Forem: Hatica

11 Slides CTOs Must Present In Their Next Board Meeting

Naomi Chopra — Wed, 31 Jan 2024 10:49:44 +0000

Board meetings could be a little tricky for a CTO.

In an internal meeting, your audience would mostly comprise only internal team members and CXOs. So, in such meetings, you can talk about the technical processes of XYZ project(s), successful milestones of engineering teams, new initiatives, completed projects, engineering metrics, etcetera.

This changes completely when you’re presenting as a CTO to the board of directors.

Attending board meetings as a CTO is not similar to attending another internal meeting.

You can no longer talk about your “engineering things” in isolation or prevailing work silos. There is a tacit expectation of presenting software that was shipped, the velocity at which the work is getting done, customer experience and feedback on these newly released product features, the overall cost of software development, and the ROI of investments in the engineering initiatives.

Net-Net A CTO’s rendezvous with The Board of Directors is going to be about:

The ROI
The Impact
The Future Growth Projections

If there are concerns around technical governance & operational workflow challenges, then the board would want to hear your mitigation strategy for the same.

You might be thinking, “Isn’t that what a CEO should be talking about?”.

And you’re right.

The CEO would most probably touch upon these in detail in the board meeting. But you need to dive a little deeper and help the board of directors understand how aligned is engineering to the business. Basically, you would make the CEO’s promises believable when it comes to the engineering side of things in your organization.

So, what should you present?

Argh! To your disappointment, we are not going to talk about including a welcome slide, intro & agenda slide, or the thank you slide at the end of the board meeting presentation. Sorry. We shall only talk about the meat of the presentation. The core CTO & engineering stuff.

Here are the slides/deck that you as a CTO must include in your board meeting presentation to the board of directors:

11 Essential Slides for a CTO to Present in Board Meetings

Remember, in a board meeting, as a CTO, you’ve only two real friends. No, not the CIO or CEO. But Data & Context.

So, know your data. And learn to add context to your data so that board members could easily comprehend what you’re talking about and why it is important.

Having said that, here are the slides to include in your CTO presentation deck for the board meeting:

1. Your engineering Health: Summarize Key Engineering Metrics & Recent Launches

Slide 1: Ideally, start with the news about your recent successes. It could be:

A major launch
Moving an entire business vertical to the cloud
Opening up new data centers or offshore development hubs,
Winning industry awards, etc.

Also, help the board members fathom how these engineering advancements map to the overall business charter. Share how these achievements help improve:

Reliability for a better customer experience (CX)
Scalability to serve more customers
Cost-efficiency

Slide 2:
Next, talk about how the engineering team has solved one/more major issue(s) which has been a big pain - on the To-Do list - for a long time. For example:

How did you lower the application crash rate?
How did you meet the data center outages SLA?
How have you significantly minimized the peak-hour downtime issue?
How did you solve the bot attacks mitigation challenge?

Again, show the board members how this translates into the bottom line. For example, bot mitigation helps not only protect your platform data and safeguard your users, but also fixes the cost of operating your IT infrastructure by stopping the bots from accessing your platforms, thus improving cost-efficiency. Also, this would mean more resources are now available to serve real users, thus improving platform speed and reliability.

The point is to break the numbers for the board members. Help them see the business value of engineering initiatives. Avoid subjective information, and always speak in numbers. It helps. A lot.

Slide 3:
The first two slides were for warming up the board of directors. Now, you’re ready to give them insights into the organization’s engineering health.

Beware, do not clog your CTO presentation deck with jargon or irrelevant KPIs & metrics that do not interest the board members. Only include the ones that are of interest to the board executives.

What engineering metrics should you include in the CTO’s board meeting presentation deck?

To answer this, ask yourself the following questions before including engineering metrics & KPIs in your CTO deck for the board meeting presentation?

Does this metric help demonstrate how technology is supporting our organization’s mission & strategic goals?
Does this metric impact the bottom line — revenue, profits, and cost savings?
Does this metric reflect our future readiness?
Does this metric capture the ROI of recent technology investments?

In general, it’s a good idea to have the following engineering metrics categories on your deck for the board meetings presentation as a CTO:

Show Performance Metrics

Give insights into the overall performance of your IT infrastructure systems & applications. Talk about the enhancements or deterioration in the following:

Response time (how fast your applications respond to user requests)
Uptime & availability (how available your system is to users)
Throughput (transactions handled per second)
Scalability metrics (elasticity i.e., ease with which you can scale up/down your infrastructure, failure resilience score)

You don’t need to be explaining the HOW, just present the numbers to the board members and map them to ‘how it helps you as an organization to win more happy customers’.

For example, you don’t need to tell how effectively you’re using load balancers for distributing traffic across resources, or how you have implemented algorithms to improve concurrency.

Just talk about:

How many customers you can serve per minute during peak hours?
How many transactions can be completed per second?

Then you can throw some light on:

How these numbers looked earlier?
What was the failure rate in transactions - before and after?

And then, explain what can be expected with the newly upgraded efficiency i.e., give the board the estimates about what the numbers may look like now as the transaction failure rates have improved. It’s better if you already have that data, and are not making predictions.

How do you collect these performance metrics to include in the CTO presentation deck?

For collecting these metrics, depending on your internal engineering stack, you could use application performance management and monitoring tools like Pingdom, New Relic, Dynatrace, Apache JMeter, Gatling, LoadRunner, AWS Cloudwatch, Prometheus, HPA, Gremlin (chaos engineering tool), etcetera. Ask engineering managers for help with collecting the metrics.

Quality & Reliability Metrics

Talk in numbers about bug counts, error rates, system downtime/availability, test coverage, Service level agreements (SLA) targets, fault tolerance, data integrity, security attacks & resilience.
SLA metrics include your security, data, governance, and compliance-related metrics. This also encompasses security incidents, backup, and recovery metrics.

Again, give the board of directors a quick overview about how increased test coverage helps in decreasing bug counts & error rates, which improves system availability, and ultimately helps meet the business outcomes i.e., a better CX & more revenue.

As mentioned earlier, the metrics depend on the industry, if you’re a hardware OEM org or provide IaaS, or PaaS solutions to your customers, SLAs are relevant to you. But in a retail consumer-facing startup like an eCommerce business or a stock analysis platform, your vendors are responsible for SLAs, not you.

How do you collect these quality & reliability metrics to include in the CTO presentation deck?

You could use application performance monitoring tools (AppDynamics, New Relic), Log management tools (ELK stack, Splunk), infrastructure monitoring tools (Pingdom, Nagios), testing & test management tools (TestRail, OWASP ZAP), and data validation tools (DBUnit), and/or chaos engineering tool (Gremlin).

Slides 4 & 5:

Engineering Efficiency metrics

There are 100s of engineering efficiency metrics that you can track (say ‘Hi’ to Hatica), but you need not share all of them in the board meetings.

For example, Hatica helps VP of engineering, engineering managers, and CTOs of startups, SMEs, and enterprises track about 130+ engineering metrics & KPIs. But neither you nor the board members would have time to go through each one of them. So, here is a prioritized list of engineering efficiency metrics with an impact on the bottom line that you, as a CTO, can talk about in the board meetings:

Mean Time Between Failures (MTBF) is the average time in which your system tends to fail, and needs the attention of the SRE teams or the DevOps teams. The higher it is, the more reliable is your system, and thus the better the CX, and the lower the development & maintenance costs.
Mean Time To Detect (MTTD) and Mean Time To Repair (MTTR) reflects how fast your team identifies & mitigates the issues. These should be ideally as low as possible. That would mean a quick remedy, and thus it would minimize the negative impact on the bottom line i.e., minimal revenue loss.
Mean Time Between Deployments (MTBD), Deployment Frequency, Development velocity, the rate at which PRs Merge, and other velocity metrics speak loads about your engineering efficiency.

Deployment metrics shouldn’t be looked at in isolation, as then it is nothing but a vanity metric. But when you help the board team get a broader picture by looking at these metrics holistically i.e., MTTD, MTTR, CFR, MTBF, Code Churn, and Incident Frequency together, then it gives clear insights about the effectiveness of the SDLC processes.

If you can do some maths to find out how x% improvement in how x% improvement in Dora metrics (DF, MTTR, CFR) impacts the bottom line in terms of the number of transactions increased, y% revenue increase etcetera (DF, MTTR, CFR) impacts the bottom line in terms of the number of transactions increased, y% revenue increase etcetera, it will be brilliant.

Change Failure Rate (CFR) gives you the probability of deployments that would need hotfixing, patches, or rollbacks in the future. The lower the CFR, the better. Low CFR means high-quality code is being shipped, and thus low tech debt, and hence low cost of maintenance, and improved system reliability. So, if you’re improving CFR, do bring this to notice, and applaud the team responsible for this.
Code Churn is a measure of what percentage of code gets deleted after being merged. It’s not the same as refactored code, which is done to improve readability or performance. High Code Churn means poor requirements gathering, scope creep, or exposes the inefficiency of engineering talent & code review process. Low code churn is good for reducing development costs.
Incident Frequency (IF) is the average number of incidents that happen in a time period. Incidents could be software crashes, network interruptions, data loss, power outages, third-party vendor services failures, human errors, server outages, database errors, etcetera. Instead of diving into each of these, just give an overall view. And demonstrate the ROI on investing in low incident frequency. In some cases, after a particular IF level, investments toward preventing incidents are costlier than responding to them.
Cycle Time is how long it takes the code to go from the developer’s first commit to the deployment. It includes coding, review, and rework time. Companies strive for lower cycle time. Normally, cycle time is under 4 days for most companies. Low cycle time displays the efficacy of your engineering teams & processes.
Data Metrics, to give insights into the ROI of investing in data. For example, talk about the percentage increase in revenue attributed to data-driven decisions, and improvement in the accuracy & precision of your predictive models with the increase in data sources integrated for analytics.
ESG Metrics, to demonstrate how your engineering practices & initiatives have resulted in decreased energy consumption, hence low contribution to the carbon emission (carbon footprint reduction), and the impact on operational costs. Basically, tell the board members how green & sustainable is your engineering supply chain. Celebrate wins like LEED green certifications for your data centers, etcetera.
Compliance and regulations metrics to share compliance audit results, the number of data & regulatory compliance requirements met, the number of data breaches & compliance violations, etcetera.

2. Team KPIs — Developer Productivity, Developer Experience (DX), and More!

In your Slide 6, share the team & developer coding experience-focused metrics, such as:

Active Contributors count who are committing to the code base. This reflects effective human talent utilization & engagement. The higher, the better.
Developer Coding Days is a measure of how active are developers on average. Ideally, it should be 100% for a flawless engineering process & culture. Low coding days expose process or engineering inefficiency. And that means, there is a scope of optimization where costs could be saved, or developer experience could be improved. If it is high, it is awesome. Else, share with the board the reason, the strategy, and the investment needed to improve this.
Code churn (the same as above), if high, could be a signal for inefficient coding skills or process inefficiencies like requirement gathering. This results in poor developer productivity and spoils the developer experience (DX), hence hitting the bottom line negatively. You should have an idea of the possible reasons, and how to mitigate them.
Pickup time, active reviewers average, reviewer reaction time, submitter response time, involvement, and other code review-related metrics are measured to demonstrate how well the code review process is implemented in the organization, across project verticals. Explain how adherence to the ideal numbers for these metrics means an improved developer experience, and high-quality code yield, and also contributes to higher maker time. Thus, positively influencing the bottom line by reducing the costs of dealing with technical debt, and employee churn.
Maker time could be the highlight of this slide as it tells what percentage of uninterrupted time a developer gets on average to actually do the coding. A high maker time positively impacts metrics like PR throughput per contributor, Lines of Code (LoC), coding time average, coding days, and code review metrics. It won’t be an exaggeration to say, the higher the maker time, the better the developer experience.

Read why you as a CTO should be championing investing in a better developer experience for your board of directors.

A low code churn (explained above) may mean improved prowess of engineering talent. So, you can put that as well in this slide.
Besides, you can talk about the percentage of talent who participated in tech training programs to upskill and share metrics about tech talent retention. These too reflect a well-engaged culture, which is a sign of good developer experience.

3. Market Dynamics & The current Landscape of the Industry

Slide 7 should give a quick overview of:

What’s buzzing in the industry?
Competitor research insights
Future of technical landscape in your industry, and basically noteworthy emerging technologies from the Gartner hype cycle.
New Feature/Product Introductions to match the paradigm shift.

This helps stakeholders & board of directors to grasp a good understanding of where the industry is headed. You may take the help of a VP, EM, or tech lead to prepare this slide. Salespeople may also come in handy. This is a good exercise to nurture the next generation of internal leaders and to be a floor leader even as a CTO.

Slide 8 should be about Where do you stand — your tech stack, talent pool, training programs, etcetera

Based on what you’ve included in the industry landscape slide 7, help the board of directors to:

Take a peek into your current organization tech stack.
Understand how prepared you are technically for the future, and the strategy for tackling any anticipated or unexpected disruption, or tapping the emerging market opportunity.

4. Innovation & Research — Your Tech Radar

Slide 9 should be a continuation of your last slide. Here, you can talk about:

Your initiatives targeted at growth, adaptability, innovation, and disruption. Like Thoughtworks, you can share your own tech radar, where you show your prowess in different technologies, and what you plan to adopt in the future.
Also, you can share the number of patents filed, intellectual property assets created by the team, open source leadership, etcetera.

5. Budget & Resource Allocation — The Sweet Spot of CFOs, and CEOs

Slide 10:

Lastly, take the help of your VPs from different engineering departments or geographies, and prepare a slide where you could demonstrate how you’ve been allocating financial resources and its ROI.
Also, advocate for new investment opportunities for improving engineering outcomes.
Find a balance of investments, and allocation with your CFO– building quality products without offshoot budgets. One way to do so is via capitalizing some of your R&D costs. Keep your finance team in the loop about presenting capitalized vs non-capitalized expenses, and how they impacted the overall development costs. It’s one way to shoot two stones with one arrow: Free up budgetary considerations for your engineering team, while keeping the C-suite, and board members happy, and enrolled.

