Developer Productivity Metrics

Has Amazon cracked the code on developer productivity with its cost to serve software (CTS-SW) metric? Amazon applied its well-known "working backwards" methodology to developer productivity. "Working backwards" in this case starting with the outcome: concrete returns for the business. This is measured by looking at the rate of customer-facing changes delivered by developers, i.e. "what the team deems valuable enough to review, merge, deploy, and support for customers", in the words of the blog post by Jim Haughwout https://lnkd.in/eqvW5wbi . This metric is different from other measures of developer productivity which look only at velocity or time saved. Instead, "CTS-SW directly links investments in the developer experience to those outcomes by assessing how frequently we deliver new or better experiences. Some organizations fall into the anti-pattern of calculating minutes saved to measure value, but that approach isn’t customer-centered and doesn’t prove value creation." This aligns with Gartner's own research on developer productivity. In our 2024 Software Engineering survey, we asked what productivity metric organizations are using to measure their developers. We also asked about a basket of ten success metrics, including software usability, retention of top performers, and meeting security standards. This allowed us to find out which productivity metric was associated most with success. What we found in our survey was that *rate of customer-facing changes* is the metric most associated with success. Some other productivity metrics were actually *negative associated* with success. But *rate of customer-facing changes* is what organizations should focus on. Sadly, our survey found that few organizations (just 22%) use this metric. I presented this data at our #GartnerApps summit [and the next summit is coming up in September: https://lnkd.in/ey2kpc2 ] Every metrics gets gamed. So I always recommend "gaming the gaming". A developer might game the CTS-SW metric by focusing more on customer-facing changes. But... this is actually a good thing. You're gaming the gaming. We will be watching closely how this metric gets adopted alongside DORA, SPACE, and other metrics in the industry.

“Developer productivity will be measured by the number of git commits per day” Thinking about this almost gets me so fired up that I can’t think straight. Replace “git commits” with “lines of code” - same thing. I had heard of this happening but each time it sounded more like an urban legend than reality. Until someone told me first hand that a manager at their workplace was thinking of implementing it. I told them to resign. Immediately. There is just no room for mincing words. If you’re considering this you should not be leading developers in any capacity and you should hire someone who knows what they’re doing. Any developer can commit as many commits as they need to to meet your metric. Every keystroke could be a git commit if they need it to be. Thousands of commits per day. Smart developers will even write an app that can automatically split a days work into many commits. Senior developers spend significant time preventing your project from failing. Sometimes that is with code, but often it’s meetings with other teams, helping guide other engineers away from disaster or designing architecture so that the system can scale appropriately. None of those activities are represented in git. However they are more critical to team productivity and project success than code output. Developers who bang out code without thinking do not usually arrive at the best, cheapest or fastest option. There are usually many potential ways to solve a problem and spending some time thinking about it is more likely to get better results. Thinking time and considering options are not represented in git. Hire leaders who understand what it takes to motivate software teams, support them when production explodes and what it takes to build a culture that grows high performing teams. Hire engineering leaders who know what productivity looks like, how it can be measured, and what needs to be changed to see it improve. Fire any leader that would use git commits or lines of code to measure productivity. If you don’t want to fire them, make sure they never step anywhere near a software team, or you may find many resignation letters in your inbox.

Redefining Productivity in Software Development: Beyond Lines of Code In the fast-paced world of software development, productivity often gets measured in lines of code, features shipped, and bugs fixed. But, is this truly the hallmark of our best developers? 1. **Innovative Problem-Solving**: The most valuable developers are those who solve problems in ways that prevent future issues, potentially reducing the need for additional code. Encourage a culture where innovative solutions are celebrated over sheer output. 2. **Mentorship and Collaboration**: Exceptional developers elevate the skills of those around them. By mentoring juniors, they multiply their own productivity across the team. Recognise and reward the role of mentorship in your team’s success metrics. 3. **Technical Debt Reduction**: Often overlooked, the effort to reduce technical debt is a long-term investment in productivity. Developers who focus on clean, maintainable code ensure faster and more reliable output in the future. Shift your performance metrics to value quality and sustainability. 4. **Strategic Contributions**: The strategic insight that senior developers bring to a project can far outweigh the immediate productivity of coding. Their contributions to architecture, technology selection, and process improvement can set the stage for exponential growth. Let’s start valuing the qualities that truly enhance our teams. Foster an environment where strategic thinking, mentorship, and innovation are as prized as coding speed.

