@inproceedings{yang-etal-2025-docagent,
title = "{D}oc{A}gent: A Multi-Agent System for Automated Code Documentation Generation",
author = "Yang, Dayu and
Simoulin, Antoine and
Qian, Xin and
Liu, Xiaoyi and
Cao, Yuwei and
Teng, Zhaopu and
Yang, Grey",
editor = "Mishra, Pushkar and
Muresan, Smaranda and
Yu, Tao",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-demo.44/",
doi = "10.18653/v1/2025.acl-demo.44",
pages = "460--471",
ISBN = "979-8-89176-253-4",
abstract = "High-quality code documentation is crucial for software development especially in the era of AI. However, generating it automatically using Large Language Models (LLMs) remains challenging, as existing approaches often produce incomplete, unhelpful, or factually incorrect outputs. We introduce DocAgent, a novel multi-agent collaborative system using topological code processing for incremental context building. Specialized agents (Reader, Searcher, Writer, Verifier, Orchestrator) then collaboratively generate documentation. We also propose a multi-faceted evaluation framework assessing Completeness, Helpfulness, and Truthfulness. Comprehensive experiments show DocAgent significantly outperforms baselines consistently. Our ablation study confirms the vital role of the topological processing order. DocAgent offers a robust approach for reliable code documentation generation in complex and proprietary repositories."
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<abstract>High-quality code documentation is crucial for software development especially in the era of AI. However, generating it automatically using Large Language Models (LLMs) remains challenging, as existing approaches often produce incomplete, unhelpful, or factually incorrect outputs. We introduce DocAgent, a novel multi-agent collaborative system using topological code processing for incremental context building. Specialized agents (Reader, Searcher, Writer, Verifier, Orchestrator) then collaboratively generate documentation. We also propose a multi-faceted evaluation framework assessing Completeness, Helpfulness, and Truthfulness. Comprehensive experiments show DocAgent significantly outperforms baselines consistently. Our ablation study confirms the vital role of the topological processing order. DocAgent offers a robust approach for reliable code documentation generation in complex and proprietary repositories.</abstract>
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%0 Conference Proceedings
%T DocAgent: A Multi-Agent System for Automated Code Documentation Generation
%A Yang, Dayu
%A Simoulin, Antoine
%A Qian, Xin
%A Liu, Xiaoyi
%A Cao, Yuwei
%A Teng, Zhaopu
%A Yang, Grey
%Y Mishra, Pushkar
%Y Muresan, Smaranda
%Y Yu, Tao
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-253-4
%F yang-etal-2025-docagent
%X High-quality code documentation is crucial for software development especially in the era of AI. However, generating it automatically using Large Language Models (LLMs) remains challenging, as existing approaches often produce incomplete, unhelpful, or factually incorrect outputs. We introduce DocAgent, a novel multi-agent collaborative system using topological code processing for incremental context building. Specialized agents (Reader, Searcher, Writer, Verifier, Orchestrator) then collaboratively generate documentation. We also propose a multi-faceted evaluation framework assessing Completeness, Helpfulness, and Truthfulness. Comprehensive experiments show DocAgent significantly outperforms baselines consistently. Our ablation study confirms the vital role of the topological processing order. DocAgent offers a robust approach for reliable code documentation generation in complex and proprietary repositories.
%R 10.18653/v1/2025.acl-demo.44
%U https://aclanthology.org/2025.acl-demo.44/
%U https://doi.org/10.18653/v1/2025.acl-demo.44
%P 460-471
Markdown (Informal)
[DocAgent: A Multi-Agent System for Automated Code Documentation Generation](https://aclanthology.org/2025.acl-demo.44/) (Yang et al., ACL 2025)
ACL
- Dayu Yang, Antoine Simoulin, Xin Qian, Xiaoyi Liu, Yuwei Cao, Zhaopu Teng, and Grey Yang. 2025. DocAgent: A Multi-Agent System for Automated Code Documentation Generation. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 460–471, Vienna, Austria. Association for Computational Linguistics.