Slide 11: Feel free to conclude the presentation on hope with announcements of major upcoming launches, and/or small celebratory news, and/or by highlighting the major positive takeaways from the presentation. Or the best, applaud some of the emerging engineering talents within the organization.

Building a Strong Engineering Narrative for the Board

Putting up a deck could be very challenging, especially for a CTO, as you’ve no time for it. But you can work with engineering managers & VPs who could help you with it. If you’re using Hatica, then it’s almost a cakewalk, as it tracks almost 90% of the metrics you would need for the decks. Almost all the engineering efficiency metrics that you would need to present in the board meeting as a CTO can be easily collected from the Hatica dashboard. In fact, you would get ready-made aesthetic charts to include in your board meeting presentation deck.

Besides,

Hatica is a gift for every engineering team to get unprecedented visibility into engineering processes & operations. It helps in unlocking developer productivity and delivering enchanting developer experiences (DX). Book a demo to see why 20k+ engineers are using Hatica for driving engineering excellence. Also, if you do not want to miss out on more engineering insights, subscribe to our blog.

How To Get C-suite Buy-in for DevEx Investments

Naomi Chopra — Tue, 16 Jan 2024 11:52:35 +0000

94% of organizations say that they have a DevEx strategy in place, while only 25% believe that the strategy is mature and delivers value. Despite all the opportunities that DevEx brings, the investments made toward a joyful DevEx are still sluggish.

But this needs to change.

With a 64.4% burnout rate in IT, economic headwinds, and the great resignation continuing, retaining engineering talent is a challenge for CIOs & IT leaders. Hence, it is more important than ever for the top leadership to ramp up the investments into improving the developer productivity, the developer experience (DevEx), joy, and well-being.

You, as an engineering manager, CTO, or CIO, might already know the impact/benefits of investing in developer experience. But the challenge is to get the rest of the C-suite to buy-in the DevEx strategy, especially the CFO, CEO, and the board of directors.

Why so?

First, the challenge is such initiatives aren’t just cost-intensive, but also demand a cultural shift in engineering processes & how people work. This steep change curve is what scares the C-suite.
Second, the DevEx advocates don’t talk in the language the C-suite understands, i.e., Financial Numbers, aka Money.

Read this insight to find out how you can overcome the above challenges and get your CFO, CEO, and investors to buy into your DevEx initiatives strategy.

How to Pitch for DevEx Investments to the CXOs?

Speak the linguistic dialect of the C-suite executives. Help them see that the DevEx strategy is for the business's sustainability & profitability.

Note: If your CTO is not convinced about DevEx initiatives, read our blog on 7 reasons to give to your CTO for investing in developer experience. The current blog is more about winning the trust of the CEO, COO, CMO, investors & other stakeholders who would put the rubber stamp on the DevEx initiatives proposal.

1. Quantify all the Value Propositions

Let’s say, in your proposal, you’re suggesting investing in a log analyzer tool like Splunk. Then try the 2-step strategy to win c-suite trust & get buy-ins.

Highlight how manual issue identification, root cause analysis & diagnosis can take hours. Especially, for large distributed systems. Increasing MTTD and MTTR and slowing the velocity metrics.
Explain how manually scanning log files kills developer productivity.
Prove the utility of a log analyzer tool in minimizing developer interruptions, and maximizing maker time.
Show the average time that a developer spends trying to identify the issue without a log analyzer, vs. the time spent with a log analyzer.

The same applies to all the tooling-based investments. You have to explain the problem it solves. Then be it IDEs, browser dev tools, AI code generator tools, code quality & review tools, CI/CD platforms, software development & testing frameworks, infrastructure orchestration & monitoring tools, security tools, communication & collaboration tools, or code documentation tools.

But all this is what a CTO would buy into. To have the CEO & CFO on your side, your narrative needs to be further personalized & revenue-rationalized.

Map all the benefits of investing in developer experience to the business’s bottom line. Share with the c-suite the findings of a Forrester report.

2. Communicate The DevEx Impact on Engineering Productivity & Project Success

Investing in devEx initiatives has helped 74% of organizations to unlock higher Developer productivity. DevEx means reducing friction from SDLC workflows. It means investing in Dev tools and inculcating a culture that maximizes the maker time metrics. A developer-first culture helped 81% of organizations improve developer retention.

Better productivity & talent retention strengthen the innovation gears of your organization. DevEx investments improve developer productivity, which translates into better engineering velocity metrics (indirectly, it’s a measure of value delivery rate to the end customers). In fact, 77% of organizations investing in DevEx noticed a significant positive impact on the time to market. This could be because happy developers tend to write quality code with fewer bugs (comparatively), which results in lower technical debt, an increase in code commits, PRs raised, PRs reviewed & PRs merged, high deployment frequency (DF), and low code-churn. As a result, 75% of organizations experienced higher customer attraction & retention rates. Plus, 82% of organizations performed well on customer satisfaction metrics.

3. Translate Ideas Into Revenue Opportunities

Also, the C-suite may just buy in when you tell them how DevEx impacts profitability & revenue. Improving DevEx also means investing in modernizing SDLC workflows with infrastructure orchestration & monitoring tools. This helps DevOps teams proactively respond to security threats in real time. Ultimately, minimizes business losses due to breaches or system downtime/failure. Not to mention, robust security improves business reputation & recognition, customer loyalty, and assists you in competitive market positioning. Thanks to improved innovation muscles, and happy customers, 85% organizations reported a positive impact on revenue growth, and 81% improved profitability as well.

In short, to get buy-in for developer experience initiatives from the top leadership, make your pitch personalized (so that HR, Finance, and other teams support you), and get revenue-focused.

4. Pitch for Incremental/Phased Investment

Strive for gradual progress, recognizing that both large enterprises and smaller businesses often grapple with budget limitations. But you also need to move the needle. So, come up with a strategy for enhancing developer experience that doesn’t feel like a mission impossible. Basically,

Categorize developer experience into different segments based on outcomes. It could be reducing developer friction, streamlining SDLC, optimizing infrastructure, etc.
Prepare phased project documentation with budget infusion estimates, timeline, expected tangible outcomes, and impact on the bottom line.
Factor in the cultural changes as well in your project documentation. For example, if you’re adopting GitOps to streamline infrastructure operations, it would reshape the roles & responsibilities of the operations team, release engineers, configuration managers, and others.

Persist Until DevEx Initiatives Get a Green Flag

For enhancing developer experience, getting the buy-in from the C-suite is undeniably crucial.

However, obtaining approvals or pouring resources into DevEx initiatives doesn't automatically translate to better business results. There are multiple factors at play.

For instance, siloed departments can hinder innovation. So, inter-team collaboration is going to be a key factor for DevEx's success. You need to foster a culture of developer autonomy, ownership, and accountability, which again improves the developer experience. But cultural transformation is not simple. A common bummer is resistance to change. Then there is the challenge of re-skilling the workforce to help them adapt to the new technologies and engineering practices.

The best leg forward to overcome these challenges is to improve your overall engineering workflow visibility.

Engineering workflow visibility tools (say ‘Hi’ to Hatica) are the underrated and unsung heroes of your DevEx initiatives. With engineering management platforms, you can help the C-suite to gain data-driven visibility into your engineering processes, practices, and projects. It helps you to clearly visualize, and communicate how the investments impact the bottom line, and most likely, win their support.

Icing on the cake, Hatica is a perfect match for your DevEx initiatives, as it empowers you to place significant focus on the well-being of your developers.

As a takeaway,

Be shamelessly persistent about getting the buy-in for DevEx initiatives
Harness the superpowers of Hatica for data-driven DevEx strategy
Become better at mapping the engineering activities to the business bottom line.

Good luck with your DevEx transformation!

What is Technical Debt and its Associated Cost Implications?

Naomi Chopra — Wed, 10 Jan 2024 17:24:11 +0000

Silicon Valley is the only jungle where you will find an Elephant sprinting like a Cheetah. Because the rule of the jungle is to “Move fast and break things. Unless you are breaking things, you’re not moving fast”.

At the core, the above rule/motto evangelized by Zuckerberg, the founder of Facebook, is not much different than the first agile principle i.e., continuous innovation and value delivery. Still, the topic of ‘moving fast’ often radically divides the tech community into two halves i.e., for and against. However, none can deny that agility, adaptability, and flexibility are the key enablers of innovation and growth for both, the Elephants (large enterprises) and the Cheetahs (startups).

In fact, it’s often fascinating and admirable to see companies innovate & scale at breakneck speed. But in the pursuit to be the next Lion King (market leader), make sure you do not overlook technical debt. Or else, forget about Elephant & Cheetah, you may end up as a Rabbit losing the race to a Tortoise.

Read this insight to avoid any such unfortunate outcomes, and understand the cost implications of not acknowledging and addressing the technical debt proactively.

Technical Debt Quadrants Types & Associated Cost Implications

Technical debt is inevitable, no matter how small or large the project is. However, the severity of cost implications and the underlying cause could be different. The nature and severity could be understood using the famous technical debt quadrant by Martin Flower, where the author segmented tech debt into four quadrants:

1. Deliberate & Prudent Technical Debt

The 1st quadrant is where the team knows the risks that they are taking, the associated inherent reward, and the debt they will have to pay in the long run. These are conscious decisions around code and barely introduce any significant technical debt with high payoffs.

This sort of technical debt can often be found in the less critical components of a software system, the components which are seldom used by the users, or touched by the developers. The cost implications of such debts are negligible, and teams often escape paying off these debts.

An example could be non-critical application features like a dysfunctional scroll icon that doesn’t do anything on click.

2. Deliberate & Reckless Technical Debt

The 2nd quadrant covers the types of technical debt where the team deliberately ends up introducing high-interest debts. A major cause of these debts is the ignorant attitude of the capable development team towards following the best software development practices/principles (reckless technical debt).

Often, teams who introduce deliberate technical debts estimate the perks of immediate release worth paying the high debt payoffs down the line. While deliberate & prudent technical debt is risky too but manageable, the cost implications of deliberate & reckless debts are crippling enough to make even the mightiest of organizations kneel down.

For example, inadequate input validation or lack of proper authorization checks during the checkout process in an ecommerce app could be catastrophic if hackers manipulate cart prices, bypass payment authorization, and exploit vulnerabilities for unethical advantages.

3. Inadvertent & Reckless Technical Debt

The 3rd quadrant encompasses technical debts that are unintentional. These sort of debts gets introduced due to a lack of awareness or technical inexperience of the engineering teams. The cost implications of inadvertent & reckless technical debts could be equally fatal as deliberate & reckless debt.

For example, a social invitation app that limits you to invite only 50 people every week to follow you. But if you merge two of your social accounts then you’re able to invite an unlimited number of people per week. This sort of bug is mainly because of the complexity of implementation and arises due to lack of proper technical requirements document. Now, if the number of invitations was your revenue model, your business might be staring at staggering opportunity loss. But if it was just an add-on feature, the losses are capped too.

4. Inadvertent & Prudent Technical Debt

The 4th quadrant of technical debt is where the team may have unintentionally introduced technical debt that could be taxing for the maintenance teams, and the organization in general. These debts are often identified late in the software development lifecycle process. By late, we mean, the team realizes that they have been developing the software the wrong way, and the best way to do this is radically different. The cost implications here are such that teams are often expected to rip off the entire system under development and build it again from scratch.

An example of this is developing a custom edTech LMS, only to realize later that you could have used an open-source edTech framework Moodle to build your LMS.

Unveiling How Rising Technical Debt Hits Your Bottom-line, and The Price You Pay

Irrespective of the quadrant it falls in, technical debt is a costly gamble. It can break your bank and even make you file for bankruptcy. No kidding, we are dead serious. The cost implications of technical debt are multifaceted and can be felt across the organization, both horizontally and vertically. Here is how it hits your bottom line:

1. Staggering Financial Burden

As per a survey by McKinsey, companies are allocating 10-20% of their new project budget toward technical debt.

Technical debt, aka code debt, is often an outcome of shortcuts and tradeoffs development teams make to meet deadlines. Sometimes, it is also because of the negligence, inexperience, incompetence, and careless attitude of software developers and other project stakeholders.

Engineering managers often greenlight an inefficient solution because, in the short run, it seems less taxing financially and technically.

But in the long run, as the debt accumulates, the complexity of the software systems becomes tightly coupled. And the costs incurred towards the maintenance and upgradation of tightly-coupled solutions i.e., technical debt repayment costs often drain the finance team of the budget.

The implied costs can be in the form of infrastructure upgradation, re-platforming, application code refactoring, patching critical vulnerabilities, or extra investment that goes into customizing an off-the-shelf third-party solution to effectively integrate it with your tech stack.

2. Loss of Developer Productivity, Increased Cycle Time, and Plummeting Release Velocity

A lot of times, talented developers get unpleasant surprises. Especially, when they work on the extensibility of pre-existing software, and are developing new features for it. These surprises are nothing but the technical debt that creeps up on your SDLC process, which could be:

Bugs
Poorly implemented non-modular software components
Conflicting code blocks
Inconsistency in data models & data access
Non-compliance of the code with relevant geographical, legal, and industrial regulations & standards.

In general, every second a developer spends dealing with tech debt results in loss of developer enthusiasm, and a loss of developer time that could have been spent writing the application code and developing new innovative features.

To effectively minimize the risks associated with error-prone code, the development teams put extra effort toward software testing to improve the test code coverage. And because the developer is busy modernizing/patching/testing the legacy code, it slows down the value release velocity of the development teams. In fact, as per reports, an average developer spends 17.3 hours per week dealing with bad code and tech debt, which accounts for $85 Billion in annual losses.

3. Opportunity loss

A non-quantifiable implication of technical debt is opportunity loss, aka loss of revenue and customers.

Just like the sand accumulating in an hourglass, 60% of organizations said that their technical debt has compounded over the last three years. And rise in technical debt gradually robs organizations of their ability to adapt, innovate, and scale. Hence, projects plagued with technical debt are often late when it comes to releasing new features, and miss to grab emerging market opportunities or gain first-mover advantage.

