Optimizing Pull Request Reviews: Balancing Code Volume and Efficiency in AI-Assisted Development

AI-assisted development has led to an explosion in the size of pull requests (PRs), with many now exceeding 3,000 lines of code (LOC). This trend is driven by AI tools enabling rapid code generation, often resulting in PRs that are both voluminous and complex. Traditional review processes struggle with this new reality, as researchers have identified cognitive limits to working memory.

After reviewing around 200-500 LOC, reviewers often experience fatigue, which can lead to missed bugs, design flaws, and inefficiencies in the code. The issue is compounded by the tendency for developers to merge multiple features or fixes into a single PR due to time constraints, further increasing the cognitive load on reviewers.

While AI-generated code is generally functional, it can sometimes introduce subtle issues that are hard to spot in large PRs. Large PRs also contribute to higher technical debt, as they make it harder to isolate and resolve issues after merges. This can result in increased technical debt and the risk of software failures, with mistakes potentially going unnoticed until significant problems arise in production.

Addressing this challenge requires a combination of larger PR size limits and better tools for splitting large changes into manageable chunks. Research indicates that cognitive fatigue typically sets in after evaluating around 200-500 lines of code, after which review efficiency declines. Implementing stricter PR size limits could help mitigate these issues by ensuring that each PR is manageable for reviewers.

Additionally, tools that automatically suggest splitting large PRs into smaller, focused changes can improve review efficiency and code quality.