The 20 most popular developer productivity metrics: a practical reference for leaders

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Lou Bichard / Product Manager at Gitpod / Jan 27, 2025

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!

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