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How it works

The review efficiency index is a composite metric that combines three code review metrics into a single 0-100 score. It helps you understand how effective your team’s code review process is by measuring resolution rates, review quality, and turnaround time. A higher score indicates a more effective review process - comments are being resolved, reviews are high quality, and turnaround is fast.

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Calculation

The review efficiency index uses percentile-based normalization to combine metrics with different scales. Each component is converted to a percentile rank (0-100) based on global benchmarks, then combined with equal weighting by default. Admins can change the weights for the organization or a team under Settings → Weights → Metrics. Formula:

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Component metrics

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Percentile normalization

Since these metrics have different scales (percentages, scores, time), we convert each to a percentile rank before combining them:
  1. Your value is compared against global benchmark data from all organizations
  2. A percentile rank (0-100) indicates where you fall in that distribution
  3. For “lower is better” metrics (turnaround time), the percentile is inverted so that higher always means better
Example: If your review turnaround is in the 10th percentile (very fast = good), it becomes the 90th percentile rank in the efficiency calculation.

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Technical details

Comment resolution rate
  • Measures the percentage of suggestion comments that the PR author addressed with a later commit or a clear reply
  • Formula: Resolved Comments / Total Suggestion Comments
Code review quality
  • Uses a sigmoid transformation to scale raw review ratings to 0-100
  • Formula: 100 / (1 + e^(-(rating - 800) / 250))
  • A rating of 800 maps to approximately 50
  • See review quality for more details
Code review turnaround
  • Measures the median time from a review request to the review, or from the latest code update if your organization tracks turnaround from code updates
  • See review turnaround for more details
All three component metrics must be present to calculate the efficiency index. If any component is missing for a given time period, that data point is skipped.This ensures the score is always based on complete data rather than partial calculations.
The review efficiency index pulls data from:
  • GitHub
  • GitLab
  • Bitbucket
Last modified on October 17, 2026