.g., transform, opacity) instead of JavaScript-heavy effects, as excessive animations can slow down low-end devices.

4. Test for uncommon resolutions and aspect ratios
Don’t just test popular screen sizes. Instead, use emulators or cloud-based testing tools to examine a broader scope of devices. This approach helps pinpoint potential layout issues on less typical devices.

Conclusion

Devices and browsers are highly fragmented, and it can be a bit overwhelming to prioritize what’s important and what’s not. That’s why you should check your website analytics (Google Analytics, for example) to see which devices and browsers your visitors use the most.

This information will help you conduct tests on the most commonly used platforms. Remember, iOS and Android behave differently, and so do the browsers. Therefore, test key interactions on both.

Source: For more details, readers may refer to TestGrid.

Beyond the Hype: Real Benefits of Public Cloud for Modern Enterprises

TestGrid — Fri, 21 Feb 2025 13:03:13 +0000

While few can deny the prevalence of the public cloud in the IT landscape, some businesses remain hesitant to abandon their on-premises systems, private or hybrid cloud solutions, and go “all in” on the public cloud. If you’re in that camp, you need to understand what you’re missing and why the time has come to embrace the cloud.

The public cloud hosting market provides a variety of deployment models that address the needs of businesses of all sizes and industries. One of the biggest advantages is the ability to leverage cloud-based automation testing, which ensures that applications perform seamlessly across different environments without requiring extensive infrastructure. Additionally, AI for software testing is revolutionizing quality assurance by making testing more intelligent, adaptive, and efficient.

In this article, we will examine the advantages of public cloud and how it supports modern testing strategies like automation and AI-driven testing to enhance software reliability.

Benefits of Public Cloud

1. Increase Security

Forward-thinking businesses worldwide recognize the added security benefits of moving to the cloud. However, there is the misconception that if you don’t control and house your data, it’s less secure.

This couldn’t be further from the truth, as evidenced by the fact that on-premises solutions account for the vast majority of data leaks. There are a variety of reasons, why the public cloud is beneficial; including:

Expertise in cyber security: Big cloud attracts the world’s most talented engineers and has the resources to fund large security teams and the most advanced security tools. You gain access to highly skilled teams of IT professionals tasked solely with protecting your data in the cloud infrastructure.
Security advancements: Cloud-native security services are the most advanced. Security innovation is taking place in the cloud and is being tailored to cloud-based solutions.
Regular penetration testing: In Public clouds, regular penetration testing is held to higher standards than on-premises solutions and private clouds. Some private clouds are never subjected to acceptable levels of penetration testing.
Access is restricted: The majority of data breaches are the result of human error. Cloud sceptics believe that keeping their data in-house gives them more control, but the opposite is true. For example, data stored in the public cloud has a minuscule chance of falling into the hands of the wrong people as a result of an employee error. Your risk increases as human control over your information decrease.

2. Global Availability

One of the main benefits of the public cloud is, a fast and inexpensive way to store data in any country on the planet.

For example, if a company wants to reduce latency for its services, it has to choose a provider that stores the cloud in its preferred country.

This can also be used to keep data in a specific jurisdiction. SIM-Cloud, for example, is housed in a data center in Germany, where the law protects against illegal seizures.

Owning a data center of your own outside of your home country is a challenging and expensive endeavor. The business will also need to determine the specifics of the nation’s legislative structure in addition to the logistical issue.

For example, your company won’t have to deal with this problem any longer if you use a cloud solution because the provider has already taken care of everything.

3. High Scalability

The public cloud can be scaled without investing in new hardware and manually installing it in an on-premises data center. The virtual machines can have additional CPU cores, RAM, or storage whenever you need it. The hardware foundation of the cloud is kept in the provider’s data center, and the resources are also made available via remote access.

Small businesses that are unable to expand their IT capabilities by purchasing expensive hardware will find this benefit of public clouds to be especially helpful.

The public cloud can be instantly scaled to fit the task at hand and expanded as the business grows. Compared to a dedicated server, this is much faster and more affordable. You will need to purchase new components and swap out the old ones if you need to scale a physical server.