This triggers a domino effect, where customer churn is high, as your competitors gain an unfair advantage over you and are better positioned to attract new customers. Not just that, they also improve existing customer loyalty with continuous value delivery and delight customers with breakthrough innovations, new features, and experiences.

4. Talent loss

A non-empirical cost implication of technical debt is talent loss, aka employee churn or turnover. The frustrating experience of maintaining and fixing the legacy code can take a toll on your developer’s well-being, and may even cause burnout. Just like customers, unhappy employees too may bid adieu to your organization and look out for better opportunities. This could be a cost-intensive affair for you, as on average it takes an average employer USD 4000/- and 24 days to replace an employee. The costs could be much higher if you are on the lookout for people with exquisite skills in outdated or less popular technologies like embedded systems programming.

Put a Check on the TCO

Gartner says, “Through 2023, I&O leaders who actively manage and reduce technical debt will achieve at least 50% faster service delivery times to the business”.

Besides, as discussed above, inadvertent ignorance of the looming cost of technical debt could clog your growth gears and bring your organization to a screeching halt. So, engage in periodic/continuous code reviews, look out for code smells, and track performance-related engineering KPIs like cycle time, code churn, deployment frequency, change failure rate, mean time to restore, etcetera to spot inefficiencies in your SDLC process that could be an outcome of accruing tech debt.

Tackling Technical Debt with Hatica

Make use of Hatica, an engineering analytics platform to track the aforementioned metrics alongside 13070+ other engineering metrics to gain a comprehensive insight into your SDLC process efficacy. Also, ensure that you follow and implement emerging software development paradigms like CI/CD, Automation testing, Serverless, APIs, Microservices architecture, DevOps, GitOps, and Agile practices to command the TCO for all acquired and owned software.

Poor customer experience, dwindling brand value, high churn, loss of revenue, increased errors, bugs, and crashes, your technical infrastructure degrading to a monolithic architecture, worsening engineering metrics, and critical security vulnerabilities can all negatively influence your Total Cost of Ownership (TCO). TCO is the net expenses associated with acquiring/owning, operating, maintaining/supporting, customizing, upgrading, and replacing/abandoning a technology solution. And tech debt can significantly impact the TCO for any software application your organization uses. For sustainable momentum, you must aim to minimize the TCO, which in turn requires you to proactively acknowledge, monitor, and address your technical debt.

Keep investing in awesome developer experience, and zero tech debt!

Subscribe to the Hatica blog today to read more about unblocking developers, and boosting productivity with engineering analytics.

What is DevFinOps? Why It is Important?

Naomi Chopra — Mon, 08 Jan 2024 05:45:04 +0000

Today, CFOs, CIOs, and CTOs are at a crossroads with each other. The reason? Rising software development costs, the constant struggle to find the source of value creation, and drive business agility. The struggle is obvious as software development costs makeup 63% of a project’s budget.

Most engineering leaders face a common dilemma- shoot budgets through the roof or compromise on quality at lesser costs. The line of thought has had some anecdotal evidence from the past where developer innovation was bludgeoned in the name of optimizing software costs. This common misconception has driven many towards fiscal drag, product failures, and forgettable customer experience.

Today, C-sec executives across all verticals are frantically trying to find a single source of truth to understand, and optimize engineering spending, while fostering financial accountability.

It's where DevFinOps steps in as the game-changer.

What’s DevFinOps?

DevFinOps is a blend of Development, Operations, and Finance, all rolled into one powerful box.

It wouldn't be wrong to call DevFinOps as the ‘Henry Ford’ moment of engineering. Just like Ford introduced automation to accelerate production, and curtail costs; engineering teams must manage engineering costs.

The idea is to align development efforts with financial goals, so teams create the most value out of every engineering investment made and there is more dollar generated in turn for every dollar spent on the software development.

Traditional software development has followed the ‘build now, cost later’ approach where cost optimization takes backseat initially, and later comes to haunt C-secs for blindly offering finance buy-ins.

With DevFinOps in their arsenal, engineering teams now build products while being mindful about development costs. It also translates to building strategic assets that can be capitalized, rather than expensed, and subsume any financial burden.

DevFinOps is a natural evolution from FinOps, and expands financial prudence beyond the cloud, and across all engineering verticals- cloud, to issue trackers, maintenance costs, software licenses, training and development, prototyping, and VCS. Basically, getting at the core of development.

The Need For DevFinOps

#1 reason for this cultural shift– skyrocketing IT spending. In 2022 alone, enterprises dished out $783 billion, and will soon reach an estimated $1 trillion at a 10% CAGR– all the amount spent on IT alone.

Amid economic uncertainties, high fees, upcharges, and IT prices can cause dismay, even putting higher pressure on engineering teams, already strained with high toil, and uneven engineering demands.

The other reasons for engineering teams to shift towards DevFinOps:

DevFinOps help engineering teams imbibe strategic vision, and data-driven insights into streamlining SaaS, IaaS, and PaaS spendings.
The cultural approach empowers engineering teams to reduce resource wastage, and unsustainable spendings, in an era where tool sprawl, and unproductive cloud spending have plagued most engineering organizations.
Frugality is a super power, and most engineering teams secretly wish for it and still largely fail at it. And DevFinOps is here at the rescue! Even Mckinsey notes how incorporating financial practices into SDLC can save upto 30% of IT costs.
The combination of financial strategy with DevOps pipeline helps teams maintain business alignment, and ongoing improvement. For instance, the hybrid work shift accelerated by pandemic has rendered many telcom services, and network infrastructure unusable, and inoperable. DevFinOps brings back these lost costs through routine inspections.
DevFinOps’ true value comes in capitalizing software costs, by turning expenses into assets, and creating healthy balance sheets.

The DevFinOps principles are white hot today, even adopted by some of the leading global organizations. Shopify’s legacy in adopting DevFinOps is an inspiration for many. The eCom giant reduced 10% of their infrastructure costs through cloud migration, adopting Kubernetes, and mapping engineering efforts with every dollar spent (thanks to data-driven culture at Shopify).

Despite all the benefits that DevFinOps offers; its implementation remains scarce, and sluggish. Why?

The Bumpy Road To DevFinOps

Gartner recently surveyed global C-suite execs on adopting new approaches to their development cycle. The results were staggering. Even though 52% of CEOs aim to optimize their cost-to-growth ratio, they struggle to fully capitalize on their implemented strategies. The other 59% are in dismay because these cultural shifts take too long to reap their benefits. DevFinOps adoption too has a similar story to tell.

In engineering organizations, DevFinOps cannot scale in manual environments dominated by the excel sheet culture. A visibility driven by constant badgering to know about current engineering efforts can rebound, and harm an organization, rather than doing any good. A lack of data-led culture can also snowball into team friction, frustrated employees, followed by resignations from both sides- be it engineering or finance. These issues have roots in lack of access to historical data that might have helped teams forecast cost dynamics beforehand.

Another issue is complex engineering workflows. Constant juggle between development to testing, and deployment introduces its own set of financial considerations. Determining the cost implications of every action can feel like untangling a web of complexity.

Moreover, introducing DevFinops means tweaking your reporting, and project management operations to incorporate new practices. Any change, especially in already burdened engineering teams, comes at its own cost. This change is often met with resistance. Getting buy-in from almost everyone in the team is a tough nut to crack.

Convincing teams to adopt a new DevFinOps approach might be like herding cats. Devs, and managers are comfortable with familiar routines, and altering these routines comes with new learning curves that need time (something engineering teams never have), diverted developer attention, and even quality compromises.

This resistance often stems from knowledge-divide, a myopic view of engineering activities, and lack of team enrollment across all levels. The development team might excel in writing codes, but when it comes to understanding financial jargon, things get lost in translation. Bridging the gap between tech brilliance and financial wisdom is easier said than done!

All this leads to degradation of work ethos for the development team, and a dent on developer productivity. Cloudzero’s recent survey reveals the implications of burning software costs on engineering productivity– 41% of devs face higher interruptions, even lasting till next sprint, all because of internal cost problems.

Overcoming these challenges demands a mindset shift, a learning curve, and a hearty dose of collaboration.

Engineering Analytics: Answering The DevFinOps Challenges

It’s an old saying in engineering- you need to measure it, before you improve it. The same goes for adopting DevFinOps in your organization. A core reason behind limited success of DevFinOps is limited visibility. It’s simple as basic math- The more visibility teams have into their workflows, the more the collaboration, and a better pulse of engineering processes.

Engineering analytics make that happen, by offering end to end visibility, real-time actionable insights, and team updates- all on a single pane. Visibility, backed by contextual data into engineering spending removes blindspots between engineering, and finance teams, and helps teams to realize shared goals, while answering tough questions like:

How much does a product cost?
The viability of existing resources in maintaining IT infrastructure
Should we outsource or build in-house
Where each, and every dollar is being spent
How can we break even software costs?
The miscellaneous software spending
Revenue Impact from individual projects
Are these costs capitalizable? If yes, then how to categorize these costs.

With real-time data on development progress and costs, the two teams can collaboratively decide on all hot topics- the underutilized resources, or whether to accelerate timelines or allocate additional resources.

Another use-case of engineering analytics in DevFinOps is finding the balance between OpEx, and CapEx costs. With help of actionable data, teams can automate cost capitalization without much human intervention, and even keep a check on operating expenses everytime they breach the team threshold.

In a nutshell, data is not just the new oil, but also the key to unlock DevFinOps at your organization!

Bottom Line: Bringing Financial Clarity to Your DevOps Pipeline

Working out a DevFinOps strategy comes with its own eureka moments- only having visibility into your engineering workflows isn’t enough. It’s a continuous exercise requiring regular support from both ends of the spectrum- the finance, and engineering teams.

Capping software budgets to optimize costs is a narrow approach. And what DevFinOps brings is a new outlook of how non-tech business teams see engineering efforts, and even offers a new code of conduct for how engineering teams operate, and spend.

DevFinOps today stand at the intersection of cost excellence, and engineering success. The spirit of continuous improvement thrives with data-driven teams. DevFinOps is a path worth taking – a path that aligns development, finance, and operations to create a seamless, cost-efficient, and value-driven process.

AI For DevOps — Concepts, Benefits, and Tools

Naomi Chopra — Fri, 05 Jan 2024 06:11:35 +0000

TL;DR AI is to augment the existing processes and capabilities, it’s not built to replace the sapiens. The jobs, the responsibilities, and the challenges of DevOps are not going anywhere. The increasing inference of AI in DevOps is only going to unlock higher productivity for DevOps teams. Generative AI is already enhancing the efficiency of both developers & operations teams. The crux of AI-led DevOps is that it will improve DevOps effectiveness, and speed up the DevOps adoption, DevOps toolchain integration, and implementation of DevOps Best Practices. Lastly, as AI gels with the DevOps ecosystem, it will lower the entry barriers for developers to explore the realms of DevOps, thus attracting more talent, and enabling more innovation.

Are DevOps Teams & Engineering Managers Really Buying into the AI frenzy?

46% of engineering teams have set goals for themselves to optimize DevOps processes in 2023. You may wonder, how engineering managers are planning to do this.

Well, 41% of engineering organizations plan to adopt engineering management platforms, & dashboards to measure engineering KPIs & metrics to improve development velocity & software delivery efficiency.

Today, you go to any interactive industry event (not the ones where only the speaker blabbers), and no matter what’s the theme of it, someone will definitely steer the discussion around chatGPT & generative AI.

In DevOps-focused tech events, it’s common today to hear questions like -

How will generative AI change DevOps? Will AI take away DevOps jobs? What should be the strategy for adopting AI DevOps tools?

Which AI tools would you recommend for enhancing DevOps processes? … and so on.

Also, you will easily sense in DevOps meetups that there is pressure on enterprise leaders & engineering managers to adopt AI in DevOps. There is a sense of immediacy among organizations to ride the AI wave, leverage AI & DevOps tools, innovate digitally, and march ahead of competitors to adopt AI in DevOps. There is a sense of immediacy among organizations to ride the AI wave, leverage AI & DevOps tools, innovate digitally, and march ahead of competitors. After all, who wants to fall behind?

Everyone aspires to lead the pack, or at least remain relevant in the market. And it makes sense as well.

Imagine a decade-old enterprise being ousted from the market by a few months-old newbie startups. Wouldn’t that be demoralizing? Anyway, what’s exciting today is the fact that the majority of us, with unanimity, believe that ‘AI has arrived’.

People know, AI’s no fad. For instance, OpenAI dropped ChatGPT into the market, and boom, it exploded. Millions swarmed to use it, akin to how moths are drawn to a luminous beacon in the night sky. ChatGPT’s chest-thumping roar on arrival was so wild that even the giants could feel the chill in the spine (Nevermind, Google). It reverberated “Generative AI has arrived, and it’s gonna stay”. I doubt if you could yet say the same about Cryptos. Can you? Even to date, the majority will shy away from getting into Cryptos, despite all the noise.

In fact, we believe, all the AI frenzy that you see around is just a curtain raiser, the show is yet to begin. The following industry signals further bolsters our belief:

24% have already knitted AI deep into their DevOps processes
31% of engineering teams make use of AI/ML technologies for effective code review.
37% already use AI/ML for software testing. The numbers are estimated to double up in a year or two
51% of engineering teams make use of AI/ML to check code hygiene
62% of organizations are already practicing ModelOps — DevOps for machine learning models lifecycle management.

In short, if early signals are to be believed, it is inevitable that AI will penetrate every single industry, and every single vertical of the organizations.

How Generative AI Is Influencing the DevOps Ecosystem?

Continue reading this insight to understand what impact AI will have on DevOps, what problems it will solve, and a lot more. Basically, the insight answers how generative AI will influence the DevOps ecosystem.

1. AI is not here to replace but to augment DevOps

Do you ever wonder why DevOps or DevSecOps even exists? To ship secure software at speed, and at scale. AI won’t change that at all.

