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Standards-Guided and Dependency-Aware Code-Level Documentation Generation

Tingxu Han, Wei Song, Ziming Li, Yuchen Chen, Weisong Sun, Zhenpeng Chen, Chunrong Fang, Zhenyu Chen, Yang Liu · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.27634 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

ソフトウェアリポジトリのための高品質なドキュメンタリーを自動的にトークホーベースで生成することは依然として挑戦的である:既存のアプローチは、ドキュメンタリーをほとんど制限されないテキスト生成として扱い、明確な品質要件を欠く、権威ある基準から生じた明確な再利用可能な品質要件に大きく依存している。 Python は、リポジトリ機能とクラスの自然言語の説明としてドキュメンタリーを定義し、ソースコードレベルのドキュメンタリー生成は、代わりに、標準ガイドされたソフトウェアエンジニアリングタスクであるべきであると主張する。 CODOCED は、標準ガイドと依存意識の枠組みであり、ドキュ

Machine translation (Meta M2M100); the English original follows.

Abstract

Automatically generating high-quality documentation for software repositories remains challenging: existing approaches largely treat documentation as unconstrained text generation, lacking explicit quality specifications, depending heavily on long-context prompts over raw code, and offering limited support for reproducible evaluation. We argue that code-level documentation generation should instead be a standards-guided software engineering task, in which documentation is generated and assessed against explicit, reusable quality requirements derived from authoritative standards. We define code documentation as natural-language descriptions of repository functions and classes, organized by source-code path. We present CODEDOC, a standards-guided and dependency-aware framework that identifies dependencies between code entities via static call-graph and AST analysis and constructs dependency-aware structured prompts combining local code context, repository paths, and documentation criteria drawn from authoritative standards and style guides. We further introduce an interactive LLM-as-a-judge protocol that supplies criterion-aligned feedback to refine generated documentation along fine-grained dimensions from formal and practice-oriented standards. Experiments on ten open-source Python repositories show that CODEDOC produces higher-quality documentation than RepoAgent and a long-context baseline under standards-aligned evaluation, with relative improvements of 11.6%-17.0% and 21.4%-29.6%, respectively, while consuming roughly an order of magnitude fewer tokens per documentation entity than the long-context baseline. A human study confirms substantial alignment between LLM-based and human judgments (Spearman's rho = 0.72), and the relative findings hold across different judge LLMs. Code is available at https://github.com/ControllableModel/CodeDoc.

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Topics: Documentation, Software documentation, Internal documentation, Computer science, Technical documentation, Software engineering

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