4. Provider Takes Care of Maintenance

You no longer need to purchase hardware or software when you rent a cloud; this is now the responsibility of your public cloud provider. The provider is also in charge of all elements required for the infrastructure to operate, including power, redundant components, security, cooling systems, etc. So with the Public cloud, your company saves time and money by doing it this way.

This benefit of using a public cloud also applies to renting dedicated servers. However, there will be more maintenance expenses if the business deploys its infrastructure on-site. Rent a public cloud if you want to free your company from these costs while still enjoying all the advantages of the public cloud.

5. Save Money

What are the financial savings possible with the public cloud?

Naturally, it depends.

Some businesses actually save millions of dollars, but if your account needs to be managed better, you might not even see cost savings.

But you can get an idea of how much you can save if you look at why the public cloud saves you money and then look at your own IT environment. You save money by using the public cloud because you have the following:

No investments in capital: Equipment and storage space doesn’t need to be purchased. Setting up a public cloud subscription is inexpensive, and you only pay for the resources you use after that. Your infrastructure spending will change from a capital expense (CapEx) to an operating expense thanks to a public cloud.
There are no upkeep or update expenses: Maintenance is handled by your service provider and is a fixed expense covered by your subscription. In addition, your service provider manages all software updates and includes them in your hosting package, so you or your staff are not required to carry out upgrades.
Pay-per-use billing: You only pay for what you use, which prevents the idling of resources and unnecessary spending. Additionally, you have the freedom to quickly scale up or down, using more computing power when necessary and less when not.
Less expensive energy: By not having internal servers, you save money on the energy they use to run.

6. High Stability

Architecturally, the cloud is a fault-tolerant solution. The virtual machine will use the processing power of another server if a component malfunctions.

The systems will continue to run, and cloud services will continue functioning as usual. Reliable service providers also make use of redundant cloud components. This significantly reduces the possibility of catastrophic failures.

Zones of availability may be used to increase fault tolerance. An availability zone is a separate area of the cloud that makes use of the following:

Network Connections
Independent Computing Instances
A business can double the stability of its services by spreading out the deployment of its systems across two availability zones.

7. Short-Term Investment

Predicting long-term computing power requirements is frequently challenging when a business is still in its infancy. Avoiding solutions that require significant financial outlays, such as on-premises deployment or long-term leasing of another infrastructural solution, is advised in such circumstances.

This problem is resolved by the public cloud’s pricing structure, which allows clients to only pay for resources that are actually being used. By doing this, the client’s business can utilize a scalable and effective computing platform without entering into long-term contracts or investments.

8. Server Redundancy

Private clouds are frequently single-tenant, so your entire company is impacted if the server crashes. Additionally, you are responsible for putting redundancy measures like backup servers and cloud disaster recovery plans in place with on-premise infrastructure.

However, because public clouds support multiple tenants, your resources are dispersed among several servers. Therefore, your applications can automatically switch to another cloud server if one goes down. Your applications and data are always accessible, thanks to it. By doing this, you can lessen downtime and maintain the efficiency of your company.

Conclusion

Overall, public clouds have several advantages over private clouds and on-premise infrastructures. First, they are usually less expensive, easier to maintain, and more secure.

Furthermore, public clouds offer scalability and redundancy, which are difficult to achieve with on-premise infrastructures. So, if you’re considering moving to the cloud, a public cloud might be the best option for your company.

Source: For more details, readers may refer to TestGrid.

Automating Software Testing: Everything You Need to Know

TestGrid — Thu, 20 Feb 2025 13:59:01 +0000

Software is the fulcrum of our modern world. It’s so deeply ingrained in our daily lives that a single bug in our apps is enough to disrupt how we function.

Do you remember when a faulty software update from the cybersecurity company CrowdStrike caused approximately 8.5 million Windows devices worldwide to crash with a “Blue Screen of Death” (BSOD) in July 2024? That caused an estimated loss of $10 billion in damages!

That’s why automated software testing is so important.

While manual testing has its place in the process, you must be able to run specific tests using automation. Automating the management and tracking of tests is called test automation—and that’s what the test this guide is about.

We’ll discuss what it is, its types, tools, and what to consider when implementing test automation.

Let’s dive in.

