The 20 most popular developer productivity metrics: a practical reference for leaders
Measuring developer productivity is not optional in modern software development. While the topic certainly generates heated debate and resistance, organizations that don’t measure developer productivity lack the insights to make successful decisions about their engineering investments. Not measuring developer productivity is unacceptable for high-performing engineering organizations and their leaders.
This guide provides a practical reference for engineering leaders looking to implement developer productivity metrics at their organization. While frameworks like DORA (DevOps Research and Assessment) and SPACE offer insights into the academic research behind metrics, we’ll focus on presenting the most prominent metrics with benchmarks and guidance on their pros and cons to help you choose what works best for your organization.
The top 20 developer productivity metrics
Metric | Description | Implementation | Trade-offs |
---|---|---|---|
Deployment Frequency | How often code is deployed to production. Indicates team’s ability to deliver value to customers quickly. Implemented by: Google, Amazon, Netflix |
Difficulty: 2/5 Benchmarks: • Elite: Multiple deploys per day • High: Between once per day and once per week • Medium: Between once per week and once per month • Low: Less than once per month Tools: GitHub Actions, GitLab CI, Jenkins, CircleCI |
Pros: • Clear indicator of delivery speed • Easy to measure • Correlates with high-performing teams Cons: • Can be gamed by making smaller deployments • May not reflect quality or value • Different products have different optimal frequencies |
Cycle Time | Time from code commit to code running in production. Shows how quickly team can respond to business needs. Implemented by: Microsoft, Stripe |
Difficulty: 3/5 Benchmarks: • Elite: Less than one day • High: Less than one week • Medium: Between one week and one month • Low: Greater than one month Tools: Jira, Azure DevOps, GitLab |
Pros: • Clear measure of process efficiency • Identifies bottlenecks • Hard to game Cons: • Affected by factors outside team control • May encourage rushing changes • Complex changes take longer |
CFR - Change Failure Rate | Percentage of changes that result in degraded service requiring remediation. Indicates reliability of delivery process. Implemented by: Etsy, GitHub |
Difficulty: 3/5 Benchmarks: • Elite: 0-15% • High: 16-30% • Medium: 31-45% • Low: 46%+ Tools: PagerDuty, ServiceNow, Datadog |
Pros: • Direct measure of quality • Shows deployment stability • Hard to game Cons: • May discourage risk-taking • Affected by external factors • Definition of “failure” needs care |
MTTR - Mean Time to Recovery | How long it takes to restore service after an incident. Shows resilience and operational excellence. Implemented by: LinkedIn, Dropbox |
Difficulty: 4/5 Benchmarks: • Elite: Less than one hour • High: Less than one day • Medium: Less than one week • Low: More than one week Tools: PagerDuty, VictorOps, OpsGenie |
Pros: • Critical for reliability • Clear business impact • Encourages good practices Cons: • Highly variable by incident type • Can mask underlying problems • May encourage quick fixes |
Code Review Time | Time taken to complete code reviews. Shows collaboration efficiency and potential bottlenecks. Implemented by: Google, Facebook |
Difficulty: 2/5 Benchmarks: • Target: Less than 24 hours • Warning: More than 48 hours Tools: GitHub, GitLab, Gerrit |
Pros: • Easy to measure • Clear bottleneck indicator • Impacts developer satisfaction Cons: • Faster isn’t always better • Complex changes need more time • May discourage thorough reviews |
Pull Request Size | Number of changed lines per pull request. Indicates complexity and reviewability of changes. Implemented by: Microsoft, Meta |
Difficulty: 1/5 Benchmarks: • Ideal: Less than 200 lines • Warning: More than 1000 lines Tools: GitHub, GitLab, Bitbucket |
Pros: • Easy to measure • Correlates with review quality • Encourages incremental development Cons: • Some changes require more code • May encourage artificial splitting • Not all lines equally complex |
