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CODENS addresses the persistent challenge of scattered design knowledge in fast-moving codebases by automatically constructing typed software knowledge graphs from pull requests. This system enables agent-guided graph traversal for repository-level question answering, effectively turning incremental code changes into living documentation without requiring manual updates. While evaluation on a production Ruby on Rails project demonstrates high relevance and grounded answers, the approach highlights ongoing difficulties in synthesizing concise summaries directly from complex change histories.
CODENS: Transforming Code Changes into Living, Accessible, and Queryable Documentation
arXiv:2607.18356v1 Announce Type: cross 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.
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.
Accepted at the 26th ACM Symposium on Document Engineering (DocEng 2026). DOI: https://doi.org/10.1145/3820755.3832807
Software Engineering (cs.SE); Artificial Intelligence (cs.AI)
arXiv:2607.18356 [cs.SE]
(or arXiv:2607.18356v1 [cs.SE] for this version)
https://doi.org/10.48550/arXiv.2607.18356 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
https://doi.org/10.1145/3820755.3832807 Focus to learn more DOI(s) linking to related resources
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