🔥 We tracked one of our senior engineers for a week. Not surveillance. A voluntary experiment. He logged every interruption. Every context switch. Every "got a minute?" The results were painful. 📊 Monday through Friday: → 40 hours "at work" → 11 hours in meetings → 6 hours responding to Slack → 4 hours on code reviews → 2.5 hours on email and admin → Deep, uninterrupted coding: 8 hours. Total. For the week. 😤 8 hours of deep work in a 40-hour week. And we were paying him a senior engineer's salary to spend 80% of his time NOT engineering. 🤔 The math that changed our approach: Every context switch costs ~23 minutes to regain deep focus. He was switching context 7-8 times per day. That's 2.5+ hours of daily "reboot time" — pure waste. ✅ What we changed: → No-meeting mornings (9am-12pm = sacred deep work) → Slack goes async by default. Urgent = call. → Code reviews batched to 2pm-3pm → Questions collected and asked in batches, not one-by-one 📊 Result after 3 months: → Deep work went from 8 hours/week to 22 hours/week → Feature velocity nearly doubled → Engineer satisfaction went up (people like building things) Your best engineers don't need more hours. They need fewer interruptions. What would your team's deep-work-to-meeting ratio look like if you measured it? #Engineering #DeepWork #Productivity #CTO

The numbers don’t lie. Only 6% of engineering leaders saw real productivity gains from AI tools – despite the hype. I remember the day our team rolled out our first AI code assistant. We’d read the headlines. Heard the promises. Thought we’d finally crack the code on developer productivity. Spoiler: We failed. Not because the tools were bad. But because we skipped step one: understanding the real pain points. Here’s what we learned the hard way: 11 months earlier, I sat in a meeting where developers begged for help with code reviews. Our average cycle time? 7 days. Half that time was spent chasing down trivial issues. I pushed an AI tool that promised to automate 80% of the process. Skepticism hit hard. One developer asked, “Will this thing even understand our legacy codebase?” Another muttered, “Here comes another shiny toy that won’t fix our real problems.” The first month? False positives flooded Slack. Confusion over code ownership spiked. Productivity dropped 12%. Then came the twist. We paused. Listened. Turned our roadmap upside down. Instead of forcing AI into their workflow, we let developers show us where it could help. Turns out, they hated writing unit tests most. We pivoted. Three weeks later, an AI tool that auto-generates test cases cut testing time by 65%. The same team that resisted suddenly asked, “Can we use this for API docs next?” The real breakthrough? Trust grew when we stopped selling solutions and started solving problems. Now when I see headlines claiming AI tripled productivity, I think of that 7-day code review. Real impact doesn’t come from flashy features. It comes from knowing where your team bleeds time. From letting developers lead the way. From realizing AI isn’t magic – it’s a mirror. The tools work. But only when you point them at the right problems. Your developers already know where to aim. Are you listening? P.S. If you’re stuck chasing productivity gains that never materialize, I’ve got a free AI readiness assessment that might help. Let’s talk.

