Every developer has an opinion about pull request size. Some say keep them tiny. Others argue context matters more than line count. Teams debate whether 200 lines or 400 lines is the cutoff.

But opinions don't matter. Data does.

Research across millions of pull requests shows that PR size directly affects review quality, bug rates, and merge time. Larger pull requests don't just take longer to review. They get worse reviews, introduce more bugs, and slow down entire teams.

The data is clear. Small pull requests work better. Here's what the research actually shows.

TLDR

• Research analyzing millions of pull requests reveals that PR size significantly impacts code quality and development speed.

• Pull requests under 200 lines get merged faster and reviewed more thoroughly. PRs over 400 lines catch fewer bugs during review.

• Small pull requests (50 lines) are 15% less likely to be reverted than larger ones (250 lines).

• Reviewer focus drops after 60 minutes, causing defects to slip through on large PRs. 

• While breaking work into smaller PRs requires more planning, automated PR review tools handle consistent checks across all PR sizes, catching issues that reviewers miss when overwhelmed by large diffs.

What the research tells us

Multiple studies have analyzed PR size and its impact on development workflows. The findings are consistent.

1. PRs under 200 lines get reviewed faster

Research shows that smaller pull requests are reviewed faster and with fewer delays, while larger PRs often slow down review processes and introduce more complexity.

Pull requests that change fewer files also merge faster. Even a small increase in the number of files changed can take more than double the time to merge.

Lines of code and files changed show how big the change is. Bigger changes are harder to review. They often involve more dependencies and more coordination. As the size grows, review time increases, CI failures become more common, and the risk of bugs goes up.

2. PRs over 400 lines catch fewer bugs

Pull requests over 400 lines of code catch fewer bugs during review. After 60 minutes of review time, "focus fatigue" kicks in. Defects slip through.

Large PRs often get zero review comments. Not because they're perfect, but because reviewers lose focus when faced with too much code.

3. Small PRs are less likely to be reverted

Data shows that 50-line changes are 15% less likely to be reverted than 250-line changes. Smaller pull requests mean fewer bugs make it to production.

This tracks with defect rate research. If code has an average defect rate of 50 defects per 1,000 lines (common for pre-review code), and reviews catch 75% of defects, you find one defect per 27 lines reviewed. Larger PRs with more lines contain more defects that reviewers miss.

4. Review quality drops as PR size increases

Data on time spent per file shows a counterintuitive pattern. As PRs grow beyond moderate complexity, reviewers spend less time per file, not more.

Why? Reviewers shift from careful line-by-line analysis to broader risk assessment. They lose focus across many files. Review becomes superficial. The old joke holds: "Ask a programmer to review 10 lines, and they'll find 10 issues. Ask them to review 500 lines, and they'll say it looks good."

Why large PRs create problems

The research reveals specific mechanisms behind why large pull requests harm code quality.

1. Context switching overhead

Large PRs require a significant time commitment from reviewers. They can't review a 1,000-line PR between meetings. They need dedicated blocks of focus time.

This creates delays. Pull requests sit waiting for reviewers to find multi-hour slots. Meanwhile, the author might have moved on to other work, creating more context switching when feedback arrives days later.

2. Cognitive overload

Human attention spans have limits. After reviewing hundreds of lines of code, focus deteriorates. Reviewers start skimming. They miss subtle bugs. Critical issues get overlooked simply because there's too much to process.

Research shows review effectiveness drops sharply after 60 minutes. Large PRs that take 2-3 hours to review get progressively worse feedback as the reviewer fatigues.

3. Rubber stamp approvals

When PRs are too large, reviewers face a difficult choice. Spend hours doing a thorough review, or approve it quickly to unblock the author.

Data shows that very large PRs often get approved with minimal comments. This is because reviewers don't have the time or energy to properly review them.

4. Increased bug introduction

The data shows a clear correlation between PR size and bug rates. Larger PRs introduce more bugs that survive review and make it to production.

This happens because both the author and reviewers lose track of how changes interact. In a 50-line PR, it's easy to see how everything fits together. In a 500-line PR spanning 20 files, interactions become impossible to track mentally.

The cost of large pull requests

Beyond immediate code quality issues, large PRs create organizational costs.

