How to Understand Whole Software Repository? (2406.01422v1)

Abstract: Recently, LLM based agents have advanced the significant development of Automatic Software Engineering (ASE). Although verified effectiveness, the designs of the existing methods mainly focus on the local information of codes, e.g., issues, classes, and functions, leading to limitations in capturing the global context and interdependencies within the software system. From the practical experiences of the human SE developers, we argue that an excellent understanding of the whole repository will be the critical path to ASE. However, understanding the whole repository raises various challenges, e.g., the extremely long code input, the noisy code information, the complex dependency relationships, etc. To this end, we develop a novel ASE method named RepoUnderstander by guiding agents to comprehensively understand the whole repositories. Specifically, we first condense the critical information of the whole repository into the repository knowledge graph in a top-to-down mode to decrease the complexity of repository. Subsequently, we empower the agents the ability of understanding whole repository by proposing a Monte Carlo tree search based repository exploration strategy. In addition, to better utilize the repository-level knowledge, we guide the agents to summarize, analyze, and plan. Then, they can manipulate the tools to dynamically acquire information and generate the patches to solve the real-world GitHub issues. Extensive experiments demonstrate the superiority and effectiveness of the proposed RepoUnderstander. It achieved 18.5\% relative improvement on the SWE-bench Lite benchmark compared to SWE-agent.

Analyzing "How to Understand Whole Software Repository?"

The paper "How to Understand Whole Software Repository?" authored by Yingwei Ma, Qingping Yang, Rongyu Cao, Binhua Li, Fei Huang, and Yongbin Li from Alibaba Group, proposes a significant advancement in the field of Automatic Software Engineering (ASE). This work introduces RepoUnderstander, an agent-based method designed to guide LLM-based agents to acquire a comprehensive understanding of entire software repositories.

Core Contributions

1. Problem Context

The authors situate their work within the broader scope of ASE, acknowledging recent advancements driven by LLM-based agents. However, they identify a gap in existing methods, which predominantly focus on local code information such as issues, classes, and functions. This local focus leads to a failure in capturing the global context and interdependencies within software systems, which are crucial for complex tasks in ASE.

2. RepoUnderstander Overview

The proposed RepoUnderstander method aims to address these limitations by developing a comprehensive understanding of whole repositories. The paper outlines several steps:

1. Repository Knowledge Graph Construction: A hierarchical tree structure is constructed from the repository, summarizing essential code snippets and their interdependencies.
2. Monte Carlo Tree Search (MCTS) Strategy: An exploration strategy based on MCTS is deployed to navigate the repository knowledge graph, focusing on nodes with high relevance scores.
3. Information Utilization and Patch Generation: Agents are guided to summarize and analyze the collected information, ultimately generating patches to resolve real-world GitHub issues.

Key Methodological Insights

The approach leverages several technical innovations:

• Top-down Repository Knowledge Graph Construction: By organizing repository information into a hierarchical structure, the method significantly reduces complexity, making it easier for agents to navigate and understand the code context.
• MCTS for Repository Exploration: The use of MCTS represents a nuanced strategy for effective repository understanding. By simulating multiple paths and evaluating reward scores, the method narrows down the search space to focus on the most relevant areas.
• In-context Learning and Chain-of-Thought for Reward Evaluation: These techniques enable a nuanced assessment of node relevance, ensuring that the agents can effectively prioritize important information.

Empirical Validation

The paper's empirical section demonstrates the method’s performance using the SWE-bench Lite benchmark, showing an 18.5% relative improvement over the current leading method, SWE-agent. Crucially, RepoUnderstander achieved a problem-solving rate of 21.33%, the highest among competitive baselines. These results underscore the effectiveness of understanding the global context within repositories for ASE tasks.

Practical and Theoretical Implications

Practical Implications

RepoUnderstander’s ability to understand and navigate large codebases can significantly enhance the efficiency and accuracy of ASE tasks such as fault localization and program repair. The method’s applicability to real-world GitHub issues highlights its practical relevance and potential for widespread adoption in the software engineering industry.

Theoretical Implications

The framework demonstrates a shift from local to global understanding in software repositories, suggesting that future ASE methods should prioritize holistic repository comprehension. This could lead to more sophisticated models capable of tackling increasingly complex software engineering challenges.

Speculative Outlook

As LLMs and ASE capabilities evolve, future developments may integrate RepoUnderstander with runtime feedback mechanisms. Combining comprehensive repository understanding with dynamic execution feedback could further enhance the robustness and accuracy of ASE tools, paving the way for fully autonomous software maintenance and development systems.

Conclusion

The paper presents a robust method for whole repository understanding, significantly contributing to the ASE field. RepoUnderstander’s innovative use of hierarchical knowledge graphs, MCTS, and advanced LLM techniques sets a new standard for future research, emphasizing the critical role of global context in complex software engineering tasks.