Explore Pull Request Approval Rate using your GitHub data
Pull Request Approval Rate in GitHub
Pull Request Approval Rate measures the percentage of pull requests that get approved versus those that are rejected or abandoned, providing crucial insights into your development team’s code quality standards and review processes. For GitHub users, this metric is particularly valuable because GitHub captures rich contextual data around every pull request—reviewer feedback, approval timestamps, change complexity, author experience levels, and team dynamics. This comprehensive dataset enables you to identify patterns like whether certain types of changes consistently face approval challenges, which reviewers tend to be more stringent, or if approval rates vary by team, repository, or time period.
Analyzing Pull Request Approval Rate manually through spreadsheets becomes overwhelming quickly due to the sheer volume of variables to consider—you’d need to cross-reference pull request data with reviewer histories, file change patterns, and team structures while avoiding formula errors that could skew results. GitHub’s built-in analytics provide only surface-level approval statistics without the ability to segment by meaningful dimensions like code complexity, reviewer expertise, or historical trends. You can’t easily answer critical follow-up questions like “why is pull request approval rate low for our mobile team?” or explore edge cases around specific repositories or time periods.
Count transforms your GitHub pull request data into actionable insights, automatically calculating approval rates across multiple dimensions while enabling deep-dive analysis to improve pull request approval rates systematically.
Questions You Can Answer
What’s my overall pull request approval rate in GitHub?
This foundational question reveals your team’s baseline code quality and review effectiveness, helping you understand how often proposed changes meet your standards.
Why is pull request approval rate low for my main branch compared to feature branches?
Analyzing approval rates by branch type uncovers whether stricter review standards for critical branches are causing bottlenecks or if there are systematic quality issues.
How to improve pull request approval rate for pull requests with more than 10 file changes?
This identifies whether large pull requests correlate with lower approval rates, suggesting the need to encourage smaller, more focused changes that are easier to review thoroughly.
Which repositories have the lowest pull request approval rates and what’s the average review time for rejected PRs?
Cross-referencing approval rates with review duration by repository helps pinpoint problematic codebases and whether rushed reviews contribute to rejections.
How does pull request approval rate vary by author experience level and reviewer assignment patterns?
This sophisticated analysis reveals whether junior developers need additional mentoring or if certain reviewer combinations consistently produce better outcomes.
What’s the correlation between pull request size, number of reviewers assigned, and approval rate across different teams?
This advanced question uncovers optimal review processes by examining how team dynamics and PR characteristics influence approval success rates.
How Count Analyses Pull Request Approval Rate
Count’s AI agent goes far beyond simple metrics calculation to deliver deep, customized analysis of your Pull Request Approval Rate. Instead of rigid templates, Count writes bespoke SQL queries tailored to your specific GitHub setup — whether you want to analyze approval rates by repository, team member, or code complexity.
When investigating why your pull request approval rate is low, Count runs hundreds of queries in seconds to uncover hidden patterns. It might segment your GitHub data by file types changed, pull request size, reviewer workload, and time of submission simultaneously, revealing that large frontend PRs submitted on Fridays have significantly lower approval rates than smaller backend changes.
Count automatically handles messy GitHub data — inconsistent labeling, incomplete review histories, or missing timestamps — cleaning these issues as it analyzes. Every transformation is transparent, so you can verify how Count calculated approval rates across different time periods or team structures.
The analysis becomes presentation-ready instantly. Count might discover that how to improve pull request approval rate involves optimizing PR size (showing 87% approval for <200 line changes vs. 34% for >500 lines) and reviewer assignment patterns. Your team can collaboratively explore these insights, asking follow-up questions like “Which reviewers have the highest approval correlation with successful deployments?”
Count also connects your GitHub data with other sources — JIRA tickets, deployment logs, or customer feedback — providing comprehensive context around approval patterns and their business impact.