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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

Hindi

लंबे समय तक मौजूदा दृष्टिकोणों में दस्तावेज़ को ज्यादातर असीमित पाठ उत्पादन के रूप में संदर्भित किया जाता है, विशिष्ट गुणवत्ता आवश्यकताओं के विपरीत, लंबे संदर्भ के अनुमोदन पर निर्भर करता है, और पुनरावृत्ति योग्य मूल्यांकन के लिए सीमित समर्थन प्रदान करता है: हम तर्क देते हैं कि कोड स्तर दस्तावेज़ उत्पन्न करने के बजाय एक मानक-आधारित सॉफ्टवेयर इंजीनियरिंग कार्य होना चाहिए, जिसमें दस्तावेज़ उत्पन्न किया जाता है और स्पष्ट, पुनरावृत्ति योग्य गुणवत्ता आवश्यकताओं के खिलाफ मूल्यांकन किया जाता है, जो प्राधिकरण मानकों से उत्पन्न होता है। हम कोड दस्तावेज़ को प्राकृतिक-भाषा वर्णन के रूप में परिभाषित करते

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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