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Use Code Review analytics to answer three questions: Are reviews moving? Is the feedback useful? Where is code risk accumulating? Start with a trend, then open the underlying pull requests before drawing a conclusion.

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Review flow

The Overview tab tracks:
  • Total and open pull requests
  • Time to first review and time to merge
  • Merge rate and pull-request velocity
  • Active reviewers
  • Review comments and average comments per pull request
  • Pull-request size and backlog by author
Use these metrics to find a bottleneck: a growing backlog, slow first response, or a review policy producing too much back-and-forth.
Code Review Overview with sample pull-request flow and response-time metrics

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Code quality

The Code Quality tab brings together:
  • Code churn over time and churn-impact summaries
  • Churn hotspots and the most-churned files
  • Reverted pull requests
  • Three-month code turnover
  • Bug reports by repository
  • Pull requests with quality score, status, date, and ticket access
High churn is a prompt to investigate, not a verdict. It can reflect healthy iteration, a migration, generated changes, unclear ownership, or repeated rework.
Code Quality dashboard with sample weekly code-churn data

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Review effectiveness

The Review Analytics tab shows:
  • Average review cycle time
  • Comments addressed and total comments
  • Comment acceptance
  • Average comments per merged pull request
  • Merged pull requests by team
  • Entelligence and human comments addressed
  • Usage by user or team
Review Analytics with sample cycle-time, comment-acceptance, and merged-PR metrics
Falling acceptance can point to noisy guidance, stale Learnings, or the wrong priority threshold. Rising cycle time can point to review volume, unclear ownership, or changes that are too large.

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Repository operations

Add or search repositories, open Show details, inspect enabled review behavior, and jump to repository pull requests.

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Ask better questions of the data

Some analytics expose Ask Ellie. Useful prompts include:
  • Which repositories drove the increase in time to first review?
  • Show the pull requests behind this churn hotspot.
  • Which guidelines correlate with frequently dismissed findings?
  • Where did automated reviews reduce human back-and-forth?
Validate the answer against the selected date range and source pull requests.

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Use metrics responsibly

  • Improve review policy, repository ownership, and tooling.
  • Compare trends across similar time windows.
  • Pair counts with pull-request context.
  • Do not turn one activity metric into an individual performance score.

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Next step

Review the metric glossary or tune Code Review settings.