🤖 AI Summary
This work addresses the challenge of outdated documentation and fragmented design knowledge in rapidly evolving codebases, where critical insights are scattered across source code and pull requests. To tackle this, the authors propose a method for incrementally constructing a typed software knowledge graph grounded in pull request data. Leveraging schema-driven semantic extraction and relation inference, the system supports three retrieval modalities—including agent-guided graph traversal—enabling question-answering over a living documentation system and tracking semantic change histories. This approach represents the first effort to automatically transform pull requests into a structured, queryable, and continuously evolving knowledge base. Evaluation on a real-world Ruby on Rails project demonstrates the system’s ability to generate highly relevant, evidence-backed answers, though user feedback indicates room for improvement in the conciseness of synthesized documentation.
📝 Abstract
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.