📊 Metrics Prioritization Guide

1. Most Important Metrics

These metrics provide the core insights necessary for understanding engineering productivity, code quality, and delivery performance. They should be prioritized to effectively manage team output and improve software delivery.

• Impact
  Measures the cognitive load and significance of work done by contributors, helping managers understand the true value of individual and team contributions beyond raw code changes.

• Efficiency (%)
  Reflects the percentage of productive code, indicating how much code actually delivers business value, which is essential for assessing developer output quality.

• Cycle Time (sec)
  Represents the total time from the first commit to deployment, highlighting bottlenecks in the development process and delivery speed.

• Lead Time For Changes (sec)
  Measures the time it takes for committed code to reach production, directly linking engineering efforts to business outcomes.

• Deployment Frequency (per day)
  Shows how often code is deployed to production, a key indicator of a team’s agility and continuous delivery capability.

• Change Failure Rate (%)
  Indicates the percentage of deployments that fail or are cancelled, critical for assessing release stability and risk.

• Mean Time To Recovery (sec)
  Measures how quickly the team recovers from failures, emphasizing resilience and operational efficiency.

2. Relevant Metrics

These metrics provide additional context and detail to supplement core insights. They help managers fine-tune processes and understand team dynamics more deeply.

• Active Days (days)
  Tracks how many days contributors were active, useful for monitoring engagement and workload distribution.

• New Work (%)
  Indicates the share of newly added code, helping understand innovation and feature delivery vs. maintenance.

• Refactor (%)
  Measures the amount of code updated or rewritten, showing technical debt management efforts.

• Help (%)
  Reflects collaboration by showing how much code was updated by others, highlighting team knowledge sharing.

• Churn (%)
  Shows early code rewrites or deletions, helping identify unstable or experimental work.

• Knowledge Sharing Index
  Quantifies how well the team collaborates in code reviews, supporting healthy team dynamics.

• Total Pull Requests Merged Without Review
  Alerts to potential quality issues by showing PRs merged without review.

• Average time to merge from review (sec)
  Measures efficiency in PR review and merge processes.

3. Metrics to Take into Consideration

These metrics provide specialized or situational insights that can help identify specific issues or opportunities but may not be essential for all teams or scenarios.

• Low/Medium/High Risk Commits
  Assesses the risk level of commits, helping identify potentially problematic changes for deeper analysis.

• Productive Throughput (LoC)
  Tracks code volume that’s productive (not churn), useful for assessing effective output.

• Pull Request Size (LoC)
  Helps monitor PR sizes, which can affect review efficiency and code quality.

• Comments received (comments)
  Reflects engagement in PR discussions, useful for collaboration insights.

• Rubber Stamped PRs (%)
  Highlights PRs merged without meaningful review, signaling potential process risks.

• Failed PRs (PRs)
  Tracks problematic PRs to understand issues in code quality or process.

• Traceability (% and counts)
  Indicates linkage between tickets and code, supporting traceable workflows.

• Code Coverage (%)
  Shows test coverage levels, important for quality assurance but dependent on team testing practices.

• Code Bugs, Vulnerabilities, Smells
  Important for teams focused on code health and security but may require additional tools or context.