RepoAgent: An LLM-Powered Open-Source Framework for Repository-level Code Documentation Generation
Qinyu Luo, Yining Ye, Shihao Liang, Zhong Zhang, Yujia Qin, Yaxi Lu, Yesai Wu, Xin Cong
Abstract
Generative models have demonstrated considerable potential in software engineering, particularly in tasks such as code generation and debugging. However, their utilization in the domain of code documentation generation remains underexplored. To this end, we introduce RepoAgent, a large language model powered open-source framework aimed at proactively generating, maintaining, and updating code documentation. Through both qualitative and quantitative evaluations, we have validated the effectiveness of our approach, showing that RepoAgent excels in generating high-quality repository-level documentation. The code and results are publicly accessible at https://github.com/OpenBMB/RepoAgent.
BibTeX
@inproceedings{luo-etal-2024-repoagent,
title = "{R}epo{A}gent: An {LLM}-Powered Open-Source Framework for Repository-level Code Documentation Generation",
author = "Luo, Qinyu and
Ye, Yining and
Liang, Shihao and
Zhang, Zhong and
Qin, Yujia and
Lu, Yaxi and
Wu, Yesai and
Cong, Xin and
Lin, Yankai and
Zhang, Yingli and
Che, Xiaoyin and
Liu, Zhiyuan and
Sun, Maosong",
editor = "Hernandez Farias, Delia Irazu and
Hope, Tom and
Li, Manling",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-demo.46/",
doi = "10.18653/v1/2024.emnlp-demo.46",
pages = "436--464"
}