Practical Solutions for AI-Driven Software Engineering
Addressing the Challenge of Large Code Repositories
Large Language Models (LLMs) struggle with handling entire code repositories due to the complexity of code structures and dependencies. Current methods like similarity-based retrieval and manual tools have limitations in effectively supporting LLMs in navigating and understanding large code repositories.
Introducing CODEXGRAPH: Enhancing LLM Interaction with Code Repositories
CODEXGRAPH integrates LLMs with graph database interfaces, enabling precise, code structure-aware context retrieval and navigation. This approach introduces a significant innovation by improving the interaction between LLMs and code repositories, leading to better performance in both academic benchmarks and real-world applications.
Key Features of CODEXGRAPH
- Constructs a code graph database representing symbols and relationships in the code
- Utilizes a two-phase process for capturing and analyzing code structures
- Enables LLM agents to interact with the graph database through natural language queries
- Evaluates performance across repository-level benchmarks, demonstrating competitive results
Advantages of CODEXGRAPH
CODEXGRAPH significantly surpasses other methods in achieving exact match scores and demonstrates effectiveness in complex, reasoning-heavy tasks, showcasing its potential in real-world software development scenarios.
Impact of CODEXGRAPH
CODEXGRAPH enhances the ability of LLMs to navigate and retrieve relevant information from large code repositories, contributing to the advancement of automated software engineering and paving the way for more efficient and accurate LLM-driven coding solutions.
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