LocAgent: Graph-Guided LLM Agents for Code Localization
LocAgent is an innovative framework designed to enhance code localization through a graph-based representation of codebases. By transforming code into directed heterogeneous graphs, LocAgent captures essential structures and dependencies, allowing Large Language Model (LLM) agents to accurately search and identify relevant code entities through multi-hop reasoning.
Key Features:
- Graph-Based Representation: Efficiently transforms codebases into directed graphs.
- Multi-Hop Reasoning: Enables effective searching capabilities leveraging LLMs.
- Benchmarking: Compatible with linked datasets like SWE-Bench and Loc-Bench, tailored for code localization tasks.
- Easy Setup: Clear instructions for setting up the development environment and launching the framework.
Benefits:
- Improves the efficiency of code localization processes.
- Facilitates deeper understanding of code relationships for AI agents.
- Streamlines the evaluation of localization tasks with JSONL output formatting.
Highlights:
- Supports multiple datasets and processing modes for extensive evaluations.
- Integrates well with existing Python environments via conda and pip.
- Results evaluation and performance metrics available through dedicated scripts and notebooks.
