Focusing on incorrect developer performance metrics can cause critical issues. Look at fundamental metrics to improve working processes and keep a positive environment.

We often see how developer productivity directly affects a company's success or, sometimes, failure. If developers use time efficiently, the team can keep up with the competitors, which causes delays in providing the features users wait for.

When workforce reduction is necessary, tech leaders should decide who stays and who leaves. They must be cautious not to mistakenly let go of high performers that drive company growth and make cuts based only on seniority or experience. Mistakes here are critical for a company's well-being or existence, so making data-based and unbiased decisions is mandatory.

But how do you evaluate something so abstract, complex, and creative as software development? Let's take a look.

What's wrong with measuring productivity

Whether you choose DORA, SPACE, McKinsey's, or another framework, remember it might not be a good fit for your team, especially because many developers either need to learn about them or think they don't work.

One more challenge here lies in the collaborative nature of the software development process, which is hard to track. It's far too easy to fall into the trap of wrong practical use, for example, by applying DORA to measure an individual's performance.

Finally, once you announce a new productivity metric, your brilliant team can try to artificially high their scores to prove their proficiency. Don't blame them; you'd likely do the same if you were in their shoes, and let's think how to deal with this.

Stop Tracking Vanity Metrics

First of all, here are a few examples of vanity metrics that may seem convincing, but their results don't reflect the accurate picture and miss the fundamental goal of measurement:

Hours worked. Yes, it's easy to track, but we know that long hours don't directly correlate with productivity.

Lines of code. Lengthy code can highlight poor quality, and measuring this leads engineers to adopt bad practices to meet their KPIs.

Bugs fixed. Bugs have different levels of complexity and importance, and this quantity metric likely gives your team the illusion of hard work.

Deployment frequency. While valuable for assessing teams or systems, deployment frequency relies on everyone doing their responsibilities and is not a reliable metric of individual productivity.

Focus on metrics that matter

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Just say no to overthinking. Instead of reinventing the wheel, choose a few metrics and try how they fit your development team. Gather information from last one-to-one meetings, peer reviews, and self-assessment surveys as a starting point. At Redwerk and QAwerk, we collect this kind of qualitative data every 3 months. We've been doing it for the past 19 years. You'll have enough data to clarify how well the software engineer manages their tasks, how involved and proactive they are, and what skills they need to improve.

Sticking to Deadlines
Developers should estimate how long features will take before starting tasks to set realistic expectations. Regularly missing deadlines leads to bottlenecks, chaos, and long-term team burnout. Reviewing historical data and conducting a thorough investigation can help managers determine whether inaccurate estimates or the developer's speed is the issue. However, sometimes the problem with estimation extends beyond a single engineer's role and requires more attention from tech leaders.

Code quality
Code quality seriously influences product metrics and, as a result, business success metrics too. Poorly written code is hard to reuse and will need costly refactoring. Likewise, new teammates may spend hours deciphering it, which delays feature development. To avoid this, teams conduct regular peer reviews or involve experts in complicated code audits.

Monitoring the bug rate (number of bugs detected during testing) is a different way to know code quality issues. This metric, usually a ratio or percentage of bugs per unit, like lines of code, indicates possible codebase issues. A high bug rate suggests serious defects, while a low rate shows better stability.

Problem-solving skills
With so many AI-powered code assistants available, writing quality code that meets industry standards has become much easier for developers. The seniority of an engineer now means their problem-solving abilities. Do they quickly mark challenges as "impossible" or deep dive into research and develop solutions?

Interpersonal skills
A top performer consistently delivers not only quality code but also works seamlessly with teammates. They proactively communicate possible threats or blocks, offer improvements to feature implementations with clear reasoning, and are always ready to help their team reach goals.

Final thoughts

Measuring developer productivity has just one reason: it's a long-term success. It's not about comparing one engineer to another based on their metrics. Use this to identify factors that might boost productivity. It should also be a collaborative effort between managers and developers to build an environment where every developer can grow and help drive the company's success.