CODENS：将代码变更转化为生动、可访问且可查询的文档
CODENS: Transforming Code Changes into Living, Accessible, and Queryable Documentation
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中文总结 AI 辅助
研究如何在快速发展代码库中维护文档，提出CODENS系统，它从拉取请求构建知识图，经语义提取等操作，通过三种检索模式展示知识，保留变更历史并集成评估指标，在项目中评估效果良好，但在文档综合方面有挑战。
中文摘要 AI 辅助
在快速发展的代码库中维护最新代码文档很困难，因为设计知识分散在源文件和拉取请求中。我们提出了CODENS系统，它能将拉取请求转化为生产代码库的生动、可访问且可查询的文档。CODENS从拉取请求中增量构建类型化软件知识图，通过模式驱动的语义提取丰富组件，推导它们之间的类型化关系，并通过三种检索模式展示结果知识。该系统还保留拉取请求间的语义变更历史，并集成答案质量和操作评估指标。我们在一个生产中的Ruby on Rails客户端项目上评估了CODENS。结果表明CODENS能产生高度相关且有充分依据的答案，同时定性反馈突出了在简洁的、面向文档的综合方面仍存在的挑战。
英文摘要
Maintaining up-to-date code documentation is difficult in fast-moving repositories because design knowledge is scattered across source files and pull requests. We present CODENS , a system that turns pull requests into living, accessible, and queryable documentation for production codebases. CODENS incrementally builds a typed software knowledge graph from pull requests, enriches components through schema-driven semantic extraction, derives typed relations between them, and exposes the resulting knowledge through three retrieval modes, including agent-guided graph traversal for repository-level question answering. The system also preserves semantic change history across pull requests and integrates both answer-quality and operational evaluation metrics. We evaluate CODENS on a client Ruby on Rails project in production. Results show that CODENS produces highly relevant and well-grounded answers, while qualitative feedback highlights a remaining challenge in concise, documentation-oriented synthesis.
发表机构
- CAPSENS
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