AI-led DevOps simply means embracing ML models in your DevOps process to automate SDLC stages as much as possible, without trading security or sacrificing product features. Basically, AI will help DevOps teams to innovate faster with rapid feedback-led iteration cycles, automated testing, predictable deployment frequency, shorter lead time to change (LTTC), and reduced mean time to recovery (MTTR). All this while enhancing the developer experience, and making software development more streamlined & less stressful, if not delightful.

So yes, you inferred it right — the core role & responsibilities of DevOps professionals aren’t changing with AI entering the realm.

DevOps teams will continue to shoulder the responsibilities such as continuous integration & deployment, security, safety, maintenance, resilience, speed, and other key aspects of code/application quality.

AI is not eating away your jobs, but it’s not even like nobody is losing jobs.

“Going to the very beginning of the Ops world, because the landscape changes, and the level of abstraction changes, if you get really comfortable using a particular set of tools, and you really stake your professional identity to that, you’re probably always going to be at risk”, says Forrest Brazeal, Head of Developer Media @ Google Cloud.

Forrest is of the view that it’s more important than ever to learn to code.

Generative AI tools can easily spit a tremendous amount of code, but someone needs to read, understand, debug, and maintain this code. So, generative AI-led DevOps is not a threat. Instead, on the contrary, good DevOps engineers can leverage the speed that generative AI unlocks to create higher value to impact the business's bottom line.

In a nutshell,

New layers of abstraction in an AI-led DevOps world are inevitable.
Roles won’t be wiped out, but they will evolve.
AI is not a panacea, but the DevOps processes will definitely get accelerated with AI capabilities.
DevOps professionals will be able to speed up the process of completing templated tasks like -
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… possibly, all this can be completely automated with AI.

2. Reimagining Complex Workflows with AI

DevOps’ success is directly proportional to the level of automation you can achieve across the SDLC stages. Basically, the more you can automate repetitive stuff in the software development life cycle, the higher the efficiency you’ll unlock. That’s exactly what AI does — automation of repetitive tasks.

AI tools for DevOps can help reduce MTTR, improve code quality, simplify code documentation, and improve interoperability. AI-powered DevOps speeds up integrations, optimizes application performance, reduces operational costs, and implements security at scale.

And that’s why, engineering managers have high hope for AI & DevOps-led engineering workflows.

AI is no panacea, but it can definitely help with the slow, manual, complex, and error-afflicted DevOps & SDLC processes that delay the software release cycles.

In simpler terms,

Generative AI can help with code development at speed & scale. It can autocomplete your code blocks, or generate code for you based on your natural language inputs/instructions.
You may also leverage AI tools for DevOps to speed up code reviews, identify bugs, and security vulnerabilities, and resolve any code quality-related challenges. For instance, ML models can easily spot software development issues like resource leaks, buffer overflows, concurrency race conditions, thread starvation, data inconsistencies, or wasted CPU cycles.
And of course, you can use AI tools for DevOps to not only automate the generation of test scripts (code), but also to automate software testing, security testing, test data creation & management, and making sense of test results data.
Besides, AI tools for DevOps also help with infrastructure orchestration (GitOps), monitoring & alert, and infrastructure resources cost optimization.

Though actual AI-driven DevOps lifecycle may vary based on the engineering culture & DevOps maturity of your organization, on a high level, the evolution of existing DevOps processes may unfurl as the following:

AI-powered Continuous Integration & Continuous Deployment (CI/CD) pipelines transcends beyond automating building & delivering artifacts to production.

AI-led CD platforms pull time-series & events data, logs, and errors from business & engineering KPIs & metrics tracking tools through webhooks/connectors/HTTP assertions. Next, the ML models feasts on this data to unsurface any anomalies around deployment, performance, and reliability.

If anything critical is spotted, AI-powered CD tools can alert DevOps teams for manual intervention, or even auto-resolve issues with corrective actions based on pre-defined standard operating procedures for known issues. For instance, AI can roll back build artifacts if the impact of an exposed code vulnerability could be significant to the business.

However, if everything is flawless, AI helps deploy code changes across cloud & on-premise environments.

3. AI-led DevOps Monitoring and Alerting Systems

As mentioned earlier, AI tools for DevOps proactively detect anomalies in engineering code & data and responds with corrective actions. Furthermore, predictive analytics helps with preemptive measures by utilizing historical platform/application data to forecast potential vulnerabilities in the code.

4. NoOps: Scaling, and Automating Infrastructure

DevOps teams can seamlessly adapt to infrastructure usage patterns via AI-driven insights. AI-led DevOps analyzes historical data, usage patterns, and performance metrics to anticipate demand & accordingly, provision & scale resources to ensure optimal performance and responsiveness with minimal human intervention.

5. Automated Configuration Management, Security, and Compliance Assessments

AI-governed infrastructure configuration & orchestration means reduced downtimes, improved trustworthiness, increases reliability, and ensures uniformity in release management. When the self-healing AI systems detect inconsistencies (unusual data patterns) in configuration, or slight drifts in security or compliances, these AI systems perform root cause analysis and accordingly introduce corrective actions (intelligent rollback & recovery), and restore self to optimal configurations, optimizing schedules and minimizing disruptions.

However, human expertise remains indispensable for strategic decisions, ethical considerations, and tasks demanding manual intervention.

Why DevOps Practitioners Must Embrace AI?

As an tech lead or engineering manager do you actively build your AI-led DevOps tooling system, aka DevOps tech stack?

Do you understand where those AI-powered DevOps tools can be used in your engineering operations management?

Also, do you feel bothered by the NoOps fear-mongering, aka a world with no human DevOps professionals?

Well, complete automation of SDLC, NoOps is the ideal outcome of DevOps, but that’s way elusive for now. What matters for now is whether you have the necessary skills that DevOps professionals must inculcate/possess to capitalize AI to its best, and subsequently unlock greater agility for teams & organizations.

A no-brainer, of course, you need to learn using AI tools. It’s helpful if you understand neural networks (GANs, transformers), and generic machine learning algorithms & concepts. For instance, supervised & unsupervised learning, data processing techniques (cleaning, normalization, and feature extraction), ML models & their performance evaluation metrics (Precision, recall, F-1 score), etcetera.

Yes, the learning curve might be steep and time-consuming, but the rewards will be comparatively way more fascinating.

Vendor Engineering

As things get automated, a key factor that would differentiate you from your competitors is how well you leverage AI. Why it is suggested to know the basics of AI & ML because then you can optimally customize & utilize it as per your need.

Going forward, how well-versed you are with the AI & DevOps tooling ecosystem is going to be a game changer for your organization. It can be your new moat because you’ll be able to integrate exactly the right tools from the right vendors into your SDLC lifecycle in the right way to maximize value yield while optimizing resource utilization.

In a nutshell, it is more important than ever to embrace agile principles & AI DevOps tools into your work culture.

Agile principles like continuous innovation, effective communication & customer collaboration, and continuous delivery of software are already integral parts of the DevOps fabric.

Teams just need to get more serious about abiding by these principles. Read our insights on What’s DevOps, Agile software development, and Agile Principles to fully tap into the value when these are fused together.

Also, you may want to explore the following AI DevOps tools that are popular among AI & DevOps practitioners

Hatica, to optimize engineering efficiency and enhance developer experience & productivity by analyzing 130+ key engineering metrics pulled from a wide range of SDLC tools your organization uses. It helps reduce your technical debt and improves development velocity.
Amazon DevOps Guru, to detect anomalies & deviations from normal operating patterns to automate root cause analysis & resolution, as well as to help avoid resource outages in the AWS environment.
Amazon CodeGuru, the CodeGuru Reviewer analyzes code on pull requests to the attached repositories and suggests improvements in the form of comments. The Profiler helps enhance performance by identifying the most expensive lines of code in terms of vulnerabilities or latency introduced by it and makes recommendations to address the same. Profiler also helps troubleshoot operational challenges in a production environment.
Datadog, Measures logs, traces, and metrics from every infrastructure component across the organization (including upstream & downstream systems) to help teams visualize real-time data flow between different services and infrastructure components of an application to better understand its infrastructure and dependencies. Helps in implementing automated monitoring solutions to gain improved visibility into configuration management tools and DevOps orchestration platforms.
Dynatrace, is a full stack application & platform observability & security tool. It detects service degradation, security issues, and performance anomalies in auto-trigger remediation workflows. Also helps with alerts and continuous release validation.

For the brevity of this insight, we are omitting some of the names that deserve to be here. Especially the AI testing tools like Applitools and SauceLabs that gel well with your CI/CD pipelines. There are tons of other AI tools that aim to help engineering teams with DevOps success, including Appdynamics, NewRelic, IBM AIOps, and others.

Key Takeaways

To sum it up, DevOps is headed towards NoOps, but that’s a far, far, far distant future.

Right now, companies can concentrate on reaping the full benefits of AI by optimizing their AI & DevOps led SDLC processes to alleviate technical debt, accelerate innovation, and delight customers with continuous value delivery.
Organizations must leverage AI-assisted DevOps workflows and make them democratically accessible across the organization's infrastructure & application teams.
Your AI-led DevOps strategy must plan to squeeze in the following benefits:
Improve code quality with AI-assisted code review, bug identification, issue resolution, and code optimization.
Automated configuration management & infrastructure orchestration, performance observation, security monitoring, and alert systems.
Intelligent & continuous AI-led software testing, with automated test script generation, test data generation & management, and analyzing test data results at scale for remediation or improvement of identified issues, vulnerabilities, and performance bottlenecks.

There are indeed security concerns about how the data will be utilized by these external AI DevOps tools, but that shouldn’t deter you from evaluating & identifying the right AI DevOps tools with the right policies around data governance & utilization, with no known security & IP vulnerabilities/risks.

If you’re not already using it, you may also equip your engineering teams with Hatica for gaining better visibility into your engineering processes, and to improve your velocity metrics & engineering efficiency, as well as enhance the overall developer experience of the engineers who you employ.

Look for more helpful engineering blogs on Hatica, and book a demo to see how Hatica can help you with engineering sorcery.

R&D Cost Capitalization For Engineering Teams Explained

Naomi Chopra — Tue, 21 Nov 2023 10:18:05 +0000

In 2018, Paul Romer won Noble for his long-standing work on how investments fuels microeconomic growth. His words of caution were- Quit seeing research and development (R&D) as optional spending. Champion a new approach: turn your development costs into assets.

It was one of those wakeup calls for engineering leadership to embrace cost capitalization, and streamline engineering expenses.

Fast forward to 2023, software companies are adopting aggressive growth goals. For engineering leaders, the top financial hurdle to hit these goals revolves around dissecting engineering expenses and pinpointing where value truly originates. Enter R&D cost capitalization, a strategic avenue to tackle this challenge head-on.

What is R&D Cost Capitalization?

R&D cost capitalization is a legal accounting practice where software R&D costs, like FTE wages, and software licenses, are listed as investment, rather than an expense. In simpler terms, you're not just spending money; you're investing in your company’s future.

Today, every engineering team is working on the next big thing, and software development demands resources - be it time, or scientific manpower. These development costs can run in millions, often costing upto 63% of a project’s budget- making their financial sustainability a center point of C-suite discussions. While a significant portion is recognized as expenses, R&D cost capitalization helps turn some immediate expenditure into long-term, strategic costs.

The cost capitalization framework is solidly outlined under the International Financial Reporting Standards (IFRS) and the US Generally-Accepted Accounting Principles (GAAP) regulations. However, as of now, GAAP has stayed silent on the proportion of software expenses eligible for capitalization. Nevertheless, over the past half-decade, the US has successfully capitalized approximately 17% of their expenditures, with an eye on capitalizing an additional 12%.

Benefits of R&D Cost Capitalization

Continuous innovation is the lifeblood of engineering teams today. It requires resources to thrive, and cost capitalization as a financial strategy makes it achievable. Here are some reasons why engineering teams should adopt R&D cost capitalization:

Adopting R&D cost capitalization enables companies to treat certain strategic costs as assets instead of keeping them under the profit and loss (P&L) section of their balance sheets.
That way, balance sheets create a more accurate representation of an org’s financial health, and they can distribute their costs over time, avoiding a hefty one-time hit in a fiscal year. This might attract investors and stakeholders, showing them the value created for the company's future.
These costs are spread using the straight-line method over a period of two to five years, to create a revenue-time continuum for the developed product.
Organizations with higher R&D assets experience increased profitability. Reported profits have seen a jump of 14% after companies began to capitalize their R&D expenses.
Capitalizing R&D costs acknowledges that innovation is an investment, not just an expense. It aligns financial reporting with the reality that these expenditures can yield returns beyond the current fiscal year.
Capitalizing some percentage of software costs comes handy in leveraging tax incentives, and effectively streamlining Ebitda (earnings before interest, tax, depreciation and amortization). A higher Ebitda translates into better company financials, and higher profits in books.
According to a recent Livingstone analysis, the average EBITDA margin settles at approximately -6% after factoring in the R&D cost capitalization. However, this figure drops further to -8.5%, when R&D costs are treated as regular expenses.

Financial clarity is a must for engineering leaders. Or it is becoming more so now because dollars don’t come in easy. Capitalizing R&D costs translates into effectively assessing project RoIs. This informed decision-making can lead to allocating resources to initiatives with higher potential– paving the way for engineering success.

Software Costs That Qualify for Capitalization

Not all expenses can be transformed into assets, and the GAAP rules have been pretty clear about what qualifies for cost capitalization.R&D costs must meet certain criteria to earn their spot as an asset on the balance sheet:

Technology feasibility: The Capitalizable costs must translate into a tangible product or process.
Intent to complete: A clear commitment snowballs into a well-defined plan, and filters out half-baked endeavors.
Economic potential: Go-to market projections, and proof that the product will yield financial returns down the line.

