代码与文档变更的关联：初步分析
Linking Code and Documentation Churn: Preliminary Analysis
AI总结：
该研究针对代码与文档变更不同步影响项目可维护性的问题，通过分析3个GitHub开源项目，探究二者同步性及文档自动生成可行性，指出LLM等技术可助力同步，以提升开发效率与项目健康度。
摘要：
代码变更（code churn）是指对项目中新增、修改或删除的代码量的度量，常用于评估代码库的稳定性与可维护性。程序理解，即变更的可理解程度，对可维护性同样至关重要。文档对于知识传递至关重要，尤其是在新维护者接手废弃代码时。我们强调必须同步更新对应文档，因为这反映了项目的健康度以及作为第三方库的可信度。因此，我们认为每一次代码变更都应触发文档更新（即文档变更，documentation churn）。将代码变更与文档更新关联起来对项目可持续性至关重要，因为它能促进知识传递，降低程序理解所需的工作量。\n本研究调查了3个GitHub开源项目中代码变更与文档更新的同步性。我们将采用定性分析与仓库挖掘方法，考察代码变更与文档更新随时间推移的匹配度与相关性。我们希望识别出哪些代码变更可能与文档同步，以及文档可自动生成的程度。初步结果显示，不同项目的同步程度存在差异，凸显了集成式同步文档实践的重要性，并为如何利用以大语言模型（即LLMs）为形式的AI等前沿技术保持代码与文档变更同步提供了思路。本研究的创新之处在于，证明了将代码变更与文档更新同步可通过提升多样性与效率来改善开发生命周期。
英文摘要：
Code churn refers to the measure of the amount of code added, modified, or deleted in a project and is often used to assess codebase stability and maintainability. Program comprehension or how understandable the changes are, is equally important for maintainability. Documentation is crucial for knowledge transfer, especially when new maintainers take over abandoned code. We emphasize the need for corresponding documentation updates, as this reflects project health and trustworthiness as a third-party library. Therefore, we argue that every code change should prompt a documentation update (defined as documentation churn). Linking code churn changes with documentation updates is important for project sustainability, as it facilitates knowledge transfer and reduces the effort required for program comprehension. This study investigates the synchrony between code churn and documentation updates in three GitHub open-source projects. We will use qualitative analysis and repository mining to examine the alignment and correlation of code churn and documentation updates over time. We want to identify which code changes are likely synchronized with documentation and to what extent documentation can be auto-generated. Preliminary results indicate varying degrees of synchrony across projects, highlighting the importance of integrated concurrent documentation practices and providing insights into how recent technologies like AI, in the form of Large Language Models (i.e., LLMs), could be leveraged to keep code and documentation churn in sync. The novelty of this study lies in demonstrating how synchronizing code changes with documentation updates can improve the development lifecycle by enhancing diversity and efficiency.
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。
