LLM-Based Code Documentation Generation and Multi-Judge Evaluation
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 acr...
| Authors | Ikbel Ghrab, Mohamed Dhieb, Ismail Khenissi et al. |
|---|---|
| Primary category | cs.HC |
| Published | 11 May 2026 |
| Last updated at source | 11 May 2026 |
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