GAAP’s FASB Account Standard Codification ASC Topic 350 – Intangibles deals with internal-use only software eligible for capitalization:

Software developed for internal-use only. The moment a company intends to sell the prototype, it will become an expense.
Activities undertaken during development stage- testing, coding, and installation
Software maintenance, and user training
FTE compensation of engineers involved in the development for aforesaid period

On the other hand, FASB Accounting Standards Codification (ASC) Topic 985 – Software deals with sellable software for external use. It includes:

Costs incurred in the technology feasibility stage
Activities undertaken during development stage- testing, coding, and installation, independent consultations, product development, and FTE compensation
However, costs associated with initial planning, and prototyping cannot be capitalized, and hence not exempted for tax calculations.

Generally, tech companies capitalize engineering compensation, product owners, and third-party platforms, algos, cloud services, and development tools. Moreover, in some cases, an organization’s acquisition targets too can be capitalized, and amortized.

The Challenges of Capitalizing R&D Costs

While software capitalization helps engineering organizations to revamp their balance sheets, it has been increasingly difficult to capitalize R&D costs over the years.

Originally, the GAAP practices were invented when software companies were still following the traditional waterfall model. However, times have changed, and so has the approach to building tech products. Today, companies no longer follow the linear method of development. The feedback loops are more dynamic now, and teams do not have to follow a fixed approach to software engineering.

When teams analyze, plan, design, and develop simultaneously in real-time, it becomes difficult for engineering teams to capitalize R&D costs at each stage. The term ‘technological feasibility’ itself leaves a lot of room for interpretation. With cutting-edge tools and platforms emerging constantly, assessing the feasibility of your next product against ever-changing benchmarks is a daunting task.

Another challenge for teams is around quantifying efforts under cost capitalization. Engineering leadership is under constant pressure to deliver multiple projects at once, making it increasingly difficult to log work hours manually for each IC. Currently, a lot of engineering managers, and senior leadership are trying their luck with Jira issues, and HR outputs to calculate capitalizable costs for FTEs. The process is too time-consuming, plagued by accuracy and precision concerns, and prone to human errors.

At times, this issue can snowball into micromanaging, and bossware, where ICs are constantly on their toes to report the exact number of hours spent on specific projects and dissecting the balance between core and non-core responsibilities. Moreover, when devs are constantly context switching between debugging, reviewing PRs, and writing code– pinpointing exact hours devoted to strategic initiatives becomes an intricate challenge.

The struggle to maintain a seamless input flow for calculating capitalizable costs sometimes feels like trying to hold water in cupped hands. Moreover, a lot of vagueness, and guesswork creeps in when engineering teams are unsure about how each engineering activity ties to the end business results, and falls under the overall schema of R&D costs.

This kind of expense tracking is culturally prohibitive for software teams, and comes at the cost of overwhelmed developers, team performance, and developer autonomy- all creating challenges for engineering managers, and the top-brass leadership.

How Hatica can help you streamline R&D cost reporting?

R&D cost capitalization is a potent, and perfectly legal accounting practice. It demands not just adherence to rules but a deep understanding of the engineering pulse. The balance between financial precision and the fluidity of technology is a tightrope to walk. However, it does get easier with the right set of tools, and objectively tracking software spending.

For companies considering R&D cost capitalization, a systematic approach is essential. It involves collaboration between finance, accounting, and R&D teams, to strike a balance between short-term profitability and long-term growth.

The best way forward is data-driven engineering analytics. Manually collating data-points comes with enormous opportunity cost, and even lacks insights for engineering leadership to make sense of the outputs that lack outright precision, and accuracy.

Engineering analytics help teams to move away from excel sheets, towards an automated, objective, and real-time tracking of engineering effort, and spending. This way, teams can produce traceable data from all workapps without having to put manual, yet crucial hours into fetching numbers across a digital toolstack.

The path to effective R&D cost capitalization requires collaboration, transparency, and a keen understanding of accounting principles. It involves harmonizing the visions of R&D teams and financial experts to make informed decisions that resonate with both growth, and fiscal prudence.

Subscribe to the Hatica blog for more insights on cost capitalization, developer productivity, and engineering effectiveness.

Unlocking Agility with a Multi-Cloud DevOps Strategy: 3 Expert Tips

Naomi Chopra — Tue, 21 Nov 2023 08:34:29 +0000

As of 2023, 94% of large enterprises have already embraced multi-cloud. But nothing comes without challenges. While multi-cloud has undeniable benefits, there are plenty of challenges as well.

The complexity of managing a multi-cloud environment increases a lot for DevOps teams. This is because of the variations in the tools, interfaces, APIs, and security requirements of different cloud providers.

The main perks of DevOps are streamlining the SDLC process, the security aspects, and continuous integration & continuous delivery (CI/CD) of applications. But when you scale to multi-cloud, you’re bound to face integration challenges.

For instance, you would need to refactor your applications for deploying in different cloud environments. Also, you will need to maintain data consistency across all your cloud platforms.

Not just that, you also have to be on top of security, monitoring & observability, compliances, data transfer, latency, performance, interoperability, portability, and cost concerns in a multi-cloud approach. In fact, governance too. Without governance, you might pave the way for cloud sprawl.

Overall, you need a good multi-cloud DevOps strategy to address these pertaining issues. Otherwise, your move from cloud to multi-cloud might be the same as going from the frying pan into the fire.

But don’t worry, we’ve got you covered.

3 Tips for a Winning Multi-cloud DevOps Strategy

Here are 3 tips to devise a winning multi-cloud DevOps strategy that helps organizations be more resilient & robust.

1. Add a Good Cloud Brokerage to Your Multi-Cloud Stack

Gartner defines Cloud Service Brokerage (CSB) as “an IT role and business model in which a company or other entity adds value to one or more (public or private) cloud services on behalf of one or more consumers of that service via three primary roles including aggregation, integration and customization brokerage”.

In simple terms, a CSB is an intermediary or third-party service provider that facilitates you in selecting, procuring, and managing cloud services for your organization. The core utility of a CSB is that it brings multiple cloud tools and offerings from different providers under one roof. And it ensures seamless integration among these cloud products. Also, they help in tailoring the solution as per your unique business needs. Besides, your CSB should also help you with security, compliance, management, monitoring, and cost-controlling aspects of the subscribed cloud offerings.

Leverage a good CSB and Infrastructure as Code (IaC) DevOps practices for multi-cloud environment provisioning & management, and for deploying code/applications consistently across different cloud platforms. This can be automated, and repeated as required. Thus, ensures the reliability, performance, security, and cost-efficiency of cloud infrastructure and applications.

Besides, imbibing IaC into your multi-cloud DevOps strategy allows you to have a centralized code repository. This facilitates teamwork & effective collaboration and improves the overall developer experience. You can easily integrate Hatica with your developer tools and your CSB for advanced engineering analytics, engineering metrics & KPIs tracking, etcetera to get valuable insights for enhanced developer productivity.

2. Stitch DevOps into the Existing Multi-Cloud Workflows for Containerization, Virtualization, and Hybridization

Enterprises in their quest for cloud-enabled agility harnesses cloud resources at all three levels i.e., Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Function as a service (FaaS).
At the IaaS level, let’s say, they make use of multiple hypervisor solutions based on the need basis. They could be AWS AMI, Google KVM, IBM powerVM, or numerous others from the likes of Oracle, VMware, Microsoft, Redhat, etcetera. The process of updating and managing machine images is an ongoing and dynamic process. As applications & the underlying platforms evolve, security patches are released and new software versions are introduced among multiple other things. Your DevOps team constantly needs to handle the creation and management of these machine images to keep up with the changes in the software stack and infrastructure requirements for reliable application deployment and functionality. It’s quite complex when this has to be done at scale.

The same applies to PaaS, for instance, container orchestration across Kubernetes, Docker Swarm, Amazon ECS, and other containerization platforms. Multi-cloud helps diversify your workload distribution and reduce dependency on a single cloud platform for increased application availability. But it’s no cakewalk given the complexity that emerges from this.

Your enterprise and engineering VPs must help software architects and DevOps professionals implement unified DevOps workflows that span end-to-end IaaS, PaaS, and FaaS. It may require the addition of multi-cloud DevOps tools to your engineering stack. For example, you can use Terraform or Protego for managing a heterogeneous serverless cloud.

3. Go Cloud Agnostic & Infuse DevOps Fundamental Principles

In general, companies first move from on-premise to the cloud. And as the needs evolve with business growth, and/or when cost optimization, performance, and other factors come into play, enterprises make the move to the multi-cloud.
A lot of enterprises do not anticipate this progression from the beginning, and hence their teams heavily get dependent on vendor-provided tools for DevOps implementation. Down the line, because of the tight coupling between your applications and the specific products/tools of your vendor, it becomes tough to switch/migrate to other cloud providers or DevOps tools.

As a result, the more you adopt multi-cloud, the more your engineering teams get isolated, lack visibility, and end up accumulating huge technical debt.
Hence, it is recommended to use cloud-agnostic development and deployment tools from the very start.

This enables greater visibility and enables seamless operations and management of resources across the multi-cloud. The core DevOps principles revolve around the same concept, where you embrace the tools and processes that facilitate collaboration, CI/CD automation, and continuous improvement, and thus allow you to be agile and deliver customer-centric applications.

A Roadmap to Effective Multi-Cloud DevOps Implementation

Research & adopt a good cloud service brokerage platform.
Devotionally commit to IaC, containers, and to building unified DevOps workflows.
Use cloud-agnostic DevOps stack, cloud arbitrage, and aim for higher visibility, lower latency, minimal technical debt, and high agility.

A couple of other things that you should keep in mind for a good multi-cloud DevOps strategy:

Leverage cloud arbitrage, and shift your workloads across cloud service providers by region and country to optimize for higher availability, lower latency, better performance, and budget alignment.
Thoroughly document multi-cloud DevOps workflow procedures, policies, and processes, and use the same to train the teams for better engineering efficiency.
Implement multi-cloud observability, monitoring, and logging tools, with real-time alert systems in place for higher visibility into your multi-cloud environment.

Follow Hatica Blog for more amazing insights on effective development strategies and tools that make your engineering teams more efficient, productive, and future-ready!

Unproductive Meetings: The #5 Developer Productivity Killer

Naomi Chopra — Tue, 21 Nov 2023 06:53:45 +0000

Picture this: A team of 20 pumped-up engineers, fresh from their morning workout or playing with their kids, ready to code, and finish off their projects like a pro. They gather for a quick 15-minute Zoom meeting, and later get on with their day. But what was meant to be a swift update turns into a 90-minute marathon. The energy they brought from a fresh day now wanes as their update meeting loses agenda, gets clogged with unnecessary details, and a dev’s personal workday too gets derailed. Now they are exhausted, and struggle with switching context back to code. All because of one unproductive meeting!

Reality-Check: Your developers struggle with more than 1 such unproductive meetings in a given work day.

Recently, Shopify cracked on its unproductive meetings after realizing the overhead cost borne by the organization. As per Shopify’s internal meeting calculator, the eCommerce giant loses $1600 weekly on a 30-minute meeting for 3 employees. For medium, and large engineering teams, imagine the operating cost for 100+ employees, and the financial burden it accompanies.

Software engineering demand is on rise, and every minute matters to deliver high-quality products to end users. Amidst this high-paced engineering, meetings need to evolve to be more than just a team gathering. They are a powerful, and shared platform to enroll team members, bring alignment, and foster collaboration.

Sadly, most engineering teams are still stuck with mundane, and unproductive meetings that distract devs more than empowering them.

The Cost of Unproductive Meetings

Did you know an average developer spends 23 hours a week in meetings, or 4-5 hours in a day and (surprise!), that doesn’t include the impromptu huddles, or Zoom calls. However, what’s worrying is 92% of the workforce considers these meetings unproductive, and costly. In another survey by Insider, everyday Americans conduct 11 million meetings. and that’s just the US alone. A third of them are unproductive, and can cost organizations upto $37 billion.

The total cost associated with these unproductive meetings is exorbitantly high for any engineering team, and yet mostly unaccounted for. Sadly, these unproductive meetings do not just impact organizations financially, but also cripple the overall developer productivity, and engineering efficiency.

Unproductive meetings take a toll on developers, zapping their productivity and leaving them frustrated. Time wasted in aimless discussions, without an agenda or highly digressed from pre-set agenda, means less time for coding, or grooming and that hits hard. Feeling disconnected and disengaged, developers struggle to find their coding groove. Their creative juices get stifled, and deadlines become a constant source of stress.

Moreover, a meeting overload shifts developers towards Zoom fatigue, and worse– technostress, and developer burnout. We all saw The Great Resignation growing before our eyes, and most engineers we surveyed candidly told us about why they quit. One of the major, but expected reasons– high meeting toll, and lack of focus hours. A known devil we have ignored for way too long.

Unproductive meetings have a high opportunity cost. When devs could have utilized the same workhours in brainstorming new ideas, networking, or completing their project checklist; they were stuck discussing off the charts topics, leading them nowhere. Besides, when meetings are unproductive, decisions are made haphazardly, leading to delayed projects, missed opportunities and lost revenue.

Now that we know how unproductive meetings affect engineering teams, let’s see what makes them ‘unproductive.’

4 Common Reasons Behind Unproductive Meetings

47% of the workforce considers “too many meetings” as a time-waster. Find out why.

1. Lack of Data-Driven Conversations

Data is the lifeblood of effective decision-making and team collaboration. Without access to real-time data, team discussions can lack the necessary context for understanding project deliveries, team velocity, and engineering bottlenecks. Lack of data-driven conversations directly translates to shallow and unfulfilling discussions, where teams fail at finding the root-causes, and rather delve into just status-updates. The pattern is not just limited to group meetings, but IC-EM 1:1s, and even conversations higher up the hierarchy between an EM and a DoE or a DoE and CTO for that matter.

Data-driven meetings focus more on recognizing trends and patterns that may bogging the team down– the cycle time is high, is it because of slow code reviews? Last week too had fluctuating lead times. Is it lack of automation, or the devs are stretched too thin?

Without data, teams only revolve around– our deliveries are late, and maybe we should free up more time to code, with no clear plan in sight. Without data analytics, meetings, or 1:1s quickly devolve into subjective opinions, and guesswork, and not objective insights.