Build Time | Time taken for automated builds to complete. Impacts developer flow and productivity. Implemented by: Twitter, Uber |
Difficulty: 2/5 Benchmarks: • Target: Under 10 minutes • Warning: Over 30 minutes Tools: Jenkins, CircleCI, Travis CI |
Pros: • Directly impacts developer experience • Easy to measure • Clear ROI for improvements Cons: • Varies by project size/type • Improvements can be expensive • May conflict with test coverage |
Test Coverage | Percentage of code covered by automated tests. Shows confidence in change safety. Implemented by: Google, Amazon |
Difficulty: 2/5 Benchmarks: • High: 80%+ coverage • Medium: 60-80% coverage • Minimum: 40-60% coverage Tools: Jest, JaCoCo, Istanbul |
Pros: • Objective measure of testing • Easy to track • Clear targets possible Cons: • Coverage doesn’t equal quality • Can encourage poor testing • Different code needs different coverage |
MTTR - Mean Time to Resolution | Time from bug report to fix deployment. Shows quality response capability. Implemented by: Mozilla, Adobe |
Difficulty: 3/5 Benchmarks: • Critical: less than 24 hours • High: less than 1 week • Medium: less than 2 weeks • Low: less than 1 month Tools: Jira, Bugzilla, Linear |
Pros: • Customer-centric metric • Easy to understand • Clear business impact Cons: • Severity affects resolution time • Can encourage quick fixes • Dependent on report quality |
Sprint Velocity | Amount of work completed per sprint/time period. Helps with planning and tracking. Implemented by: Spotify, Atlassian |
Difficulty: 3/5 Benchmarks: • Highly team dependent • Focus on stability and trends Tools: Jira, Azure DevOps, Trello |
Pros: • Useful for planning • Team-based metric • Trend analysis valuable Cons: • Easily gamed • Not comparable between teams • Story points are subjective |
Code Rework Rate | Percentage of code changes that modify recently changed code. Implemented by: Intel, IBM |
Difficulty: 4/5 Benchmarks: • Target: less than 20% • Warning: > 40% Tools: SonarQube, CodeScene |
Pros: • Highlights design issues • Shows technical debt impact • Objective measure Cons: • Some rework is normal • May discourage refactoring • Context dependent |
Time Spent in Code Review | Time developers spend reviewing others’ code. Implemented by: GitLab, GitHub |
Difficulty: 4/5 Benchmarks: • Target: 15-20% of time • Warning: less than 5% or > 30% Tools: Reviewpad, GitPrime |
Pros: • Indicates team health • Shows knowledge distribution • Quality indicator Cons: • Hard to measure accurately • Quality over quantity • Context dependent |
Production Incidents | Number and severity of production issues. Implemented by: AWS, Azure |
Difficulty: 3/5 Benchmarks: • Varies by service criticality • Industry dependent Tools: PagerDuty, Opsgenie |
Pros: • Clear business impact • Easy to understand • Shows reliability Cons: • May discourage innovation • Context dependent • Definition varies |
Code Churn | Amount of code rewritten shortly after being written. Implemented by: Microsoft, Atlassian |
Difficulty: 4/5 Benchmarks: • Warning: > 30% churn • Investigate trends over time Tools: GitPrime, Pluralsight Flow |
Pros: • Shows process issues • Identifies unclear requirements • Objective measure Cons: • Some churn is healthy • Hard to set targets • May discourage experimentation |
Technical Debt Ratio | Estimate of maintenance burden in codebase. Implemented by: SonarSource, Square |
Difficulty: 4/5 Benchmarks: • Target: less than 5% • Warning: > 10% Tools: SonarQube, CodeClimate |
Pros: • Forward-looking metric • Helps prioritize maintenance • Quantifies gut feel Cons: • Hard to measure accurately • Subjective elements • Context dependent |
Release Frequency | How often features reach users. Implemented by: Netflix, Facebook |
Difficulty: 2/5 Benchmarks: • Varies by product type • Industry dependent Tools: LaunchDarkly, Split.io |
Pros: • Business aligned • Easy to track • Clear impact Cons: • Different from deploy frequency • Product dependent • May encourage small releases |
Customer-Reported Defects | Bugs found by customers in production. Implemented by: Adobe, Salesforce |
Difficulty: 3/5 Benchmarks: • Highly product dependent • Track trends over time Tools: Zendesk, Intercom |
