CodeRAG: Supportive Code Retrieval on Bigraph for Real-World Code Generation

• URL: http://arxiv.org/abs/2504.10046v1
• Date: Mon, 14 Apr 2025 09:51:23 GMT
• Title: CodeRAG: Supportive Code Retrieval on Bigraph for Real-World Code Generation
• Authors: Jia Li, Xianjie Shi, Kechi Zhang, Lei Li, Ge Li, Zhengwei Tao, Jia Li, Fang Liu, Chongyang Tao, Zhi Jin,
• Abstract summary: Large language models (LLMs) have shown promising performance in automated code generation.<n>In this paper, we propose CodeRAG, a retrieval-augmented code generation framework.<n> Experiments show that CodeRAG achieves significant improvements compared to no RAG scenarios.
• Score: 69.684886175768
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) have shown promising performance in automated code generation, especially excelling in simple tasks such as generating standalone codes. Different from simple tasks, real-world code generation usually depends on specific programming environment (e.g., code repositories). It contains complex dependencies and domain knowledge, which is needed for LLMs when generating target code snippets. In this paper, we propose CodeRAG, a retrieval-augmented code generation (RAG) framework to comprehensively retrieve supportive codes for real-world code generation. Beginning with the requirement, CodeRAG first constructs a requirement graph for the current repository, and retrieves sub- and similar- requirement nodes of the target requirement on the graph. Meanwhile, it models the repository into a DS-code graph. CodeRAG then maps these relevant requirement nodes into their corresponding code nodes, and treats these code nodes as archors for LLM reasoning on DS-code graph. Finally, CodeRAG introduces a code-oriented agentic reasoning process, seamlessly allowing LLMs to reason and comprehensively retrieve for supportive codes which LLMs' need for generating correct programs. Experiments show that CodeRAG achieves significant improvements (i.e., increasing 40.90 and 37.79 Pass@1 on GPT-4o and Gemini-Pro on DevEval) compared to no RAG scenarios. Further tests on reasoning LLMs (i.e., QwQ-32B) confirm CodeRAG's adaptability and efficacy across various types of LLMs. In addition, CodeRAG outperforms commercial programming products such as Copilit and Cursor. We further investigate the performance of our framework on different dependency types, and observe that CodeRAG is superior in generating examples where target codes invoke predefined cross-file code snippets. These results demonstrate CodeRAG's potential in solving real-world repo-level coding challenges.

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