CodeRAG-Bench: Can Retrieval Augment Code Generation?
Zora Zhiruo Wang, Akari Asai, Xinyan Velocity Yu, Frank F. Xu, Yiqing Xie, Graham Neubig, Daniel Fried · 2025
While language models (LMs) excel at generating code, many programs are difficult to generate using only parametric knowledge.Despite the success of retrieval-augmented generation (RAG) in text-centric tasks, its potential for code generation remains under-explored.This work introduces CODERAG-BENCH, a holistic retrieval-augmented code generation benchmark covering tasks like basic programming, open-domain, and repository-level problems and provide reproducible evaluations on both retrieval and end-to-end code generation performance.We further create a diverse, open datastore for code retrieval, aggregating sources such as competition solutions, tutorials, library documentation, StackOverflow posts, and GitHub repositories.Based on CODERAG-BENCH, we conduct large-scale evaluations of 10 retrievers and 10 LMs and systematically analyze when retrieval can benefit code generation models and identify remaining challenges.We find that while retrieving high-quality contexts improves code generation, retrievers often struggle to fetch useful contexts, and generators face limitations in using those contexts effectively.We hope CODERAG-BENCH encourages further development in code-oriented RAG methods.