Pros: • Customer perspective • Clear business impact • Hard to game Cons: • Reactive measure • Depends on user base size • Reporting inconsistent |
KLOC - Lines of Code | Amount of code written or modified. Implemented by: Used widely but carefully |
Difficulty: 1/5 Benchmarks: • Not recommended as target • Use for trends only Tools: Git, any version control |
Pros: • Easy to measure • Objective • Available everywhere Cons: • Quality not quantity matters • Easily gamed • Language dependent |
Developer Satisfaction | Team happiness and engagement measures. Implemented by: Google, Microsoft |
Difficulty: 3/5 Benchmarks: • Industry average: 7.5/10 • Warning below: 6/10 Tools: Culture Amp, OfficeVibe |
Pros: • People-focused • Leading indicator • Hard to game Cons: • Subjective • Survey fatigue • Complex factors |
Time to First Commit | How long before new developers make first change. Implemented by: Stripe, Square |
Difficulty: 2/5 Benchmarks: • Target: less than 1 week • Warning: > 2 weeks Tools: GitHub, GitLab |
Pros: • Clear onboarding metric • Easy to measure • Actionable Cons: • Quality matters more • Team dependent • Can rush people |
Implementation tips
When you start to think about your metrics be sure to consider the following:
- Start small and add metrics gradually - Starting with too many metrics creates overhead and resistance. Allow teams to adjust to measurement before adding more. Each new metric should solve a specific problem or answer a specific question.
- Be transparent about what you’re measuring and why - Share the reasoning behind each metric. Explain how metrics will be used and be clear about what isn’t being measured and why. Address privacy concerns upfront to build trust.
- Use metrics to identify areas for improvement, not to punish - Focus discussions on system and process improvements. Celebrate positive trends and improvements. When metrics decline, treat it as a learning opportunity.
- Review and adjust metrics quarterly - Assess if metrics are driving desired behaviors and remove metrics that aren’t providing value and adjust targets based on your learnings. Get regular feedback from teams on metric usefulness.
- Share data with teams regularly - Make metrics visible and accessible. Provide context and trends, not just raw numbers. Enable teams to access their own metrics and create forums for discussing metric trends.
Common anti-patterns
- Using metrics for individual performance reviews - This creates incentives to game metrics and can damage trust in your measurement program. Also, ignoring the team nature of software development and can lead to an unhealthy engineering culture.
- Setting arbitrary targets without context - Different teams have different constraints. Targets should be based on historical trends and consider team maturity and context. Allow teams to set their own improvement goals.
- Comparing teams with different contexts - Teams work on different types of products with varying technical constraints. Team maturity levels differ and business contexts vary. Instead, compare each team against its own history.
- Ignoring team feedback about metrics - Teams often identify gaming opportunities first and know their constraints best. They can identify when metrics drive wrong behaviors. Their buy-in is crucial for success, making regular feedback sessions essential.
- Adding too many metrics too quickly - Creates overhead in collection and analysis and makes it hard to identify what drives improvement. Overwhelms teams with data, dilutes focus from most important metrics, and can lead to metric fatigue.
Start small. Be patient. Focus on improvement.
Remember that no single metric tells the whole story. You must build complementary metrics to balance one another. Start with metrics aligned to your goals and evolve your measurements based on feedback. Focus on using metrics to drive improvements rather than performance management. Remember that successful measurement programs focus on improvement rather than judgment. And most importantly, be patient. Implementing effective metrics takes time and requires building trust with your team.
Good luck!