Presenting to the board of directors? Check out our guide to Board Meeting Slide Decks here!

2. ‘Too Long’ Meetings

A healthy team meeting should last for 15-20 minutes, and a weekly 1:1 should not take more than 45 minutes. However, the reality is different, and even alarming. Most team meetings stretch for over an hour, and some of them even extend to more than 90 minutes. Unless it is an all-hands of all business teams, or company-wide celebrations, 90-minutes can be a make or break for developer’s day.

Usually, over-stretched meetings create time constraints for developers, even leading to rushed work, or something working in off-hours to complete daily goals. Long meetings quickly turn unproductive because of no clear agenda, lack of a plan of action, interrupted engineering workflow, and broken engagement (remember the time you used your phone and turned off the camera).

These long meetings are usually due to lack of data-driven conversations, zero to low visibility in engineering workflow, and no clear structure.

We discussed the former already, let’s talk about the latter.

3. Lack of Structured, and Planned Meetings

The most foundational issue with unproductive meetings is lack of agenda, structure, and planning. It’s more common for devs to go to meetings that we know nothing about than the ones we know. A clear structure keeps meetings organized, when they could have descended into chaos. No planning or structure often translates into meetings becoming presentations, limited participation, lack of focus, and a lot of wasted engineering hours.

Even when meetings seem productive initially, their impact can be negated without crisp, and effective follow-ups. Failing to document action items, responsibilities, and deadlines results in dragged discussions falling by the wayside, with no tangible outcomes.

4. More Status Updates, and Less Meaningful Discussions

Even now after so many conversations around developer productivity, still a lot of meetings start and end at: Can you update your work status? What have you done since last week? Instead of making the discussion more about identifying blockers, where the teams and individuals are actually stuck, figuring out internal/external dependencies we are stuck with mere status updates.

Even now, meetings are seen as a way to share retrospective information, rather than focusing on the future, or finding out about the ‘how’ of producing results. Engineering challenges require in-depth discussions around code reviews, best practices, technical standards, and project delivery. When meetings lack meaningful discussions, there is limited opportunity to address complex technical issues, potentially leading to unresolved problems and project bottlenecks.

Moreover, unproductivity creeps in when meetings become monotonous status updates. These types of meetings translate into inadequate knowledge sharing, a disconnect with the team, and missed innovation potential.

[ Read Related: Unlock True Meeting Management ]

How Unproductive Meetings Kill Developer Productivity?

One of our customers, an ed-tech platform, had a rigorous meeting schedule initially. However, as the team grew, so did the number of meetings on their weekly calendar. The meetings were timely run, and yet their volume impacted engineering workflows. Now more and more hours went to discuss immediate issues, while developer’s core tasks took a backseat.

They are not alone in their struggle with unproductive meetings. Find out for yourself how unproductive meetings wreak havoc on engineering teams:

1. Less Development Time For Engineers

As the user experience demonstrates, meetings, especially unproductive ones, force devs to trade-off their effective coding hours. Moreover, a lot of devs usually cope up with the issue by stealing from their personal time, or working out of office hours. No wonder, a developer only codes for 33% of their time. Writing even a few lines of code requires deep focus; but with too many meetings scheduled throughout the day, devs find it hard to concentrate.

Besides, most developers are introverts, and experience pre-meeting anxiety, especially if they are unsure about the meeting's agenda or their role in the discussion. This anxiety further aggregates into the "meeting recovery effect" where devs get so overwhelmed with meetings that they need time to mentally transition back to their primary tasks. Read more about how less coding time affects developer productivity.

2. Disrupted Flow State, and Maker Time

An engineer requires at least 30 minutes to get back into their ‘flow state’ and focus on writing code again. With meetings slated in different work halves- right from 10 am standups to 5pm catchup calls, developers struggle with their ‘deep work’ state– a prerequisite to write great code.

Ideally, developers have high maker time requirements- atleast one batch of 120-min time block reserved for high cognitive work requiring mental focus. However, with a fragmented schedule, developers have to find this quiet time on either weekends, or after-office hours. This often leads to a work-life imbalance, and unsustainable workload, making engineers anxious, and even burnout.

3. Frequent Context Switching

Another issue that keeps devs on toes, and disrupts their flow is the context switching. Devs constantly switch context, both in, and after meetings. 92% multitask during meetings, from checking emails to working on other tasks.

Further, 43% of devs accepted context switching as part of their fragmented workhours.

As dev attends around 8 meetings a week. Think about the extent of multitasking, and context switching done between them. This frequent context change often follows increased mental fatigue, higher error rates, lower code quality, and even negatively impacting creativity– all signs of low developer productivity and eventually poor output. Learn more about how context switching kills developer productivity.

4. Poor Developer Experience

Developer happiness too takes a hit with the rise of unproductive meetings.In a study at the University of North Carolina, Steven Rogelberg, the primary researcher wrote in great lengths about how an employee’s satisfaction is distorted with unproductive meetings. High screen time, and limited non-verbal cues are mentally taxing to most developers.

Moreover, Unproductive meetings can lead to a sense of frustration and disengagement among team members. Imagine dedicating time and effort to prepare for a meeting, only to find that it's disorganized, lacks focus, and leads nowhere. When employees repeatedly experience such scenarios, it can lower morale and dampen enthusiasm for future projects. A disheartened team is less likely to bring their best selves to the table, leading to decreased motivation and commitment to projects.

This cognitive load causes our brains to consume more energy, making us more exhausted, both physically and mentally. When devs are so exhausted, lack flow state hours, and are constantly interrupted, their overall experience, and satisfaction with work, techstack, and teammates go down. A lack of predictability in meetings, and simplicity of engineering workflows can massively distort developer’s overall experience.

In terms of project deliveries, unproductive meetings have a direct impact on developer experience. Too many meetings without a PoA can wreak havoc on project timelines. As deadlines approach, devs often scramble to catch up on the lost development work. This last-minute rush often translates into lower code quality, increased bugs, and a stressful work environment.

Fixing The Meeting Madness At Work

Much has been written about the meeting overload and its solutions: establish an agenda, have “standing” meetings, and so on. However, most of these are just quick-fixes, and at times, teams do not know how to work them through.

Real improvement requires continuous and structural change. Here’s some actionable ways to fix meeting madness, and improve developer productivity:

1. Use Data to Drive 1:1s and Team Meetings

Bringing contextual data into conversations has a transformative impact on engineering teams. These meetings are 2x effective, and require 80% less effort.

Data creates more action-oriented, relevant, and empathetic one-on-ones, where engineering managers can cater to each IC’s needs, circumstances, and requirements.

Only when EMs are equipped with relevant work analytics, they can resolve team bottlenecks by understanding the interruptions in workflows. Suddenly, the conversation shifts to– I can see your coding days were low last week. Also, the meeting hours were up by 30%. How can we streamline your days better?

Data also helps teams to recognize and celebrate each other’s work.

When team members are fully enrolled into each other’s work, meetings become more than just status updates.

With a single source of truth, teams have high workflow visibility, understand bottlenecks, and reduce frictions or communications debts owing to poor understanding of each other’s work.

That way meetings become more action-oriented, agenda-driven, and ends with concrete results, than meaningless discussions.

2. Have a Meeting Agenda

Data without context, and meetings without agenda do more harm than good to engineering teams. Data is useless if teams lack clarity on the direction they want to pivot in. Similarly, meetings without agenda are just random discussions with no purpose, direction, or concrete plan of action.

Sheryl Sanberg, Meta’s ex-COO, has been very intentional in holding agenda-driven meetings. Her spiral notebook has a list of pointers to discuss, and create plans for. She ensures the meeting stays true to the agenda, and doesn’t turn into a one-person presentation, or off-topic discussions. As soon as each item is checked, she strikes off the page, and moves to the next. As Meta’s COO, she ensured none of her meetings stretch longer than an hour despite too many items on her list.

Devs know what is expected from them, leading to a more organized and productive discussion.

3. Record Status Updates Through Async Standups

Async standups empower devs to share, and update work status without disrupting their flow state. Moreover, async work helps teams to keep a check on each other without

Please keep in mind, async communications aren’t antithetic to general team syncups, rather they serve as a middle-path where teams stay updated without wasting their flow state on hour-long status update meetings.

Hatica check-ins equips engineering teams with workflow visibility, day to day action items, team pulse, and developer workload- all at a single glasspane.

Hatica integrates with a team’s VCS, issue tracker, and communication tools to give a 360 degree picture of an engineering team’s work. It also helps engineering managers with early interventions as and when bottlenecks occur.

Want to know more about async standups? Check out how to use Hatica check-ins for daily standups.

These async standups are agenda-driven, take a few minutes, and keep everyone informed about each other’s progress, so teams can have meaningful discussions later on.

4. Use Hatica’s Maker Time Dashboard

Hatica’s maker time is built in a way to reduce engineering distractions, so devs can have their uninterrupted flow state.

With data from the maker time dashboard, devs can configure their maker time hours, control time spent in meetings, and create a balance between all core communications: meetings, 1:1, and interviews.

Moreover, EMs can create a maker time benchmark for each ICs. As soon as the line is breached, managers can take proactive steps to reduce distractions, help devs reschedule their meetings, and free up their workspace.

5. Consolidate Important Meetings

What’s worse than attending meetings? Our answer- Attending them throughout the day. Slack got so fedup with recurring meetings that they declared a calendar bankruptcy, removed all meetings from their calendar, and started from scratch. Since not everyone can afford the same luxury as Slack, the other way is to consolidate all your fragmented meetings. Most devs we know keep their second half free for meetings.

Again, using team data to understand an IC’s meeting hours is the key to reverse their fragmented schedule.

Hatica’s communication analytics help the engineering team to understand a team workday, their meeting schedule, and deep work hours.

With a transformative understanding of each other’s workday (with Hatica check-ins) teams can schedule meetings in one go, reduce their fragmented time, and achieve deep work hours. Amenify, a Silicon Valley PropTech company used async standups, consolidated meetings, and increased their flow state by 10 more hours, using Hatica. Read about Amenify’s transformation.

With help of Hatica’s communication analytics, most users have been able to creating atleast two meeting-free days a week, and 30% less meetings (again, thanks to check-ins).

Hatica: Tackle Unproductive Meetings Head On!

Productive meetings have a ripple effect on engineering teams. They empower teams to find their flow state, improve developer productivity, and reduce the overall financial burden of an organization.

When used sustainably, meetings are a tool for change. They are a power reminder that every team is built on the pillars of effective communication, harmony, and shared vision. Powerful meetings plug feedback loops, keep projects on track, and bring team alignment.

With agenda-driven meetings, and data-backed communication, large engineering teams can save upto $1600 weekly! (remember, the meeting calculator!) Teams across the globe are adopting best meeting practices to empower developers, and offer teams autonomy to work. Shopify was able to save upto 15% of their overall operating expenditure, and 322,000 hours of manpower by culling down unproductive meetings.

Streamlining communications is the first step towards engineering success. Engineering teams use Hatica to cut on unnecessary meeting time, improve deep work hours, and reduce company costs caused by unproductive meetings.

Learn more about how to cut company costs, streamline meetings, and improve developer productivity with Hatica. Request a demo here→

ChatGPT Code Interpreter Feature: 2023’s Detailed Guide

Naomi Chopra — Fri, 10 Nov 2023 08:07:18 +0000

As a developer, you'll be thrilled to know that ChatGPT has introduced a powerful new "Code Interpreter" feature that allows you to interactively run and execute code within the ChatGPT environment. This feature opens up a world of possibilities, making it easier than ever to experiment, prototype, and get instant feedback on your code snippets without leaving the ChatGPT interface, a game-changing tool for developers.

In this article, we'll explore the superpowers of the Code Interpreter, covering its activation, language support, real-time feedback, error handling, integration possibilities, and practical examples to demonstrate its capabilities and any limitations it might have. Get ready to unleash the full potential of the ChatGPT for developers.

Activating and Accessing the Code Interpreter

Getting started with the Code Interpreter is simple. When using the ChatGPT web interface, developers can access the Code Interpreter as a dedicated section where they can enter and execute code snippets.

To access the extraordinary ChatGPT plugin for the code interpreter, you'll need to have a ChatGPT Plus account, available at a monthly subscription of $20. It's important to note that OpenAI currently imposes a limit of 25 messages in a 3-hour time frame with GPT4. This limitation ensures fair access for all users and helps manage server load.

To enable the plugin, simply navigate to your settings and locate the "Beta Features" section. There, you'll find the option to activate the ChatGPT code interpreter plugin and unlock its powerful capabilities.

How To Use the ChatGPT Code Interpreter?

The code interpreter plugin is powered by generative Python and operates within a secure, sandboxed Python execution environment. While code generated by ChatGPT can access local information within the Python environment, it is strictly prohibited from connecting to the Internet to access or download external data. This safety measure ensures a controlled environment for code execution.

To put the capabilities of the code interpreter to the test, we'll perform an interactive demonstration in a moment. However, before the interpreter can operate on your data, you'll need to upload it into the secure sandbox environment. By adhering to these measures, you can confidently explore and experiment with your code while maintaining the utmost security and privacy. Let's proceed with the demonstration to witness the code interpreter in action.

Real-Time Feedback and Code Support

One of the most significant advantages of the Code Interpreter is its real-time feedback during code execution. As you enter your code snippet, the interpreter actively analyzes and executes it. If any errors or exceptions occur, the interpreter promptly displays relevant messages, enabling developers to identify and address issues on the fly.

For instance, imagine you're implementing a Python function to code a snippet of a web scraper using the BeautifulSoup library to extract news articles related to superconductors. The interpreter immediately provides

import requests
from bs4 import BeautifulSoup
from datetime import datetime, timedelta

How to Ensure Accuracy With Code Interpreter?

While the Code Interpreter strives for accuracy, its interactive nature imposes certain limitations. For example, code that is time-intensive or requires extensive system resources might encounter execution time constraints due to the shared environment.

