用大模型自动生成代码文档并多维度评估质量，提升医疗软件可维护性。
LLM-Based Code Documentation Generation and Multi-Judge Evaluation
- 用8个主流大模型结合提示工程生成结构化代码文档。
- 在医疗物理库上测试，顶尖与落后模型差距达42%。
- 四模型互评框架确保文档质量，适合高安全要求场景。
高质量代码文档对关键领域（如医疗）至关重要，但常被忽视。我们提出一个基于AI的自动化框架，利用八种先进大语言模型（包括GPT、Gemini、Qwen和LLaMA系列）从代码和仓库中生成文档。该系统基于PocketFlow编排框架，采用模块化流程和高级提示工程，生成上下文感知的结构化文档。为保证质量并指导模型选择，我们引入了MultiLLMasJudges评估框架，由四个独立的LLM在九个维度（如完整性、清晰度、忠实度）上评估输出。在开源医疗物理库上的实验表明，顶级模型与最差模型间性能差距达42%。通过融合多样模型输出、优化提示策略及严格评估，本方法显著提升文档质量，减少人工工作量，尤其适用于安全敏感的医疗软件开发。
原文摘要 · Abstract (English)
High-quality source code documentation is vital yet often neglected, especially in critical domains like healthcare where reliability and maintainability are essential. We presented an AI powered framework that automates documentation generation from code and repositories using eight state of the art Large Language Models (LLMs), including GPT, Gemini, Qwen, and LLaMA variants. Built on the PocketFlow orchestration framework, the system applies modular pipelines and advanced prompt engineering to produce structured, context aware documentation. To ensure quality and guide model selection, we introduced a MultiLLMasJudges evaluation framework, where four independent LLMs assess outputs across nine criteria, such as Completeness, Clarity, and Faithfulness. Experiments conducted on an open-source medical physics library, demonstrated showed a 42% performance gap between top and bottom models. By combining diverse model outputs, optimized prompting, and rigorous evaluation, our approach enhances documentation quality and reduces manual effort, especially in safety critical healthcare software.
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