What is Test Automation?

Test automation involves using software tools, scripts, and frameworks to automate various aspects of the testing life cycle, including test case creation, execution, result analysis, reporting, and defect tracking.

Its fundamental goal is to boost efficiency, accuracy, and consistency by integrating automation into the software development life cycle (SDLC).

Automated software testing can handle many repetitive but necessary tasks and enable manual testing that would be difficult or impossible. For example, you can write Selenium scripts to automate web UI testing or use JUnit or TestNG for automated unit testing.

Or, implement CI/CD pipelines with automated tests. Additionally, cloud-based automation testing enables teams to execute tests across multiple environments without the limitations of physical infrastructure, ensuring broader test coverage and faster feedback loops.

When Should You Automate? (And When You Shouldn’t)

Not every test should be automated; knowing when to use automation effectively is key to optimizing your testing efforts.

Let’s take a look at the tests you should automate:

Regression testing is one of the best candidates for automation because it ensures that new code changes don’t break existing features.
Performance and load testing also benefit because they require running thousands of operations under different conditions, something manual testing can’t efficiently handle.
API testing is another strong example since APIs require frequent validation as integrations evolve. automated testing helps catch issues early, saving time and reducing errors in production.

Now, let’s review the tests that shouldn’t be automated:

Exploratory testing, which relies on human intuition, should be best left to manual testers.
UI/UX testing, where you must assess how a real user interacts with an app, also doesn’t fit well with automation.
Lastly, if your test cases change frequently, such as in the early stages of development, automating them too soon can result in high maintenance costs without much return.

Key Components of Test Automation

A structured set of guidelines and best practices that define how automation scripts are created, executed, and maintained
Automated test cases written using programming languages or automation testing tools to validate application functionality
Managing test data for automation to ensure consistent and repeatable tests
The component responsible for running the automation scripts on different environments, browsers, and platforms
Automated logging and reporting of test results to track pass/fail status, logs, screenshots, and execution time
Stores and manages test scripts, automation framework code, and configurations
Regularly updating test scripts to accommodate application changes and ensuring automation remains scalable

Who Does Test Automation?

The test engineer or software quality assurance person must have software coding ability since the test cases are written as source code. However, some test automation tools enable test authoring through keywords instead of coding.

How to Automate a Simple Test Case

There are many ways to write a test case, and the best-suited role for this test automation is Selenium since it’s a highly versatile framework. Now, let’s write a simple Java program to automate a login function using Selenium WebDriver.

import org.openqa.selenium.By; import org.openqa.selenium.WebDriver; import org.openqa.selenium.WebElement; import org.openqa.selenium.chrome.ChromeDriver; import org.testng.Assert; import org.testng.annotations.Test; public class LoginAutomation { @Test public void login() { // Set the path of the ChromeDriver executable System.setProperty("webdriver.chrome.driver", "path/to/chromedriver"); // Initialize WebDriver WebDriver driver = new ChromeDriver(); driver.manage().window().maximize(); // Navigate to the TestGrid login page driver.get("https://public.testgrid.io/"); // Locate elements and perform login WebElement username = driver.findElement(By.id("email")); WebElement password = driver.findElement(By.id("password")); WebElement login = driver.findElement(By.name("submit")); username.sendKeys("your_email"); password.sendKeys("your_password"); login.click(); // Validate login by checking the URL String expectedUrl = "https://public.testgrid.io/"; String actualUrl = driver.getCurrentUrl(); Assert.assertEquals(actualUrl, expectedUrl); // Close the browser driver.quit(); } }

Explanation of the code

First, import all the Selenium Webdriver packages required to execute the test case.
If you’re using the Google Chrome browser, also import the Chrome driver package.
Structure the test using ImTestNG annotations.
Instantiate the new Chrome driver instance to launch the browser using the System.setProperty command.
On using driver.get() command, navigate to the TestGrid login page
Find elements using the id locator and enter credentials and click the login button.
When you run the code, Selenium will automatically open the Chrome browser and navigate to the login page of TestGrid.
After that, it will log in using the appropriate credentials and check the status of the test case by comparing the URL.
The test case checks whether the login was successful by comparing the current URL with the expected URL.

