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
this https URL

RepoAgent 是一个大型语言模型驱动的开源框架，旨在主动生成、维护和更新代码文档，并通过定性和定量评估验证了其在生成高质量的存储库级文档方面的有效性。

RepoAgent: 开源框架的 LLM 驱动代码仓库级别文档生成器

RepoAgent: An LLM-Powered Open-Source Framework for Repository-level  Code Documentation Generation

Jupyter notebook allows data scientists to write machine learning code
together with its documentation in cells. In this paper, we propose a new task
of code documentation generation (CDG) for computational notebooks. In contrast
to the previous CDG tasks which focus on generating documentation for single
code snippets, in a computational notebook, one documentation in a markdown
cell often corresponds to multiple code cells, and these code cells have an
inherent structure. We proposed a new model (HAConvGNN) that uses a
hierarchical attention mechanism to consider the relevant code cells and the
relevant code tokens information when generating the documentation. Tested on a
new corpus constructed from well-documented Kaggle notebooks, we show that our
model outperforms other baseline models.

本文提出了一种新的任务 —— 计算笔记本代码文档生成（CDG），并提出了一种新模型（HAConvGNN），通过分层注意机制考虑相关代码单元和相关代码标记信息来生成文档，实验结果表明，与其他基线模型相比，我们的模型表现更优。