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Managing Large Pull Requests

Learn why large pull requests slow teams down, increase risk, and how LinearB helps you detect, manage, and improve them using AI insights, team workflows, and automation.

Steven Silverstone
Updated by Steven Silverstone

Large pull requests (PRs) are one of the most consistent predictors of delays, review friction, and production risk.

Studies show that once a PR exceeds 400–800 lines of code (LOC), the odds of introducing a bug or stalling a review increase sharply. Small, focused PRs (under 100 LOC or five files) are easier to review, test, and merge safely. They're also better for onboarding, iteration speed, and team alignment.

LinearB gives you the tools to measure, monitor, and manage PR size across your team.

What Qualifies as a “Large” PR?

Use these benchmarks to understand and set your own team’s thresholds:

Signal

Why it matters

"Small"

"Large"

PR Size (net lines changed)

Leading indicator for review time and change-failure rate

< 100 LOC — sweet spot

> 400 LOC slows reviews; > 800 LOC = very high risk

Code Changes (cumulative diff)

Captures the work done, not just the final state

< 150 LOC

> 500 LOC

Files Touched

Cognitive load rises with every additional file

≤ 5 files

> 10 files — triggers extra reviewer (see below)

Community Benchmark

6M PR study: elite teams average < 105 code changes per PR

< 105

Anything higher is above average

Track PR Size and Code Changes side-by-side in your LinearB Team Goals and Developer Metrics dashboard to measure progress.
You can set your own PR Size threshold under Team Goals > Pull Request Size and let WorkerB handle enforcement via automatic alerts.

What Happens When Large PRs Hit AI Code Review?

AI Code Review doesn’t block or reject PRs, instead it highlights risk. It never prevents merges, it simply flags potential issues so teams can stay in control while maintaining velocity.

When a large PR is opened, you may notice the following:

  • Estimated Review Time (ETR) labels — e.g., “20 min+” in red
  • Increased findings — the AI flags more bugs, security issues, and performance risks
  • Extra reviewers assigned automatically via gitStream when size + ETR thresholds are crossed
Large PR? Expect a longer ETR label, auto-assignment of additional reviewers, and more AI findings — use them as a signal to break up the work before requesting final review.

Strategies for Managing Large PRs

These aren’t just best practices, they’re team protectors:

  • Slice work early: Start small: aim for < 100 LOC and < 5 files. Use Git branches and feature slices instead of batching big changes.
  • Use Feature Flags: Wrap big features in flags so you can merge incremental updates safely.
  • Pre-Review with Teammates: Use Slack channels (e.g., #pr-drafts) or pair programming to validate structure and logic before polishing.
  • Assign Multiple Reviewers: Large or sensitive PRs may need more eyes. Use additional_review_for_large_pr in gitStream to require this automatically (only ~15 lines of YAML).
  • Let WorkerB Do the Policing: Enable Pull Request Size alerts so authors get a nudge as soon as they push an oversized diff — no need to wait for human intervention.

How LinearB Helps

Problem

LinearB Feature

Oversized PRs hard to catch

Team Goals → PR Size threshold + WorkerB alerts

Long review times for large PRs

ETR labels + gitStream auto-reviewer rules

Hidden risks in big diffs

AI Code Review surfaces bugs, perf, security issues

Hard to measure improvement

Developer Metrics → PR Size vs. Review Time trends

Elite teams average < 105 code changes per PR. Once you cross 400–800 LOC, the risks multiply.

Copy/Paste Checklist for Team Repos

  • Target PR size: < 100 net LOC or ≤ 5 files
  • Use feature flags for large features
  • Pre-review in Slack or pair
  • gitStream rules enforce reviewer coverage
  • WorkerB alerts for PRs above team thresholds

Goal Settings in LinearB

Managing WorkerB Team Alerts for Slack

How did we do?

How to Address Pull Request Merged w/o Review or with Basic Review

Strategies to Identify and Reduce High Pickup Time

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