Benefits of Test Automation

If you’re still questioning why test automation is worth it, here are some automation testing benefits:

1. Faster execution
A manual tester might take several hours or even days to write and execute test cases, especially for a complex mobile or web app. With test automation, you can run thousands of tests across multiple devices and browsers in minutes.

2. Higher accuracy
When manually testing an app, it’s possible typos will error, missed steps will happen, and you’ll get tired of writing test scripts repeatedly every time there’s a modification in the code. Test automation executes the same tests with 100% precision—every single time.

3. Greater test coverage
Do you want to test every single scenario manually? Not realistic. With test automation, you can run thousands of test cases at once, including edge cases that would be impossible to check manually, and that, too, in a few clicks.

4. Cost savings
You need to hire, train, and pay experienced QA engineers for manual testing to run repetitive tests. Test automation has an upfront cost, but over time, it cuts labor costs and minimizes bug-related expenses. It saves you money in the long run.

5. Early bug detection
Fixing a bug after the app has been released is 10 times more expensive than catching it during the build. With test automation, you can run tests every time new code is pushed, catching issues before they escalate.

6. Integration with CI/CD
Test automation allows you to automate the entire dev cycle, meaning you can ensure every change is validated immediately. During manual testing, developers need instant feedback on the code, which can slow down the process considerably.

Types of Test Automation Frameworks

1. Unit testing automation
If you want to ensure every single unit of code of your app, such as a method, module, or function, performs as expected without relying on external dependencies, that’s where unit testing automation comes in.

It helps you catch errors early before they affect the broader system. It also makes it easy to refactor or extend functionality without breaking the app’s existing features. Popular tools for unit testing include JUnit for Java apps, NUnit for .NET, and Mocha for JavaScript.

2. Integration testing automation
Next comes testing the app’s multiple components or modules to ensure they work as desired. Unlike unit tests that run in isolation, integration testing verifies interactions between different app parts, such as APIs, databases, and microservices.

You can detect communication failures, data inconsistencies, and broken endpoints. Common integration testing automation tools include Postman for API testing, RestAssured for automated REST API validation, and Selenium WebDriver.

3. API testing automation
If your app relies on APIs, this test automation enables you to verify backend services and integrations – independent of the GUI implementation.

API testing is performed at the message layer since APIs serve as the primary interface to app logic. This allows you to check if they function correctly, handle requests and responses properly, and maintain security standards.

Tools like RestAssured, Postman, and Karate help in API testing automation.

4. Functional testing automation
One of your testing goals is to ensure all the features related to user interactions, workflows, and business logic perform well. Functional testing automation helps achieve that.

You can simulate real user actions, such as filling out forms, clicking buttons, and navigating through web pages with automation tools like Selenium and Cypress. For mobile apps, you can use Appium to formulate functional tests on iOS and Android devices.

Automating functional tests helps you maintain consistency, test edge cases, and speed up release cycles.

5. Smoke testing automation
Run a set of quick, high-level smoke tests to check whether the app’s critical functionalities are working after a build or deployment and if it’s stable enough for more in-depth testing.

Often called “sanity testing,” it ensures that significant app components load correctly and aren’t broken right from the start. Smoke testing automation is typically executed as part of the CI/CD pipeline using tools like Selenium, JUnit, and TestNG.

6. Regression testing automation
Conduct regression testing automation to ensure new code changes don’t introduce unintended defects in previously working functionality. Whenever a new feature is added, a bug is fixed, or an optimization is performed, it ensures existing features function as usual.

Tools like Katalon Studio, Ranorex, and Selenium help automate regression tests by recording and replaying test scripts across different app versions.

7. GUI testing automation
If your app has a Graphical User Interface (GUI), testing it is essential to ensure consistency in complex visual elements and dynamic behaviors.

Rather than manually clicking through screens, you can record and replay user actions to validate buttons, menus, and forms across different devices, screen sizes, and operating systems.

Tools like TestGrid excel at visual validation. They enable you to capture screenshots and compare them to baselines to fix UI discrepancies.

