File order affects code review attention and bug likelihood
Does Order Matter? An Empirical Investigation into the Impact of File Ordering on Code Review Effectiveness
Software Engineering
Summary
Code review helps find bugs before software is released, but how files are shown to reviewers might change how well they catch problems. The authors studied many code reviews from GitHub and found that files shown earlier get more attention and are less likely to have hidden bugs. Larger code changes get less detailed review per file, and alphabetical ordering may not be the best way to show files. The study suggests smarter ways to order and highlight files to improve review quality.
What this means in practice
- •For software development teams: Design code review tools that order files by relevance or risk instead of alphabetically to improve reviewer focus and defect detection.
- •For software engineering tool vendors: Create interfaces that highlight review coverage per file and prioritize file ordering dynamically to support reviewers in managing large code changes.$Commercial implications: This enables new code review features that improve user attention management and defect detection, creating differentiated tools for teams.
Authors
Md Shamimur Rahman, Zadia Codabux, Chanchal K. Roy
Abstract
Modern code review is central to software quality, but its effectiveness depends on reviewer expertise, change characteristics, and how tools present changes. Most platforms display modified files alphabetically by default, although our prior work shows that developers find this ordering cognitively misaligned with how they understand multi-file pull requests. Whether these ordering-related attention patterns affect outcomes at scale remains unclear. We present a large-scale study of file ordering and review effectiveness, mining 330,343 multi-file pull requests and 756,814 file instances from 182 GitHub projects in five programming languages. We examine whether file position is associated with later bug-fixing changes, whether pull request size moderates this relationship, and whether reviewer attention, proxied by comments, aligns with latent bug outcomes. Results show statistically significant but modest associations among file position, pull request size, review activity, and latent bug likelihood. Latent bug rates rise from 56.7% at position 1 to 61.5% at position 30. Pull request size has a non-linear relationship with latent bug likelihood: pull requests of about ten files have the lowest risk, while very small and very large ones have elevated rates. Hurdle models show that attention is diluted as pull request size grows: each additional modified file reduces the odds of receiving any review comment by about 8.7%. These findings reveal an attention-effectiveness gap: visible review activity does not necessarily prevent defects. Alphabetical ordering is therefore not a neutral interface default, but a structural feature shaping attention allocation, review coverage, and confidence in review outcomes. We propose context-aware file ordering, dependency-aware grouping, risk-aware prioritization, and per-file coverage indicators to make review attention more visible and actionable.