CodeRAG-Bench: Can Retrieval Augment Code Generation? (2406.14497v1)

Abstract: While LLMs (LMs) have proven remarkably adept at generating code, many programs are challenging for LMs to generate using their parametric knowledge alone. Providing external contexts such as library documentation can facilitate generating accurate and functional code. Despite the success of retrieval-augmented generation (RAG) in various text-oriented tasks, its potential for improving code generation remains under-explored. In this work, we conduct a systematic, large-scale analysis by asking: in what scenarios can retrieval benefit code generation models? and what challenges remain? We first curate a comprehensive evaluation benchmark, CodeRAG-Bench, encompassing three categories of code generation tasks, including basic programming, open-domain, and repository-level problems. We aggregate documents from five sources for models to retrieve contexts: competition solutions, online tutorials, library documentation, StackOverflow posts, and GitHub repositories. We examine top-performing models on CodeRAG-Bench by providing contexts retrieved from one or multiple sources. While notable gains are made in final code generation by retrieving high-quality contexts across various settings, our analysis reveals room for improvement -- current retrievers still struggle to fetch useful contexts especially with limited lexical overlap, and generators fail to improve with limited context lengths or abilities to integrate additional contexts. We hope CodeRAG-Bench serves as an effective testbed to encourage further development of advanced code-oriented RAG methods.

Understanding CodeRAG-Bench: Can Retrieval Augment Code Generation?

The paper "CodeRAG-Bench: Can Retrieval Augment Code Generation?" offers an extensive exploration into the domain of code generation, particularly investigating the utility of retrieval-augmented generation (RAG) methods in this context. The research sheds light on an intriguing aspect of code generation, namely, how external information, typically in the form of relevant documents, can improve the capabilities of LLMs (LMs) that are tasked with generating code.

Analysis of Retrieval-Augmented Code Generation (RACG)

The authors recognize that while LMs have shown remarkable capabilities in code generation, their performance can be hindered when faced with complex programming tasks, especially those that involve unfamiliar libraries or require up-to-date knowledge of public libraries and private codebases. The core proposition of the paper is to explore whether RACG offers substantial improvements over traditional code generation methods.

To systematically evaluate this hypothesis, the authors introduced "CodeRAG-Bench," a benchmark specifically designed for assessing the efficacy of RACG systems. This benchmark spans a variety of code generation tasks, categorized into basic programming, open-domain problems, and repository-level challenges. The introduction of such a diverse set of tasks facilitates a comprehensive evaluation of RACG methodologies across different contexts and problem types.

Integral Findings and Observations

The paper makes several key observations regarding RACG:

1. Benchmarking Diverse Tasks: CodeRAG-Bench is curated to include tasks of varying complexity and domain requirements. Basic programming problems often deal with algorithmic challenges, whereas open-domain problems necessitate the use of multiple libraries. Repository-level problems involve completion tasks that require a contextual understanding of linked files and functions.
2. Retrieval Sources: Five primary sources — programming solutions, online tutorials, library documentation, StackOverflow posts, and GitHub repositories — serve as the backbone for document retrieval. This comprehensive source pool enables RACG systems to access varied and potentially useful external contexts during code generation.
3. Document Retrieval Challenges: Despite the theoretical advantages offered by RACG, the paper identifies challenges related to document retrieval, notably the difficulty in obtaining accurate and contextually relevant documents. Models such as BM25 and dense retrievers show varied performance across different tasks, indicating that retrieval improvements are vital for effective RACG.
4. Generation with Context Utilization: Incorporating retrieved documents into the generation process often yields performance improvements, particularly noticeable in tasks where code specifics are detailed in external sources. However, the paper notes the limited context capacity of existing models, which constrains their ability to leverage lengthy documents effectively.
5. Potential of Reranking and Robust Models: The paper explores reranking strategies, although these do not consistently enhance retrieval quality. Additionally, stronger models are observed to better utilize RACG methodologies, demonstrating notable improvements with aggregated sources.

Theoretical and Practical Implications

The research conducted within the paper provides valuable insights into the practical and theoretical implications of RACG:

• Efficiency Considerations: The paper’s exploration of diverse retrieval models underlines the efficiency trade-offs in RACG, particularly in terms of document encoding latency, search latency, and storage requirements, emphasizing the importance of optimizing these factors.
• Model Robustness and Context Handling: The effectiveness of RACG systems is heavily reliant on the robustness of models to handle noisy or irrelevant contexts. Future advancements might focus on improving models' context filtering abilities to mitigate distraction from non-contributory documents.
• Extending RACG to More Tasks: CodeRAG-Bench serves as a precursor to future endeavors in realizing RACG across an even broader spectrum of programming languages and task categories. The benchmark lays a critical foundation for ongoing research to refine and enhance RACG systems.

Conclusion and Future Directions

"CodeRAG-Bench: Can Retrieval Augment Code Generation?" represents a step forward in the understanding and development of more effective RACG systems. By providing a nuanced analysis of how retrieved documents can be integrated into the code generation process, the paper sets the stage for further research into optimizing the retrieval process and improving code generation models.

The authors suggest that CodeRAG-Bench could act as a robust testbed for future pursuits in advancing RACG. They invite the research community to leverage this benchmark to experiment with novel strategies that enhance retrieval effectiveness and context assimilation, paving the way for next-generation code generation solutions.