8. Security testing automation
Security testing automation is the way to go if you want to detect vulnerabilities, security flaws, and threats within an app. Test for vulnerabilities like cross-site scripting (XSS), SQL injections, insecure data storage, and authentication flaws.

Ensure compliance with security standards and prevent potential cyberattacks. Popular security testing tools include Burp Suites, Nessus, and OWASP ZAP. They scan apps for vulnerabilities and generate reports with remediation steps.

9. Performance testing automation
With performance testing automation, evaluate your app’s behavior under different loads, stress conditions, and concurrent user interactions. Check for resource utilization, response times, and system stability under peak conditions.

Tools such as LoadRunner, Gatling, and JMeter enable testers to simulate thousands of users accessing an app simultaneously. They provide detailed performance metrics, such as transaction times and error rates, helping you identify bottlenecks and optimize system performance.

General Approaches to Test Automation

Although there are many approaches to test automation, two stand out:

Graphical User Interface (GUI) testing generates UI events, such as keystrokes and mouse clicks in the app, and observes the changes that result in the user interface to validate that its observable behavior is correct.
API-driven testing is a framework that interacts with an app’s programming interface to validate its behavior. It bypasses the UI entirely, focusing on testing public interfaces of modules, classes, or libraries. This method provides various input arguments and verifies the correctness of the returned results.

Test Automation Methodologies

To automate software testing efficiently, you need a methodology you can trust. Although there are many approaches and strategies to consider, your decision depends on several factors, such as project size, complexity, team skill set, and available tools.

Let’s take a look at the most common test automation frameworks:

1. Linear scripting
If your app is simple and doesn’t require frequent updates, this might seem like an easy solution. Here, you record user interactions with the app and play them back as automated test scripts. Since there’s no need for coding, it’s quick to get started, too.

2. Modular and framework-based testing
Have an app that’s dynamic and demands frequent updates? Go for this approach. Instead of recording everything in one go, break the test cases into smaller, reusable components or modules. Modular and framework-based testing can be sub-categorized into:

Keyword-driven testing, which lets you define reusable actions in a structured format, making automation accessible even to non-programmers
Data-driven testing, which separates test data from test scripts so you can test multiple scenarios easily
Modular testing, which organizes tests into independent units
Hybrid testing, which combines specific elements of the above three frameworks for flexibility

3. Behavior-Driven Development (BDD)
BDD works best in agile environments where multiple stakeholders, such as developers, testers, and business analysts, must collaborate. It allows you to write test cases in plain language, usually using Gherkin syntax, so everyone on your team can understand.

4. Continuous testing
This involves executing automated tests as part of the software delivery pipeline. It helps obtain immediate feedback on the business risks associated with the app whenever changes are made to its code or configurations.

What’s Included in Software Testing Automation

Testing tools can automate various tasks, such as test data creation. product installation, GUI interaction, defect logging, and problem detection (e.g., using parsing or polling agents with test oracles). However, they don’t necessarily automate the end-to-end testing process.

Therefore, when considering test automation, you must meet key requirements:

Platform and OS independence
Support for a distributed execution environment (distributed test bed)
Support for distributed applications (distributed SUT)
Common driver compatibility (e.g., Ant or Maven in Java) to integrate with the workflows of developers
Extensibility and customization (Open APIs for integration with other tools)
Data-driven capability (input data, output data, metadata)
Customizable reporting (DB access, Crystal Reports)
Easy debugging and logging
Version control friendly (minimal binary files)
Support for unattended test runs to enable integration with build processes and CI servers
Email notifications (e.g., bounce messages)

How to Set Up Test Automation: A Step-by-Step Process

If you want test automation to yield positive results in the long run and not break every two days, you must set it up in a structured manner. Let’s take a look at how you can do that in real-world scenarios:

1. Define your test scope and priorities
First things first – why are you automating tests?

Is it to:

Reduce manual effort?
Speed up regression testing?
Improve test coverage across different browsers and devices.

Automating everything is neither cost-effective nor practical, so you must prioritize test cases. For example, in eCommerce testing, you would want to test core functionalities like adding items to the cart, completing checkout, or applying discount codes.

These workflows are repetitive and must work flawlessly every time. Automating these tests can ensure they run consistently across different releases.

