LLM-Based Code Documentation Generation and Multi-Judge Evaluation
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Computer Science > Human-Computer Interaction
arXiv:2606.09852 (cs)
[Submitted on 11 May 2026]
Title:LLM-Based Code Documentation Generation and Multi-Judge Evaluation
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Abstract: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.
| Comments: | ICAHS, \c{opyright} 2025 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works |
| Subjects: | Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG); Multiagent Systems (cs.MA); Software Engineering (cs.SE) |
| Cite as: | arXiv:2606.09852 [cs.HC] |
| (or arXiv:2606.09852v1 [cs.HC] for this version) | |
| https://doi.org/10.48550/arXiv.2606.09852
arXiv-issued DOI via DataCite
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| Journal reference: | Conference ICAHS IEEE, 2025 |
Submission history
From: Ines Abdeljaoued-Tej PhD [view email][v1] Mon, 11 May 2026 11:17:58 UTC (83 KB)
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