How to Measure Software Engineer Productivity

Step-by-Step Guide to Measuring & Enhancing GCC Productivity - Define it, measure it, improve it, and scale it. Most companies set up Global Capability Centers (GCCs) for efficiency, speed, and innovation—but few have a clear playbook to measure and improve productivity. Here’s a 7-step framework to get you started: 1. Define Productivity for Your GCC Productivity means different things across industries. Is it faster delivery, cost reduction, innovation, or business impact? Pro tip: Avoid vanity metrics. Focus on outcomes aligned with enterprise goals. Example: A retail GCC might define productivity as “software features that boost e-commerce conversion by 10%.” 2. Select the Right Metrics Use frameworks like DORA and SPACE. A mix of speed, quality, and satisfaction metrics works best. Core metrics to consider: • Deployment Frequency • Lead Time for Change • Change Failure Rate • Time to Restore Service • Developer Satisfaction • Business Impact Metrics Tip: Tools like GitHub, Jira, and OpsLevel can automate data collection. 3. Establish a Baseline Track metrics over 2–3 months. Don’t rush to judge performance—account for ramp-up time. Benchmark against industry standards (e.g., DORA elite performers deploy daily with <1% failure). 4. Identify & Fix Roadblocks Use data + developer feedback. Common issues include slow CI/CD, knowledge silos, and low morale. Fixes: • Automate pipelines • Create shared documentation • Protect developer “focus time” 5. Leverage Technology & AI Tools like GitHub Copilot, generative AI for testing, and cloud platforms can cut dev time and boost quality. Example: Using AI in code reviews can reduce cycles by 20%. 6. Foster a Culture of Continuous Improvement This isn’t a one-time initiative. Review metrics monthly. Celebrate wins. Encourage experimentation. Involve devs in decision-making. Align incentives with outcomes. 7. Scale Across All Locations Standardize what works. Share best practices. Adapt for local strengths. Example: Replicate a high-performing CI/CD pipeline across locations for consistent deployment frequency. Bottom line: Productivity is not just about output. It’s about value. Zinnov Dipanwita Ghosh Namita Adavi ieswariya k Karthik Padmanabhan Amita Goyal Amaresh N. Sagar Kulkarni Hani Mukhey Komal Shah Rohit Nair Mohammed Faraz Khan

How to compare your eng team's velocity to industry benchmarks (and increase it): Step 1: Send your eng team this 4-question survey to get a baseline on key metrics: https://lnkd.in/gQGfApx4 You can use any surveying tool to do this—Google Forms, Microsoft Forms, Typeform, etc.—just make sure you can view the responses in a spreadsheet in order to calculate averages. Important: responses must be anonymous to preserve trust, and this survey is designed for people who write code as part of their job. Step 2: Calculate your how you're doing. - For Speed, Quality, and Impact, find the average value for each question’s responses. - For Effectiveness, calculate the percent of favorable responses (also called a Top 2 Box score) across all Effectiveness responses. See the example in the template above. Step 3: Track velocity improvements over time. Once you’ve got a baseline, you can start to regularly re-run this survey to track your progress. Use a quarterly cadence to begin with. Benchmarking data, both internal and external, will help contextualize your results. Remember, speed is only relative to your competition. Below are external benchmarks for the key metrics. You can also download full benchmarking data, including segments on company size, sector, and even benchmarks for mobile engineers here: https://lnkd.in/gBJzCdTg Look at 75th percentile values for comparison initially. Being a top-quartile performer is a solid goal for any development team. Step 4: Decide which area to improve first. Look at your data and using benchmarking data as a reference point, pick which metric you believe will make the biggest impact on velocity. To make this decision about what to work on to improve product velocity, drill down to the data on a team level, and also look at qualitative data from the engineers themselves. Step 5: Link efficiency improvements to core business impact metrics Instead of presenting these CI and release improvement projects as “tech debt repayment” or “workflow improvements” without clear goals and outcomes, you can directly link efficiency projects back to core business impact metrics. Ongoing research (https://lnkd.in/grHQNtSA) continues to show a correlation between developer experience and efficiency, looking at data from 40,000 developers across 800 organizations. Improving the Effectiveness score (DXI) by one point translates to saving 13 minutes per week per developer, equivalent to 10 hours annually. With this org’s 150 engineers, improving the score by one point results in about 33 hours saved per week. For so much more, don't miss the full post: https://lnkd.in/grrpfwrK

🛠️ Measuring Developer Productivity: It’s Complex but Crucial! 🚀 Measuring software developer productivity is one of the toughest challenges. It's a task that requires more than just traditional metrics. I remember when my organization was buried in metrics like lines of code, velocity points, and code reviews. I quickly realized these didn’t provide the full picture. 📉 Lines of code, velocity points, and code reviews? They offer a snapshot but not the complete story. More code doesn’t mean better code, and velocity points can be misleading. Holistic focus is essential: As companies become more software-centric, it’s vital to measure productivity accurately to deploy talent effectively. 🔍 System Level: Deployment frequency and customer satisfaction show how well the system performs. A 25% increase in deployment frequency often correlates with faster feature delivery and higher customer satisfaction. 👥 Team Level: Collaboration metrics like code-review timing and team velocity matter. Reducing code review time by 20% led to faster releases and better teamwork. 🧑💻 Individual Level: Personal performance, well-being, and satisfaction are key. Happy developers are productive developers. Tracking well-being resulted in a 30% productivity boost. By adopting to this holistic approach transformed our organization. I didn’t just track output but also collaboration and individual well-being. The result? A 40% boost in team efficiency and a notable rise in product quality! 🌟 🚪 The takeaway? Measuring developer productivity is complex, but by focusing on system, team, and individual levels, we can create an environment where everyone thrives. Curious about how to implement these insights in your team? Drop a comment or connect with me! Let’s discuss how we can drive productivity together. 🤝 #SoftwareDevelopment #Productivity #TechLeadership #TeamEfficiency #DeveloperMetrics

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.