To ensure accurate outcomes, developers can adopt best practices, such as breaking down complex code into smaller testable units. Additionally, handling exceptions gracefully contributes to more reliable results.

Suppose you're testing a complex sorting algorithm. Breaking it down into smaller functions allows you to focus on each part's behavior individually, facilitating better debugging and analysis.

Case 1: Handling Execution Time Constraints

def factorial(n):
    if n == 0:
        return 1
    else:
        return n * factorial(n - 1)

# Call the factorial function with a large number
result = factorial(1000)
print("Factorial of 1000:", result)

In the above code, calculating the factorial of 1000 will be time-consuming and may encounter execution time constraints in the code interpreter. To optimize this, you can tell it to implement a memoization technique using a dictionary to store previously calculated results:

def factorial(n, memo={}):
    if n == 0:
        return 1
    elif n not in memo:
        memo[n] = n * factorial(n - 1, memo)
    return memo[n]

# Call the factorial function with a large number
result = factorial(1000)
print("Factorial of 1000:", result)

Case 2: Breaking Down Complex Code

def merge_sort(arr):
    if len(arr) <= 1:
        return arr

    mid = len(arr) // 2
    left = arr[:mid]
    right = arr[mid:]

    left = merge_sort(left)
    right = merge_sort(right)

    return merge(left, right)

def merge(left, right):
    merged = []
    left_idx, right_idx = 0, 0

    while left_idx < len(left) and right_idx < len(right):
        if left[left_idx] < right[right_idx]:
            merged.append(left[left_idx])
            left_idx += 1
        else:
            merged.append(right[right_idx])
            right_idx += 1

    merged += left[left_idx:]
    merged += right[right_idx:]

    return merged

# Test the merge_sort function with a sample list
unsorted_list = [38, 27, 43, 3, 9, 82, 10]
sorted_list = merge_sort(unsorted_list)
print("Sorted List:", sorted_list)

In the above code, the merge sort algorithm is broken down into two functions, merge_sort and merge. This modular approach allows you to analyze each function's output and ensure that the sorting algorithm behaves as expected.

Maximizing Productivity with ChatGPT Code Interpreter

To fully harness the potential of the Code Interpreter, consider implementing these tips and tricks:

1. Rapid Prototyping

Use the Code Interpreter to quickly test ideas and concepts before implementing them in larger projects. Suppose you're developing a web application and want to validate a specific feature. By prototyping the core functionality in the Code Interpreter, you can refine the code and ensure it functions correctly before integrating it into the project.

2. API Testing

The Code Interpreter's real-time feedback makes it an excellent tool for API testing. You can interactively validate API calls and responses, ensuring the accuracy and functionality of your API implementations. This approach saves time and reduces the need for external testing tools during API development.

import requests

def get_weather_info(location, date):
    # Make an API call to fetch weather information
    response = requests.get(f"https://weather-api.com/data/{location}/{date}")

    if response.status_code == 200:
        # Parse and return the weather data
        weather_data = response.json()
        return weather_data
    else:
        return None

# Test the API call using the Code Interpreter
location = "New York"
date = "2023-07-31"
weather_info = get_weather_info(location, date)
print("Weather Information for New York on 2023-07-31:", weather_info)

3. Explore, and Implement Algorithms

Experimenting with different algorithms and data structures is crucial for developers. The Code Interpreter allows you to observe their behavior and performance interactively. For instance, you can test various sorting algorithms and evaluate their efficiency in real time, helping you make informed decisions when optimizing your code.

def bubble_sort(arr):
    n = len(arr)
    for i in range(n):
        for j in range(0, n-i-1):
            if arr[j] > arr[j+1]:
                arr[j], arr[j+1] = arr[j+1], arr[j]

    return arr

# Test the Bubble Sort algorithm with a sample list
unsorted_list = [64, 34, 25, 12, 22, 11, 90]
sorted_list = bubble_sort(unsorted_list)
print("Sorted List using Bubble Sort:", sorted_list)

4. Interactive Learning

The Code Interpreter provides an engaging platform for learning and hands-on experience. New developers can interactively explore code examples and experiment with different programming concepts. This immersive learning approach fosters a deeper understanding of coding principles.
For beginners learning Python, interactive exploration of basic concepts is valuable. Here, we experiment with Python's list comprehensions using the Code Interpreter:

# List comprehension to create a list of squares from 1 to 5
squares = [x**2 for x in range(1, 6)]
print("Squares of numbers 1 to 5:", squares)

# List comprehension to filter even numbers from 1 to 10
even_numbers = [x for x in range(1, 11) if x % 2 == 0]
print("Even numbers from 1 to 10:", even_numbers)

5. Debugging

Leveraging the Code Interpreter's real-time feedback is a great way to debug code snippets efficiently. As you encounter errors or exceptions, the interpreter guides you toward the root cause of the issue, accelerating the debugging process.
Assume you're debugging a function that calculates the factorial of a number but it's not returning the correct result. The Code Interpreter can help you identify the issue quickly:

def factorial(n):
    if n == 0:
        return 1
    else:
        return n * factorial(n - 1)

# Call the factorial function with a number
result = factorial(5)
print("Factorial of 5:", result)

In this example, the Code Interpreter will show that the result is incorrect. To fix the issue, we need to handle the case when n is less than 0 as well:

def factorial(n):
    if n == 0:
        return 1
    elif n < 0:
        return "Factorial is not defined for negative numbers"
    else:
        return n * factorial(n - 1)

# Call the factorial function with a number
result = factorial(5)
print("Factorial of 5:", result)

The Code Interpreter's real-time feedback helps you pinpoint the error and make necessary improvements in your code effectively.

Incorporating these tips and tricks with interactive code execution in the Code Interpreter can significantly enhance your productivity as a developer. It empowers you to explore, experiment, and learn more efficiently while streamlining your debugging and testing processes.

ChatGPT Code Interpreter - Your Interactive Virtual Coding Companion

The Code Interpreter in ChatGPT revolutionizes code execution, providing developers with an interactive and efficient coding experience. With its real-time feedback, multi-language support, and integration capabilities, the Code Interpreter empowers you to experiment, prototype, and learn more effectively within the ChatGPT environment. By embracing the Code Interpreter and incorporating user feedback, we collectively contribute to an ever-evolving tool that elevates the coding journey for developers worldwide.

In summary, the Code Interpreter represents a significant step forward in interactive code execution, making coding more accessible, productive, and enjoyable. Its ability to support multiple languages, provide real-time feedback, and facilitate rapid prototyping opens a world of possibilities for developers looking to streamline their development workflows and expand their coding horizons.

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Enhancing Engineering Efficiency with Velocity Metrics

Naomi Chopra — Tue, 31 Oct 2023 05:22:04 +0000

Velocity Metrics is not an engineering KPI to help you analyze your engineering team’s performance or efficiency. In fact, anyone attempting to gauge engineering efficiency by looking at Velocity Metrics in isolation may end up with terrible conclusions.

To draw an analogy, analyzing Velocity Metrics for assessing a team's performance would be the same as assessing the quality of the rap lyrics of a rapper by analyzing how many words s/he speaks per minute.

But does that mean velocity is a useless metric?

Nope.

Velocity Metrics is an amazing tool for agile project managers and engineering teams when it is looked at by clubbing with multiple other engineering metrics to assess the health of engineering processes and optimize the same.

Earlier, we had covered in detail the different metrics you need to look at in tandem with velocity to improve your engineering outcomes. In this insight, we explain ‘how to use the Velocity Metrics the right way’.

What are Velocity Metrics?

In simplest terms, Velocity Metrics are a measure of how much work an engineering team can complete in a development cycle, aka iteration, sprint.

How Velocity Metrics Are Measured?

Velocity Metrics are calculated using the story points.

Agile teams, especially those following the Scrum framework, engage in assigning a story point for each of the backlog items (also known as features or Stories). The method they use for estimating the story points may differ depending on what the Agile team finds convenient internally. The three common story points estimation methodologies include—

Planning Poker
T-Shirt Sizing
Bucket System

For Instance, Planning Poker Story Point Estimation Method Involves—

Team members individually make an estimation for the effort that may go into unit-sized components. Pay attention, it’s effort estimation and not time estimation.
The effort estimation number usually ranges between 1 to 13 (1 means least effort, and 13 means max effort).
Individual team members first assign a story point value to each of the backlog items.
Next, they work together for consensus on what should be the final story point that individual backlog items are worth (as individuals may have estimated different points).

After the story points are frozen for backlog items, a summation of story points is done for all the backlog items planned/completed 100% in one sprint (ideally 2 - 4 weeks).

Velocity Metric Terminologies

The average of the story points for 2 or more sprint cycles is called the avg Sprint velocity.
Sprint velocity estimated before actual execution is called Planned sprint velocity.
Sprint velocity calculated at the end of the Sprint is the Actual sprint velocity.
The total story points delivered by a team are called Team velocity. Team velocity shouldn’t be confused with team capacity, which is the maximum potential of the team to deliver story points. In the real world, developer burnout, technical debt, dependencies, and resource unavailability translates into lower productivity as compared to capacity.
The difference between planned sprint velocity and actual sprint velocity is Net velocity.

For Number of sprints (n), Sprint velocity (SV), and Story points (SP):

Avg SV = (SP of Sprint 1 + SP of Sprint 2 + SP of Sprint 3 + … + SP of Sprint n) / n

We have explained in detail about the sprint Velocity Metrics calculation and how you can optimize a team’s sprint velocity using Hatica. Do read that blog.

Here, let’s dive deeper into how you can use Velocity Metrics to add predictability to your project estimations, gain better visibility into your SDLC processes, improve your engineering outcomes, and learn how not to use it the wrong way.

Using Velocity Metrics The Right Way

Only 29% of projects are completed on time. After the ‘requirements analysis phase of the SDLC process, teams are required to do a rough estimation of:

How much time would it take to deliver the project?
How much would it cost?

Now, one may think that the net value of story points for the entire features list can be divided by the average sprint time to find out the number of sprints it would take to complete the project. Technically, it sounds correct, but if only things were that simple.

If you look solely at the Velocity Metrics, it will always mislead you. But if you look at it with the burndown charts or burnup charts, cycle time or lead time, and/or cumulative flow diagrams (CFDs), you may gain better insight into the health of your project.

CFD & Velocity Metric To Better Estimate Sprint Timelines

For example, let’s say the actual Velocity Metrics are way too low for the current sprint compared to the average Velocity Metrics. Now, if you look at CFDs (3-column charts — to do, WIP, done) and find out that WIP is unusually large, it means that too many backlog items are being pursued in parallel (not a good practice). If you’re an engineering manager or lead, you can check with the team and help them adopt the right approach to agile development.

Here, you may also need to look at throughput velocity and release velocity to understand if the items in WIP are actually being worked upon or not. And accordingly, looking holistically at throughput velocity, release velocity, CFD, average velocity, and team capacity metrics — you can estimate how soon the sprint will be completed.

Velocity Metrics Can Also Be Used To Adjust Project Scope and Timelines Based On Actual Team Performance.

If your team consistently completes fewer story points than expected, this could indicate that the project scope is too ambitious, or that the timeline needs to be adjusted. By using Velocity Metrics to identify these issues early on, you can help them re-evaluate project scope and timelines, and work with other stakeholders to set realistic expectations for the project.

Using Velocity Metrics To Diagnose SDLC Process Inefficiencies

In a real-world development example, consider if your team is working on a complex software project that requires integrating several different systems. Let’s say, your team lags in the velocity metric for specific stages of the SDLC process i.e., testing, and fixing bugs. After diagnosing the data, the team realizes that there are significant communication gaps between the developers working on different systems, and the QA team. This is leading to inefficient testing scripts, and a lot of bugs are going unnoticed into production, which comes back haunting as technical debt, rework, developer frustration, and project delays.

In this scenario, you can also help the team plan regular check-ins to work collaboratively and embrace modern SDLC paradigms like DevOps-led automation to streamline testing, integration delivery, and deployment (CI/CD) processes for better team efficiency & productivity.

Sprint Velocity Metrics & Developer Velocity

If the individual developer velocity i.e., story points that s/he can complete independently in a sprint for any programmer is less than the estimated velocity, it means the individual is struggling either with the bandwidth/availability challenges (maybe burnout too), or it’s a skill challenge. Accordingly, you may need to improve your resource planning or assess your organization’s learning & development initiatives.

As you would have observed, like body temperature, Velocity Metrics is a signal (symptom) and not a result (KPI) itself. But you can always analyze the velocity variance i.e., velocity metric fluctuations to identify the real problem.

How Not To Use Velocity Metrics?

Velocity Metrics are not a reflection of a developer or a team’s productivity. In fact, higher velocity doesn’t always mean faster project completion. Bloated velocity could mean low-quality code and bugs being pushed to the repository. Ultimately, this would build up the technical debt and would require a lot of rework in the future.
A team working on project ‘X’ with a higher velocity metric than a team who is working on a project ‘Y’, doesn’t mean that ‘X’ Dev team is better than the ‘Y’ Dev team. It is possible that Team X are used to working on stories that are more granular than project Y’s stories, and hence they will have higher velocity.
Solely using Velocity Metrics for estimating project timelines could be a terrible idea. Historical Velocity Metrics only account for past data. This can lead to inaccurate time estimates.
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Trying to make sense of the average velocity graph from the very beginning is not a good idea either. Average velocity graphs will be slow at the start of the project. Later, it starts gaining momentum. And a few sprints down the line, there is stability on the average velocity chart. That’s when you can start extracting true value out of it.

What’s next?

Now that you understand how to effectively use Velocity Metrics for doing more realistic project time & resource estimates, and for bridging the SDLC gaps, probably, it’s time for you to put all this into action in your next project.

How?

Say ‘Hi’ to Hatica.

You may add Hatica as your engineering analytics tool to your tech stack. It already integrates with 50+ tools & technologies to pull engineering analytics data from these platforms to help you generate, visualize, and analyze the same with interactive reports, charts, and dashboards.

