Skip to main content
Autter analytics helps you understand how pull requests move through review. Use trends to find bottlenecks and evaluate process changes.
Placeholder showing Autter analytics for merged pull requests, review time, changed lines, and active reviewers

​
Metrics highlighted by Autter

The public analytics product includes metrics such as:
  • pull requests merged per engineer
  • median review time
  • lines modified per pull request
  • active reviewers
  • cycle time from first commit to merge
  • review workload and response time
  • team and repository trends over time
Available views and exports can vary by plan. See current pricing.

​
Questions analytics can answer

  • Is median review time improving?
  • Where does cycle time accumulate?
  • Are pull requests growing larger?
  • Is review work distributed unevenly?
  • Did a new review rule reduce recurring issues?
  • Which repositories need a different rollout or rule set?

​
Use metrics responsibly

Review metrics describe a system, not a person’s value. Interpret them with repository complexity, incident work, mentoring, and team responsibilities. Good uses include:
  • finding long waits between review stages
  • comparing a repository before and after a workflow change
  • spotting an overloaded reviewer group
  • tracking whether smaller pull requests merge faster
  • identifying recurring issue categories that need a rule or engineering investment
Avoid ranking engineers from one metric. A low pull request count can reflect complex work, review load, or responsibilities outside code delivery.

​
Add AI authorship analytics

The Autter CLI can contribute explicit AI authorship and coding agent context in connected mode. This lets teams evaluate AI usage without inferring authorship from code style. See Connect to the Autter platform and Data and privacy.