Understanding what each metric measures and why it matters for repository health
RepoPulse analyzes GitHub repositories using data from the GitHub API and commit history. Each metric provides insight into different aspects of project health, activity, and community engagement.
Key Principle: No single metric tells the full story. RepoPulse combines multiple metrics with intelligent analysis to provide comprehensive health assessments.
Number of GitHub stars (bookmarks) the repository has received
Stars indicate community interest and popularity. High star counts suggest the project solves a common problem or has strong marketing.
Trust for established projects. Less reliable for new repositories.
Direct count from GitHub API
Next.js has 120K+ stars, indicating massive community adoption
Number of repository forks (copies) created by other users
Forks show active development interest. Many forks suggest the project is a foundation for other work.
Trust for libraries and frameworks. Less meaningful for end-user applications.
Direct count from GitHub API
React has 45K+ forks, showing it's widely extended and modified
Total number of issues (bugs, features, questions) reported
High issue counts can indicate active usage but also maintenance burden.
Combine with resolution rate. Raw count alone is misleading.
Direct count from GitHub API
Popular projects like VS Code have thousands of issues, showing active community engagement
Number of unique contributors who have committed code
Diverse contributor base indicates project health and sustainability.
Trust for projects with multiple contributors. Single-contributor projects carry higher risk.
Count of unique commit authors
Linux kernel has thousands of contributors, showing massive collaborative development
Total number of commits in the repository history
Commit count shows development activity over time.
More useful when analyzed by time periods. Raw total can be inflated by imports.
Count of all commits in repository
Large projects may have 10K+ commits representing years of development
Average number of commits per day/week/month
Shows current development velocity and maintenance activity.
Trust for recent periods (30-90 days). Historical data may not reflect current status.
Commits in time window ÷ days in window
Active projects average 5-20 commits per week
Days since the most recent commit
Recent commits indicate active maintenance and development.
Trust for periods under 90 days. Older projects may have different rhythms.
Current date - last commit date
Projects with commits in the last 7 days are actively maintained
Average time between issue creation and first maintainer response
Fast responses show community commitment and support quality.
Trust for recent issues. Historical data may not reflect current maintainer capacity.
Average (first response time - issue creation time) for recent issues
Top projects respond to issues within hours, not days
Percentage of issues closed vs opened in a time period
Shows how effectively the project manages its issue backlog.
Trust for periods with sufficient issue volume. Low-volume periods may be misleading.
(Issues closed ÷ Issues opened) × 100 in time window
Healthy projects maintain 80-95% resolution rates
Average time between PR creation and merge
Indicates how quickly contributions are integrated and reviewed.
Trust for active projects. Slow times may indicate maintainer bandwidth issues.
Average (merge time - PR creation time) for merged PRs
Collaborative projects merge PRs within 1-7 days
Total size of repository in bytes
Large sizes may indicate comprehensive projects or accumulated assets.
Trust for code repositories. Less relevant for documentation or data projects.
Sum of all file sizes from GitHub API
Large projects like Chromium exceed 10GB of code and assets
Percentage breakdown of programming languages used
Shows project scope and technology choices.
Trust for code analysis. May not reflect actual usage in polyglot projects.
Language statistics from GitHub Linguist analysis
Full-stack projects show multiple languages (JS, Python, SQL, etc.)
High star counts indicate popularity, not necessarily code quality or maintenance standards. Some projects gain stars through marketing rather than merit.
Many forks are created for personal use or experimentation and don't represent active contribution back to the original project.
Issue counts include feature requests, questions, and discussions alongside bugs. High issue counts can indicate active community engagement.
Commit frequency can be inflated by automated tools, refactoring, or import operations. Quality of commits matters more than quantity.
Learn more about how metrics combine into health scores