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

By Qinyu Luo, Yining Ye, Shihao Liang, Zhong Zhang, Yujia Qin, Ya-Ting Lu, Yesai Wu, Xin Cong, Yankai Lin, Yingli Zhang and 3 more. 2024

102 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Information Systems, Information Systems and Management, Code Generation & Program Synthesis

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Armando Solar-Lezama
2. Koushik Sen
3. Naman Jain
4. Tianjun Zhang
5. Wen-Ding Li
6. Ion Stoica
7. Swarat Chaudhuri
8. Rishabh Singh

In Information Systems

1. Armando Solar-Lezama
2. Koushik Sen
3. Naman Jain
4. Tianjun Zhang
5. Ion Stoica
6. Rishabh Singh
7. Mosharaf Chowdhury
8. Charles Sutton

In Information Systems and Management

1. Armando Solar-Lezama
2. Koushik Sen
3. Ion Stoica

In Code Generation & Program Synthesis

1. Armando Solar-Lezama
2. Koushik Sen
3. Rishabh Singh

Citations in the corpus

• LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code (2024, background): “…domains and tasks such as program repair (Zheng et al., 2024; Olausson et al., 2023), optimization (Madaan et al., 2023a), test generation (Steenhoek et al., 2023), documentation generation (Luo et al., 2024), tool usage (Patil et al., 2023; Qin et al., 2024), SQL (Sun et al., 2023), and more.”
• SpIDER: Spatially Informed Dense Embedding Retrieval for Software Issue Localization (2025, unknown): “Further algorithmic details are provided in Section A.4 (Algorithm 1). modules within a repository when ranking them (Reddy et al., 2025; Zhang et al., 2024; Suresh et al., 2025).”