源代码热点：质量问题的诊断方法
Source Code Hotspots: A Diagnostic Method for Quality Issues
AI总结：
本文提出了一种诊断源代码质量问题的方法，通过分析热点模式识别出自动化工具对变更历史的干扰，并提供重构指南以改善软件质量。
摘要：
软件源代码往往包含“热点”：一小部分代码更改频率远高于项目其他部分，因此集中了维护活动。我们挖掘了91个不断发展、积极开发的GitHub仓库的完整版本历史，并识别出15种重复出现的行级热点模式，解释了这些热点为何出现。三种最普遍的模式是固定版本更新（26%），揭示了脆弱的发布实践；长行更改（17%），表明布局缺陷；以及格式来回（9%），表明缺失或不一致的样式自动化。令人惊讶的是，自动化账户生成了所有热点编辑的74%，表明机器人活动是变更历史中主导但可以避免的噪声来源。通过将每种模式映射到具体的重构指南和持续集成检查，我们的分类使从业者能够采取实际行动，减少热点并系统地提高软件质量，特别是在可配置性、稳定性和可变性方面。
英文摘要：
Software source code often harbours "hotspots": small portions of the code that change far more often than the rest of the project and thus concentrate maintenance activity. We mine the complete version histories of 91 evolving, actively developed GitHub repositories and identify 15 recurring line-level hotspot patterns that explain why these hotspots emerge. The three most prevalent patterns are Pinned Version Bump (26%), revealing brittle release practices; Long Line Change (17%), signalling deficient layout; and Formatting Ping-Pong (9%), indicating missing or inconsistent style automation. Surprisingly, automated accounts generate 74% of all hotspot edits, suggesting that bot activity is a dominant but largely avoidable source of noise in change histories. By mapping each pattern to concrete refactoring guidelines and continuous integration checks, our taxonomy equips practitioners with actionable steps to curb hotspots and systematically improve software quality in terms of configurability, stability, and changeability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
