50 shades of PR
summary
Data from LinearB shows most code stalls in the pull request stage, so the concrete fix is adding automation in the middle of the pipeline: continuous merge. Examples: rules that auto-approve docs when a maintainer edits only .md or images, rules that mark safe changes (formatting, tests) to skip human review, CI bots that run auto-reviews to catch syntax or wrong-reference bugs, and generators that auto-write PR context or require screenshots for UI changes. A small set of repo rules quickly cuts review cycle time.
AI-written summary · about
chapters (10)
- 0:00Introduction
- 1:55Common delivery cycle
- 3:40CI/CD automation
- 5:27AI in code review
- 6:50Why pull requests get stuck and slow down
- 8:56Problematic PR causes
- 11:06Continuous merge automation
- 12:36GitStream examples
- 15:46PR automation processes
- 16:20Summary & Q&A
related talks
06m50 Haunting Shades of PR Dark Matter
29mBreaking the PR Review Bottleneck
14m50 shades of PR dark matter
26mA State of Continuous Merge: The Secret to More Productive Dev Teams
23mDrama-Free Code Review!
42m7 Metrics to Accelerate Delivery without Damaging Culture
21mContinuous Improvements of The Code Review Process
44mCracking the Code Review