Does Order Matter? An Empirical Investigation into the Impact of File Ordering on Code Review Effectiveness

Modern code review is central to quality assurance in software development practice, yet its effectiveness depends not only on reviewer expertise and change characteristics, but also on how review tools present code changes. Most code review platforms display modified files alphabetically by default, although our prior work shows that developers often perceive this ordering as cognitively misaligned with how they understand multi-file pull requests. However, it remains unclear whether such ordering-related attention patterns are associated with measurable review outcomes at scale. This article presents a large-scale empirical study of file ordering and review effectiveness. We mine 330,343 multi-file pull requests comprising 756,814 file instances from 182 GitHub projects across five programming languages. We examine whether a file’s position within a pull request is associated with later involvement in bug-fixing changes, whether pull request size moderates this relationship, and whether reviewer attention, proxied by review comments, aligns with latent bug outcomes. Our results show statistically significant but modest, associations between file position, pull request size, review activity, and latent bug likelihood. Later-positioned files exhibit slightly higher latent bug rates, increasing from 56.7% at position 1 to 61.5% at position 30. Pull request size shows a non-linear relationship with latent bug likelihood: mid-sized pull requests around ten files have the lowest observed risk, while both very small and very large pull requests show elevated rates. Hurdle models further show that review attention is diluted as pull request size grows, with each additional modified file reducing the odds that a file receives any review comment by approximately 8.7%. Together, these findings reveal an attention-effectiveness gap: visible review activity does not necessarily translate into defect prevention. Our results suggest that alphabetical ordering should not be treated as a neutral interface default, but as a structural feature that can shape attention allocation, review coverage, and confidence in review outcomes. These findings motivate concrete design directions for review tooling, including context-aware file ordering, dependency-aware grouping, risk-aware prioritization, and per-file coverage indicators that make review attention more visible and actionable.