发表机构
University of Saskatchewan（萨斯喀彻温大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过对182个开源项目1355名开发者的调查发现，代码评审工具默认的字母排序仅获10.2%评审者认可，会增加认知负荷，多数人期望采用依赖关系分组等自定义排序，凸显需设计契合人类认知的评审界面。
AI 中文摘要
有效的代码评审是维护软件质量的核心，但关于拉取请求（PR）中变更文件的排序方式如何影响评审有效性的研究有限。大多数流行的代码评审工具默认采用字母排序，优先考虑可预测性而非上下文相关性。尽管先前研究已考察文件位置如何影响评审者的注意力，但这种排序方式对认知负荷和感知评审彻底性的影响仍不明确。本研究对来自182个广泛使用的开源项目的1355名专业开发者展开了首次大规模调查，以探究文件排序如何影响评审行为、理解能力和感知有效性。我们的混合方法分析显示，仅10.2%的评审者认为字母排序是最优的，这凸显了他们对代码变更的认知解读存在错位。尽管部分开发者认可其可预测性，但超过半数（57.6%）的人表示该排序会增加上下文切换、破坏逻辑推理并加剧评审疲劳，63.9%的人担心默认排序可能导致他们遗漏漏洞。我们进一步明确了多文件评审中的关键挑战，并梳理了开发者对改进工具的期望，包括依赖关系感知分组和可自定义的文件排序（66%的评审者提出该需求）。这些发现凸显了以评审者为中心的界面设计的必要性，该设计需更好地使工具行为与人类认知相契合。
英文摘要
Effective code review is central to maintaining software quality, yet there is limited research about how the ordering of changed files in Pull Requests (PRs) influences review effectiveness. Most popular code review tools default to alphabetical ordering, favoring predictability over contextual relevance. While prior studies examined how file position shapes reviewer attention, it remains unclear how such ordering influences cognitive load and perceived review thoroughness. This study presents the first large-scale survey of 1,355 professional developers across 182 widely used open-source projects to investigate how file ordering impacts review behavior, comprehension, and perceived effectiveness. Our mixed-methods analysis reveals that only 10.2% of reviewers consider alphabetical ordering optimal, underscoring a cognitive misalignment in their interpretation of code changes. Although some developers appreciate its predictability, more than half (57.6%) report that it increases context switching, disrupts logical reasoning, and contributes to review fatigue, and 63.9% expressed concern that the default ordering may cause them to miss bugs. We further identify key challenges in multi-file reviews and elicit developers' expectations for improved tooling, including dependency-aware grouping and customizable file ordering (requested by 66% of reviewers). These findings highlight the need for reviewer-centric interface designs that better align tool behavior with human cognition.
Comments13 pages
Journal ref2026 IEEE/ACM 48th International Conference on Software Engineering
