CodeDocAgent: Leveraging Large Language Models for Accurate and Contextual Code Documentation
Shunyu Yao, Dan Liu, Shen Yun, Jie Hu · 2025
Software maintenance is a critical phase in ensuring the long-term success and adaptability of software to user needs, often comprising a significant portion of the project’s budget. As software complexity increases, the challenge of understanding existing code grows, making high-quality code documentation increasingly important. Traditional documentation creation and maintenance are time-consuming and require specialized knowledge, prompting the development of automated or semi-automated documentation generation technologies. These technologies aim to simplify the process by analyzing code structure, comments, and usage patterns. This paper introduces CodeDocAgent, a new framework driven by large language models (LLMs) that proactively generates and maintains comprehensive documentation for entire code repositories. Unlike traditional static annotation tools, CodeDocAgent employs deep learning techniques to deeply understand the overall semantics and structural relationships of the code. It captures the global context and generates accurate, coherent, and structured documentation. CodeDocAgent emphasizes practical guidance, offering detailed instructions on how to correctly use the code. It clarifies functional boundaries, provides warnings about potential misuse, and offers abundant input-output examples, helping developers quickly grasp the codebase, reduce the learning curve, improve work efficiency, and enhance team collaboration.