𝐃𝐞𝐯𝐎𝐩𝐬 𝐌𝐞𝐭𝐫𝐢𝐜𝐬 𝐓𝐡𝐚𝐭 𝐀𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐌𝐚𝐭𝐭𝐞𝐫 Most teams measure the wrong things. They track commits per day, lines of code, hours spent deploying. These are vanity metrics—they show activity, not impact. 𝐇𝐞𝐫𝐞'𝐬 𝐰𝐡𝐚𝐭 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐦𝐚𝐭𝐭𝐞𝐫𝐬: 𝐃𝐎𝐑𝐀 𝐌𝐞𝐭𝐫𝐢𝐜𝐬 (The only 4 metrics proven to predict software delivery performance) --- 𝐓𝐇𝐄 𝟒 𝐃𝐎𝐑𝐀 𝐌𝐄𝐓𝐑𝐈𝐂𝐒: 𝟏. 𝐃𝐄𝐏𝐋𝐎𝐘𝐌𝐄𝐍𝐓 𝐅𝐑𝐄𝐐𝐔𝐄𝐍𝐂𝐘 How often you deploy to production ✅ High frequency = faster feedback loops ✅ Indicates automation maturity Elite teams: Multiple times per day Low performers: Once per month 𝟐. 𝐋𝐄𝐀𝐃 𝐓𝐈𝐌𝐄 𝐅𝐎𝐑 𝐂𝐇𝐀𝐍𝐆𝐄𝐒 Time from commit → production ✅ Shorter lead time = faster value delivery ✅ Shows pipeline efficiency Elite teams: Less than 1 hour Low performers: 1-6 months 𝟑. 𝐂𝐇𝐀𝐍𝐆𝐄 𝐅𝐀𝐈𝐋𝐔𝐑𝐄 𝐑𝐀𝐓𝐄 % of deployments causing incidents ✅ Low failure rate = quality releases ✅ Stability over speed Elite teams: 0-15% Low performers: 46-60% 𝟒. 𝐌𝐄𝐀𝐍 𝐓𝐈𝐌𝐄 𝐓𝐎 𝐑𝐄𝐂𝐎𝐕𝐄𝐑𝐘 How fast you recover from failure ✅ Fast recovery > zero failures ✅ Resilience matters Elite teams: Less than 1 hour Low performers: 1 week to 1 month --- 𝐇𝐎𝐖 𝐓𝐎 𝐈𝐌𝐏𝐋𝐄𝐌𝐄𝐍𝐓 𝐃𝐎𝐑𝐀 𝐌𝐄𝐓𝐑𝐈𝐂𝐒 Week 1: Measure Current State → Calculate your baseline DORA metrics → Identify your biggest bottleneck → Set improvement targets Week 2-4: Automate → CI/CD pipeline (reduce lead time) → Automated testing (reduce failure rate) → Monitoring & alerts (reduce MTTR) Month 2+: Optimize → Increase deployment frequency gradually → Reduce batch sizes → Improve observability → Build blameless post-mortem culture --- What DORA metric is your team struggling with most? Drop a comment—let's discuss how to improve it. ♻️ Repost if you found it valuable ➕ Follow Jaswindder for more insights #DevOps #DORAMetrics #CloudEngineering #SoftwareDelivery #ContinuousDeployment

Most engineering teams have a blind spot: They don’t actually measure their software delivery performance. They assume that because they ship code, things are working fine. But have you ever deployed a "working" update, only to spend the next two days fixing what it broke? DORA’s research proves that gut feelings aren’t enough. They’ve identified four key metrics that predict high-performing teams: → Change Lead Time – How long does it take for a commit to go live? → Deployment Frequency – How often do you ship changes? → Change Failure Rate – How often do deployments break things? → Mean Time to Recovery – How fast do you fix failures? High-performing teams don’t choose between speed and stability. They improve both. And they measure their progress so they know they’re actually improving. The best part? These metrics work for any engineering team—whether you’re building a mobile app, a banking system, or a machine learning model. The key isn’t just to measure—it’s to measure wisely. And that’s where many teams stumble: → They set vanity targets (e.g., “Every app must deploy daily!”) instead of focusing on real improvement. → They use a single metric instead of a balanced set. → They compare teams with completely different constraints. → They measure obsessively but never take action. Avoid these traps. The goal isn’t just to track numbers. It’s to build better software, faster—and with confidence. Track what matters. Take action. Deliver with speed and stability. #DevOps #SoftwareEngineering #ContinuousDelivery #EngineeringLeadership

