AI 中文总结
研究针对开发人员对代码审查评论理解不同的问题，探索个性化代码审查解释潜力。通过混合方法用户研究发现解释风格偏好因多种因素而异，据此勾勒出以人为本、适应开发者偏好的人工智能辅助代码审查系统愿景。
AI 中文摘要
代码审查对于确保软件质量和支持协作至关重要，但先前工作表明开发人员对代码审查评论的理解可能不同，这会阻碍有效沟通。为应对这一挑战，我们探索了个性化代码审查解释的潜力。我们报告了一项正在进行的混合方法用户研究的初步结果，其中开发人员评估了跨多个代码片段的符合人物角色的审查评论。结果表明，对解释风格的偏好因解决问题的风格、经验水平和角色而异。基于这些发现，我们概述了一个包容性的、以人为本的人工智能辅助代码审查系统的愿景，该系统可根据开发人员的问题解决偏好调整反馈。
英文摘要
Code review is essential for ensuring software quality and supporting collaboration, yet prior work shows that developers can interpret code review comments differently. These differences can hinder effective communication, particularly in collaborative settings. To address this challenge, we explore the potential of personified code review explanations. We report initial findings from an ongoing mixed-methods user study in which developers evaluated persona-aligned review comments across multiple code snippets. Our results suggest that preferences for explanation styles vary across problem-solving styles, experience levels, and roles. Across problem-solving style profiles, developers valued explanatory depth, learning support, practical suggestions, and risk awareness over conciseness, highlighting the need to balance personalization with clarity and trust. Based on these findings, we outline a vision for inclusive, human-centered AI-assisted code review systems that adapt feedback to developers' problem-solving preferences.
CommentsPresented at Journal Ahead Workshop (JAWs) 2026