2. Choose the right automation tools
Once the test scope is finalized, the next step is to select the right automation testing tools for the task. This will depend on several factors:

The app you’re trying to test
The programming language your team is comfortable with
How well the tool integrates into your CI/CD pipeline

For instance, if you’re testing a web app, Selenium is a great choice because it supports various programming languages and browsers. However, Appium is a better option for automating tests for Android and iOS apps.

3. Build a strong automation framework
Once you decide on the tool, it’s time to set up the automation framework. Think of scalability from the start. Even if you have just 10 test cases today, design your framework to support 500 later.

For example, if you’re testing a travel booking app, leverage a data-driven approach where the same script runs multiple tests using different input data, such as booking flights for different dates, cities, and passenger types.

Test scripts can quickly become messy, difficult to manage, and hard to update without a framework.

4. Write and organize test scripts
The next step is to write automation scripts. When doing so, remember the following tips:

Be modular; break it down into reusable components
Avoid hardcoding values; instead, store data in separate files
Use clear naming conventions so that it’s readable for future testers

For example, if you’re testing a login page, why write separate scripts for different user roles, such as admin, user, and guest? Instead, create one login script that takes different credentials from an external data file. This process will also save you a ton of time.

5. Integrate with CI/CD for continuous testing
Test automation triggers tests automatically whenever new code is pushed, catching bugs early during the build.

For example, if you work in a DevOps test environment, integrate Selenium tests into Jenkins or GitHub Actions. This means every time you commit a new code, tests will run automatically and report back.

6. Maintain and update test scripts regularly
Automation isn’t a one-time setup. As your app grows, so does the number of automated test cases. If not optimized, test suites become slow and unmanageable. For example, running a full regression suite with 1,000+ test cases may take hours.

For instance, if you redesign the checkout page, your old automation scripts might fail because element locators have changed. Therefore, to keep scripts stable:

Use parallel test execution
Implement test case prioritization
Leverage self-healing automation tools

You must also review failed tests regularly, refactor test scripts to boost efficiency and keep test cases updated with app changes. And if a test keeps failing for no real reason, fix or remove it—don’t let flaky tests ruin your automation confidence.

Top Test Automation Tools in 2025

Test automation is an investment that pays off in the long when applied to stable and repetitive test cases. However, ongoing manual effort is required for script development, maintenance, and result analysis. That’s why you need a tool that can help you test your app efficiently.

Here’s a quick introduction to some popular options for automation testing.

1. TestGrid
TestGrid is an AI-powered end-to-end testing platform that helps you automate testing across different environments—web, mobile, and APIs—without requiring any coding expertise.

Since it’s a cloud-based platform where you can run automated tests on real devices and browsers, you don’t need to invest in any in-house device labs. You can, instead, execute tests in parallel, significantly reducing test execution time and ensuring reliable results.

In addition, TestGrid boosts the entire automation process by integrating with CI/CD pipelines. This means you can create, execute, and maintain test scripts with minimal effort, making it easier to scale your testing efforts.

Whether you’re focused on functional, performance, or security testing, TestGrid ensures that automation is faster, more intelligent, and cost-effective in the long run.

2. ACCELQ
AccelQ is a cloud-based platform that uses AI to automate and manage testing for web, mobile, API, database, and packaged apps. It uses a keyword-driven approach to build tests that require learning natural language prompts.

ACCELQ delivers user-friendly, centralized reports that highlight data discrepancies and errors. It complies with industry standards, including GDPR, PCI DSS, and more.

3. Appium
Appium is an open-source test automation framework primarily used to test Android and iOS mobile apps. It allows you to write automated tests for native, hybrid, and mobile web apps using various programming languages—like Python, Ruby, or Java—in a single platform.

4. Playwright
Developed by Microsoft, Playwright is an open-source framework for cross-browser automation and end-to-end web app testing. Its tests run locally on Linux, Windows, and macOS—locally or on your continuous integration pipeline, headless or headed. Playwright supports several programming languages, including JavaScript, TypeScript, .NET, and Python.

5. Selenium
Selenium is an industry-leading open-source framework for web app automation. It supports multiple programming languages like Python, C#, Java, and JavaScript. Due to its cross-browser testing compatibility, it’s easy to run tests across different browsers, such as Chrome and Safari.