1. Slowed velocity

Research analyzing 1.5 million pull requests shows that PRs changing many files take much longer to merge. As the number of files increases, merge time rises sharply. This creates bottlenecks, with work piling up behind large pull requests waiting for review.

High-velocity engineering teams design their systems so each PR touches as few files as possible.

Microservices architecture helps here by allowing smaller, more focused changes.

2. Increased technical debt

When large PRs get superficial reviews, technical debt accumulates. Code that should be refactored gets merged. Patterns that violate standards slip through. Architectural decisions go unquestioned.

Technical debt compounds over time. Each large PR that gets rubber-stamped adds to the debt burden.

3. Developer frustration

Engineers postpone reviewing large PRs. The prospect of spending hours on review when you're in the middle of your own work creates friction.

Authors of large PRs wait days for feedback. When feedback finally arrives, they've moved to other work and need time to reload context. The cycle repeats.

How to write smaller pull requests

The data shows that small PRs work better. But splitting work into small PRs requires discipline and planning.

1. Plan before coding

Small PRs start with planning. Before opening your editor, think about how to break the work into logical units.

Building user authentication? One PR for backend API, another for UI components, a third for tests. Each piece works independently and can be reviewed on its own.

2. Use feature flags

Feature flags let you merge incomplete features. The code is in production but disabled until ready. This enables smaller PRs without shipping half-built features to users.

3. Stack pull requests

Some changes naturally depend on others. Stack them. The second PR builds on the first. Reviewers can see each change in isolation, even though they're related.

4. Separate refactoring from features

Don't mix refactoring with feature work in the same PR. Refactor first, then add features. Or add features, then refactor. Mixing them creates large, hard-to-review PRs.

How automated code review helps with PR size

Automated code review tools handle certain checks consistently regardless of PR size. This helps maintain quality even when PRs are larger than ideal.

1. Consistent coverage

Human reviewers suffer from focus fatigue on large PRs. Automated tools don't. They apply the same standards to line 1 and line 500.

AI code reviews provide consistent feedback regardless of PR size. Security scans, style checks, and common bug patterns get caught automatically. This frees human reviewers to focus on logic and architecture rather than mechanical issues.

2. Faster feedback loops

Automated review provides instant feedback. Open a PR and get security scan results, style violations, and common issues flagged within seconds.

This matters more for large PRs where human review takes hours or days. Automated feedback lets authors fix obvious issues before human reviewers even start.

3. Cross-file analysis

Large PRs often introduce issues that span multiple files. A change in one file breaks assumptions in another. Human reviewers struggle to track these relationships across 500+ lines.

An AI code review stack with a six-layer quality strategy analyzes entire repositories, not just PR diffs.

4. Supporting tools

Codacy alternatives and similar platforms provide automated checks that complement human review. They help maintain baseline quality on PRs of any size.

When large PRs make sense

Some situations genuinely require large PRs.

• Dependency updates that affect many files can't be split meaningfully. A language version upgrade touching 100 files needs to happen atomically.

• Database migrations with extensive test data updates create large diffs but follow mechanical patterns that can be reviewed quickly.

• Auto-generated code from tools or frameworks creates large PRs that need minimal review beyond verifying the generator ran correctly.

Even in these cases, research suggests strategies to reduce review burden. Generate the changes in one PR. Write the logic that uses those changes in separate PRs. This keeps each review focused, even when total changes are large.

What the data means for your team

Research across millions of pull requests reaches the same conclusion. Small PRs result in better code quality, faster reviews, and fewer production bugs.

The ideal PR size is under 200 lines, reviewable in under 60 minutes. Larger PRs see significant drops in review quality and increased bug introduction.

Breaking work into small PRs requires planning. You need to think through how to split features into reviewable chunks. You need feature flags to merge incomplete work. You need stack PRs for dependent changes.

The effort pays off. Speed vs quality isn't a tradeoff when you keep PRs small. Small PRs move faster and maintain higher quality.

For teams struggling with large PRs, automated code review helps. Automated code review tools catch mechanical issues consistently, letting human reviewers focus on the parts that need human judgment.

Ready to maintain quality across PRs of any size? Try cubic and see how automated review catches issues that slip through on large pull requests.