Agile Metrics That Actually Matter (& the Ones That Mislead Teams): Agile teams love metrics. Velocity charts. Burndowns. Story points completed. But here’s the uncomfortable truth: 👉 Many teams measure activity… not agility. Being busy is easy to measure. Learning, flow, and value delivery are harder but far more important. Here is how to separate useful signals from misleading noise. 🟢 Metrics That Show Real Agility These align with DORA metrics and help teams improve flow, quality, and customer value. ☑️ Lead Time: How long it takes for an idea to reach the customer. Shorter = faster learning. ☑️ Cycle Time: How long work takes once it’s "In Progress." This reveals where your process is actually "leaking" time. ☑️ Work Item Age: How long current tasks have been sitting in the sprint. Highlights stuck work before the sprint ends. ☑️ Escaped Defects: Bugs found in production. This measures the health of your system, not just the skill of your coders. ☑️ Customer Adoption: Are people actually using the feature? This is the only metric that proves Value. 🔴 Metrics That Often Get Misused These aren't "bad," but they become toxic when used as performance targets or for cross-team comparisons. ⭕ Velocity: Great for internal team forecasting. Harmful when used to pressure teams to "do more" next sprint. ⭕ Story Points Completed: Measures estimation effort, not impact. You can finish 100 points and still deliver zero value. ⭕ % Utilization: A 100% utilized highway is a traffic jam. High utilization creates bottlenecks and burnout, not productivity. ⭕ Hours Logged: Measures presence, not outcomes. Time spent $\neq$ value delivered. 👉 The Real Difference Good Agile metrics help teams learn, adapt, and improve the system. Misused metrics pressure teams to look busy without actually getting better. Agility isn’t about how much work gets done. It’s about how smoothly value flows from idea to impact. Before adding a new metric, ask: "Is this helping us improve the system or just helping us micromanage activity?" 👇 Which metric has caused the most confusion in your experience? Let’s discuss in the comments. #Agile #SoftwareDevelopment #DevOps #ProductManagement #DORAmetrics

My approach to developer productivity metrics has changed a lot in the last few years. I used to recommend that leaders go deep in the research — SPACE, DORA, DevEx — to come up with their own list of metrics that fits their leadership and business needs. But now we have more information about what’s actually working in the field, and my guidance has changed. I have a very clear answer about what to measure, and it's the DX Core 4 framework. DX Core 4 unifies SPACE, DORA, and DevEx, and gives you a prescriptive list of key metrics to track. 🔹 Robust: Four dimensions that hold each other in tension for a comprehensive view into performance. 🔹 Easy to deploy: Get a baseline in weeks, not months. 🔹 Balanced: Qualitative and quantitative data to tell you not just what’s going on, but why, so that you can improve. 🔹 Peer benchmarks: See industry 50th, 75th, and 90th percentile values, including segmentation for size, sector, and even mobile engineers. This framework is based on years of research and field experience from real companies using metrics in their day-to-day operations. This framework was developed by Abi Noda and me, with collaboration from the creators of DORA, SPACE, and DevEx, and feedback from experts and our incredible DX customers. Read more here: https://lnkd.in/dSbr8aAD

Most measurement problems aren’t about numbers or methods—they’re about unclear goals and intent. But it’s easier to argue over the measurement or chase the illusion of a perfect method than to confront those implicit goals. I see this happen every day when discussing developer productivity and developer experience. Imagine three people with different goals and intent. X: "We need to measure developer productivity so we can compare teams and figure out who’s underperforming—if someone isn’t pulling their weight, we need to know." Y: "We want to measure developer productivity to identify blockers in our workflows and make it easier for teams to deliver value without burning out." Z: "We need to measure developer productivity to justify our investment in engineering and ensure leadership understands the ROI of what we’re building." If you lump these all under a "measure developer productivity", then don't be surprised when three months later you're debating the value of metrics A, B, and C.