6. Cypress
Cypress is an open-source JavaScript-based web app testing framework. It allows you to test modern apps directly in the browser. Its simple yet powerful API runs as fast as your browser can render content. Installing Cypress and writing your first passing test is a breeze. Cypress doesn’t require installing or configuring servers, drivers, or other dependencies.

Remember: Test Automation is an Ongoing Journey

As your app and testing needs evolve, so will your automation framework. Consider test automation as an investment for improving the quality and efficiency of your software development process. Implement the strategies and best practices outlined in this guide.

You’ll be able to streamline testing and accelerate releases—and deliver an app your users like to use. Good luck!

Source: For more details, please refer to TestGrid.

Mastering Regression Testing in Agile: Best Practices for Bug-Free Deployments

TestGrid — Tue, 18 Feb 2025 12:52:23 +0000

In agile development, regression testing allows development teams to focus on new functionality while preserving product stability with each new product iteration. Teams do regression testing to ensure that tested software continues to function after each change.

Regression testing is the “stepchild” of agile testing, and while it is disliked by many, it is required to achieve the high velocity that agile teams aspire for.

Regression Testing in an Agile Environment

Testing must evolve with each sprint in Agile, and testers must ensure that new modifications do not impair the application’s existing functionality. Regression testing is the term for this.

Regression testing verifies that the application’s previous functionality is still functional and that new updates haven’t introduced any new bugs. Instead, regression testing should be used whether there is a minor localized update to the software or a broader modification.

Teams must ensure that new code does not conflict with previous code and that non-changed code continues to function as planned.

There are numerous build cycles in Agile, and the application is updated regularly. As a result, regression testing is critical in Agile. Many teams now rely on cloud-based automation testing to streamline regression testing, allowing for faster execution across multiple environments without compromising quality. This approach helps teams manage frequent updates while ensuring product stability.

Therefore, a testing team should establish the regression suite from the beginning of product development for successful regression testing in Agile. Along with development sprints, they should continue to improve them.

Importance of Regression Testing In Agile Development

In an agile framework, the team focuses on sprint-specific features. While the team is focused on one product area, they cannot be expected to consider the risks their changes may cause to the overall system.

A regression test will show areas affected by the team’s recent changes across the codebase if coverage is sufficient.

Regression tests should be done immediately after changes are made, preferably automatically as part of the build process. However, when input arrives later than expected, the team may have already begun modifying other system parts.

Regression Testing In Agile Methodology

Regression testing can be done in three different methods. First, your circumstances will determine your method, the size of your codebase, the number of testers on your team, and the available resources.

Re-test everything, which entails rerunning all existing tests against the updated source. This would isolate regressions if the tests were well-designed. This strategy, however, consumes a lot of resources and may not be feasible for a massive codebase.
Selective re-testing—occasionally, it’s possible to find a subset of your existing tests that can cover all or nearly all of your codebase’s “moving parts.” It’s only a matter of rerunning that select set to find regressions throughout the codebase.
Re-testing is prioritized and is utilized on huge codebases. Priority tests focus on code routes, user activities, and areas of functionality that are likely to have defects. After completing these tests, you can move on to the remaining tests.

Regression Testing In Agile Scenario

Regression testing, on its own, ensures that past functionality continues to perform as intended after the inclusion of a new feature. In an agile development method, regression testing ensures consistent results at the end of each sprint by evaluating the impact of newly produced segments on the application, assuring the overall system’s smooth operation.

New features are introduced every sprint in an agile development process, and the regression test suite should be kept up-to-date at all times to guarantee that all parts are working correctly after each sprint.

Test cases corresponding to all previously tested and stable features should be introduced to the test suite regularly, and test cases that are no longer relevant should be removed. All of the test cases that need to be modified to reflect additional new functionalities should be done.

As a result, for effective regression testing in an agile environment, a testing team must generate regression test suites from the beginning of the development process and continue to build them as the sprints progress.