In short, Hatica helps you to leverage Velocity Metrics in conjunction with other engineering metrics and enables you to—

Build data-driven high performing engineering teams.
Plan data-driven sprints

Keep developing awesome products!

8 Tips To Make Code Reviews Less Painful

Naomi Chopra — Mon, 16 Oct 2023 06:38:34 +0000

Code reviews helps ensure that code is well-written, efficient, and meets certain requirements of a project. However, the process of code reviews can be tedious and time-consuming, leading to frustration for both the reviewer and the developer. It's important to make code reviews less painful to ensure that they are productive and valuable for all parties involved.

In this article, we'll provide eight tips for making code reviews less painful, including setting clear expectations, using a code review tool, being constructive, and recognizing good work. In this article, we will discuss eight tips to make code reviews less painful.

1. Set Clear Expectations

The first step in making code reviews less painful is to set clear expectations. Providing a style guide that outlines formatting and commenting requirements for the code, and communicates the expected timeline for reviews, helps and ensures that the developer knows what he/she is expected to do during the code review process. This includes guidelines for formatting, commenting, and testing. You should also communicate the review process to developers, including how long reviews should take, and who will be reviewing the code.

From the image attached above, communication with clear expectations ensures:

The code should follow the JSDoc style guide for comments.
All functions should have a comment explaining their purpose.

2. Use a Code Review Tool

Using a code review tool can help streamline the process and make it more efficient. Platforms such as Github, Bitbucket, and GitLab, offers feature/tools that are readily available. These tools provide a centralized location for code reviews, allowing developers to collaborate and comment on each other's code, facilitate collaboration, and streamline the review process.

Use static analysis tools for faster feedback, and a strengthened codebase.

3. Don't Make it Personal

Code reviews should be focused on the code, not the developer. Avoid using personal language or criticizing the developer's abilities. Instead, focus on specific issues with the code and suggest improvements. Code reviews should be conducted in a positive and collaborative environment. You can start by praising the code for its good aspects, and then provide constructive feedback on areas that need improvement.

4. Be Constructive

When making suggestions for improvement, be constructive. Instead of simply pointing out issues, suggest ways to fix them. This will help developers learn and improve their skills. Also, never fail to encourage team members to discuss the code and share their ideas and feedback. This can help foster a sense of teamwork and improve the overall quality of the code.

5. Prioritize Issues

Not all issues in the code are equally important. Prioritize issues based on their severity and impact on the project. This will help developers focus on the most critical issues first.

6. Keep it Brief

Code reviews can be time-consuming, so it's important to keep them brief. Focus on the most critical issues and avoid nitpicking minor details. This will help developers stay focused and avoid burnout. Don't let code reviews drag on for too long. Keep them short and sweet, focusing on the most important issues, and avoiding getting bogged down in minor details.

7. Ensure Timeliness

Timeliness is key in code reviews. Don't let code reviews drag on for too long, as this can lead to frustration and delays in the project. Set deadlines for reviews and stick to them.

8. Recognize Good Work

Finally, it's important to recognize good work. When developers write high-quality code, acknowledge it and praise them for their efforts. This will help build morale and encourage developers to continue writing excellent code. Celebrate when a code review is completed successfully, as this can help create a positive atmosphere and motivate team members to continue participating actively in the code review process.

In conclusion, code reviews are a necessary part of software development, but they don't have to be painful. By setting clear expectations, using a code review tool, avoiding personal language, being constructive, prioritizing issues, keeping it brief, being timely, and recognizing good work, you can make code reviews a positive and productive experience.

An engineering analytics platform can empower engineering teams with effective code reviews, developer productivity, and boost overall engineering effectiveness.

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Inefficient Developer Workflows: The #4 Productivity Killer

Naomi Chopra — Thu, 12 Oct 2023 08:50:58 +0000

It’s no secret that engineers love to code, and sharpen their problem solving skills. They love the thrill of building omnipresent applications, chauffeuring their creative side, and making the world a better, and tech-enabled place with all the innovative stuff they build. They form a special bond with their VCS, the tools used, processes involved, and most importantly, their workflows that help them code better and faster, with time to groom, and innovate.

But here's the catch. Like any other relationship, this fusion is driven by a lot of pain-points as much as it is fun. Engineers can only do their best work, when they have a supportive workflow.

Workflows are meant to help devs work better, smarter, and faster while keeping up with the dynamic nature of the tech industry. However, it is too perfect to be true. Today, 92% of teams are dealing with inefficient developer workflows, leading to loss of productivity, delayed software delivery, average product quality, and even developer burnout.

So, our guide explains what developer workflow is, effects of inefficient developer workflows and how to improve it to maximize developer productivity.

What Is The Developer Workflow?

The developer workflow involves processes, tools, or methodologies a developer uses right from creating their first codebase to final deployment, testing, and dispatching it to customers.

It's the series of steps a developer takes to turn their ideas into functioning software. Team size, project type, and even a developer’s personal style of doing things have a lot of say in how modern day workflows turn out.

Different developer workflows together constitute the development cycle, with the former impacting the efficiency of the whole SDLC. A typical developer workflow includes:

Planning for your project: How to get started, outlining project requirements, the timelines involved, and identifying milestones
Coding involves writing and testing code, with best practices in place to ensure code quality and consistency and achieve industry standards
Testing to track down any pesky bugs hindering your software's performance. Error-free products go a long way in building customer experience- something all PLG teams are striving for today.
Deployment involves monitoring, troubleshooting, maintenance, and releasing the software to production.
Now that we have seen an overview of a typical developer workflow, let’s see how it impacts engineering productivity.

Reasons for Inefficient Developer Workflows

Inefficient developer workflows can be attributed to various factors that hinder productivity and delay the smooth execution of projects.

Let's explore some common reasons behind such inefficiencies:

1. Use of outdated tools and technology:

Outdated tools and technology can significantly impact developer workflows. Legacy systems, obsolete software, and inefficient tools can slow down development processes, hinder productivity, and limit the adoption of modern practices. Upgrading to newer tools and technologies that facilitate automation, streamline tasks, and support efficient collaboration can help overcome these workflow inefficiencies.

2. Inadequate training and organization:

Insufficient training and organization within development teams can contribute to inefficiencies. When team members lack the necessary skills and knowledge to handle tasks efficiently, it can lead to delays, errors, and suboptimal outcomes. Moreover, a lack of well-defined processes and project organization can result in confusion, unclear responsibilities, and a lack of clarity in workflows.

3. Overcomplicated processes:

Overcomplicated processes can hinder developer workflows by introducing unnecessary complexity and overhead. Cumbersome approval processes, excessive documentation requirements, and redundant steps can drain time and resources, impeding the progress of projects. Simplifying and streamlining workflows by eliminating non-value-added activities can help alleviate these inefficiencies.

4. Unplanned changes and requirements:

Unplanned changes and requirements can disrupt workflows and cause inefficiencies. Frequent scope changes, last-minute additions, and inadequate project planning can lead to shifting priorities, rework, and delays. It's essential to establish effective change management processes, conduct thorough requirements analysis, and maintain clear project objectives to minimize these disruptions and keep workflows on track.

5. Too Many Manual Processes

Lack of automation is the number #1 red flag for any team workflow. 90% of devs are already overwhelmed with repetitive tasks, they have to cast hours aside for work that can be easily automated, or put on auto-pilot. Even after so many hauls and cries around automation, still 40% of workers spend a quarter of their week on manual tasks, and lack fully automated builds, and deployment- all leading to a lot of wasted hours for developers.

The Cost Of Inefficient Developer Workflows

Inefficient workflows are a known developer productivity killer. Devs constantly strive to produce higher quality software in minimal time, however, with redundant processes, and friction-filled hand-offs, the idea just seems a distant dream.

Let us understand the effects of inefficient developer workflows and how developers pay the price for slow workflows.

1. Technical debt

Technical debt is the burden devs share to maintain and update old or inefficient code. For 60% of devs, technical debt is a dent on their growth. Technical debt is a common occurrence in teams having a lack of proper code practices in place, and slows down any tech-driven initiative.

Technical debt is both a cause and result of inefficient workflows where engineering teams have to resort to shortcuts to just get work done.

2. Flaky Builds

Take a software team working on some new product feature, and one of the automated tests fails intermittently. The team will spend hours trying to diagnose the issue, leading to delayed project timelines, and frustrated devs. That’s what flaky builds do to software teams- slowing them down, rising team frictions, and causing a dent on developer productivity.

3. Rising Communication Debt

Inefficient workflows cause communication issues between team members, snowballing into reduced cohesion among members, hassled hand-offs, and even delayed software delivery. That’s simply inexcusable for companies who are already struggling with higher developer attrition, and low engineering efficiency in the era of “The Great Resignation”.

Code reviews bear the brunt of communication barriers in a team, even harming the overall code quality. This constant back and forth with no definite results can disempower devs from producing their best work. Today, 45% of developers struggle with the issue as they find it difficult to track their colleagues for hand-offs, and information.

4. No Policies on Standardization

An engineering team has an inconsistent coding process, they do not have documented code review practice in place, and no one knows what their teammates are working on. A reviewer is struggling to understand the context behind a PR sent by the commit owner, so he keeps it on the back burner till it's high time.

Devs might feel lost and overwhelmed if the workplace lacks documentation, or standard processes to get work done. For them searching for proper documentation is more painful than muscle cramps post workout.

3 Tips to Improve Developer Workflows

Here's how developers can bring efficiency into their workflows and improve their productivity:

1. Automating Code Review Process

Companies big or small have to deal with dysfunctional code reviews at some point of time. The ‘pain’ behind these reviews is a symptom that you need to churn out your workflows. Microsoft’s development team too had to deal with the burning issue. However, the team then went on to streamline their workflow through an internal tool called CodeFlow. They automated the whole review cycle, and plugged feedback loops for better handshakes. As a result, the team reduced their review time by 90%, while delivering constant quality.

Use automation, wherever possible, especially in reviews and testing to get work done faster. Automating repetitive tasks also helps devs to reserve some free time for their growth and development.

Engineering teams can optimize their code review workflow through pull request templates and automated testing frameworks.

2. Find a balance between writing code and maintaining documentation

Developers often struggle with maintaining and handing over documentation, and simply see it as a tertiary task. However, documentation is a necessary evil to have sustainable workflows. Use automated tools like Swagger or Javadoc to generate documentation as you code. The idea is to free up at least 20-40% of development time via standardization, and scale without hassles as teams move ahead.

The increased traceability can also help teams grow the speed of processing, and consuming documents- all leading to better workflows, and enhanced productivity.

Atlassian recently created "Code Insights" to help developers understand the impact of their code changes on documentation. The results were pretty obvious! 30% increase in code quality and a 25% decrease in the time it takes to update documentation.

3. Embrace agile

Agile sprint has already put enough emphasis on collaboration, flexibility, and rapid iteration. By breaking down development into smaller, manageable chunks, developers can anticipate issues better, work more productively and deliver software frequently.

Spotify used agile methodology to root out any bottleneck in their traditional workflows. By introducing two-week sprints, and a compulsory sprint review coupled with IC-EM 1:1s, the team managed to boost their customer experience by 50%.

Spotify is the best example of how bringing workability into developer flows can go a long way in building product resonance among customers.

Conclusion

In 2021, Garter published a report on how to retain engineering talent, while ensuring they remain productive throughout. The results were staggering, but predictable!

The reports confirmed how investing in pro-developer technologies, and improving their workflows can help in achieving business outcomes. Developer workflows are complex, and bringing chaos into this madness is not a one time deal. It requires consistent efforts, conversations with devs, and regular feedback.

Shopify has a "handoff template" to ensure feedback is taken at the right time, team work in cohesion with each other. The idea is to plug any gap between developers, managers, QA teams, or designers for all purposes, from review to testing, and debugging. Engineering teams can create their very own handoff templates suiting their needs.

Engineering efficiency today is directly related to developer productivity, and coherent workflows. For teams looking to optimize their resources, improve productivity while boosting delivery results- creating functional workflows are the way to start.

An engineering analytics platform can help teams take care of their inefficient workflows with ease. Hatica supports seamless code review collaborations by bringing the commit owner, and reviewer at the same place, with advanced capabilities to track PR changes, and ensuring code reviews aren’t treated as just another secondary work.

Moreover, Hatica offers data-driven insights with 130+ metrics across 13+ dashboards. By analyzing code metrics and team’s work trends, Hatica can help identify bottlenecks in a team’s workflow: did the dev have enough time to code and groom? Are there too many meetings? Where are devs spending their time?, or why are developers blocked.

Hatica collates all team-related data at one place so teams know where exactly they are stuck, and how to achieve a better review flow for the team.

Engineering teams today cannot afford to ignore their development workflow. In a market with cut-throat competition, every day counts, and optimizing your developer workflow can help your days be more productive, and even happier. When workflows are pro-devs, features ship faster, software are delivered in time, and customer experience shoots up organically.

FAQs

1. What are the primary benefits of efficient developer workflows?

Efficient developer workflows offer numerous benefits. They enhance productivity by minimizing time wasted on unnecessary tasks and streamlining processes. They improve collaboration and team communication, leading to better coordination and faster project completion. Efficient workflows reduce errors and bottlenecks, improving code quality and improving project outcomes.

2. How can I identify bottlenecks in my team's workflow?

To identify bottlenecks in your team's workflow, closely monitoring the entire development process is essential. Analyze the flow of tasks, communication, and dependencies between team members. Look for areas where delays or inefficiencies occur frequently. Gathering feedback from team members and conducting regular retrospectives can also help pinpoint bottlenecks and areas for improvement.

3. How can I avoid common mistakes in developer workflows?

To avoid common mistakes in developer workflows, it's crucial to prioritize clear communication, establish well-defined processes and standards, and regularly evaluate and refine workflows based on feedback and lessons learned. Encourage continuous learning and professional development within the team to stay updated with industry best practices. Regularly assess and address any identified bottlenecks or inefficiencies to ensure a smooth and optimized workflow.

Use engineering analytics to save the day.

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