@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://preview.aclanthology.org/add-emnlp-2024-awards/2024.emnlp-demo.46/",
    doi = "10.18653/v1/2024.emnlp-demo.46",
    pages = "436--464",
    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."
}