Before starting regression testing, you must first create a regression test plan. Here are a few phrases that are crucial in this process:

Test cases are chosen for execution
Identifying improvements for re-designing test cases.
Estimating the time it will take to run a regression test and laying out the test cases that will need to be automated in the regression test plan.

When To Do Regression Testing In Agile

It may take trial and error to get the correct cadence for running your entire regression testing suite. However, not every change in your code is significant enough to warrant executing your complete suite.

However, because you’ll be more familiar with the features in the new version and the volume of code you’re testing is lower, the more frequently you run the suite, the less time it will take to review the results and correct any errors.

Every time you merge a branch back to master, you should run your whole regression suite. If that isn’t practicable, there are a few general guidelines to follow. In the following scenarios, you should always run your regression suite:

When it comes to adding new features to a product (for example, adding time-tracking to an invoicing app).
After resolving flaws in an existing feature, check if the bug fixes caused any new regressions.
Following the implementation of a significant software upgrade (for example, switching to the latest version of Ruby on Rails).
Before releasing code to the public.

Regression Testing Strategy In Agile

1.Using Sanity And Smoke Test Cases
Testing teams can save time by performing smoke and sanity tests before regression tests. However, before the extra testing of a new release, sanity testing is a run-through of an application’s basic functionality, which informally checks that functionality works as expected.

To execute smoke testing, you’ll need a subset of test cases that can quickly run and test primary and core product workflows, including startup and login.

Sanity tests and smoke tests can be used to quickly determine whether an application is too faulty to require further testing, such as regression testing.

This is far preferable to running regression tests on a product that doesn’t load or allow login and then attempting to figure out why hundreds or thousands of regression tests are failing.

2.Identifying Places That Are Prone To Errors
Include the most often failed test scenarios. Some parts of the application are so prone to errors that even simple coding changes can cause them to fail.

For example, you can keep track of these failing test cases during the product cycle and put them in the regression test suite.

3.Prioritization Of Test Cases
In a risk-based approach, a testing team picks test cases that cover the application areas most affected by project modifications. They also assign a priority to them. For example, regression testing concentrates on product categories with the highest perceived risk of quality problems.

Prioritize the test cases based on the most important and commonly used features. For example, you may decrease the regression test suite, save maintenance work, and run regression tests faster and more frequently by selecting test cases based on their priority.

4.Investigating Bug Reports
Some regression testing software works in tandem with error-tracking software. This gives you detailed information about what happened during a regression test; such as if it failed, why it failed, and which line of code was affected.

During regression testing, error tracking tools can also help you get screenshots and other analytics about failures; which can help you discover and debug the problem.

5.Communication
To track and assess changes in requirements, testers should communicate with product owners. In addition, they should connect with developers to figure out which modifications were done during each iteration.

Agile Regression Testing Best Practices

Testers must be included in agile teams from the start of each sprint. This is because to structure tests and maintain them up to date right from the start.
Every time changes are made to the software under development. Regression test suites must be updated. After each sprint, they review it to streamline quality assurance, removing obsolete test cases and adding pertinent ones.
Automation is non-negotiable if you speed up regression tests for Agile sprints. Begin with an automated regression test script, then tweak it when new features are added. As a result, QAs must focus on making incremental changes with each sprint rather than running the tests.

Challenges of Regression Testing in Agile

1.Expanding Regression Test Suites
As new features are added, the size of regression tests grows. However, the suites can soon grow cumbersome and unmanageable if there isn’t enough automation and team organization.

2.Communication Requirements
Testers must communicate with developers, business analysts, users, and other stakeholders regularly. They require this to become acquainted with the features being developed and user expectations. This may be challenging to achieve and maintain for small teams in particular.

3.Test Case Maintenance Pressures
As test suites grow, they must be maintained, inspected, and updated after each sprint. Tests that are no longer in use must be removed, and new tests must be created and added. If a project necessitates many iterations, this might be tedious.

Conclusion

Regression tests are an essential part of any development process since they ensure that incremental development does not disrupt the program at any point.

Therefore, to create defect-free, high-quality user experiences in competitive schedules, designing and executing regression testing to adapt to Agile needs combines the best of Agile principles with the safeguarding mechanisms of extensive testing.

Source: For more details, readers may refer to TestGrid.