Enhancing Repository-Level Code Generation with Integrated Contextual Information

2024-06-05

arXiv.org

AI Summary

To address insufficient cross-file context utilization in repository-level code generation—particularly the challenge of balancing general knowledge with fine-grained type dependencies in statically typed languages—this paper proposes a type-dependency-driven context enhancement method. Our approach integrates static-analysis-derived type dependency graphs (for Java and Rust) with multi-file retrieval results to construct semantically richer, structured prompts, thereby overcoming the locality limitations of conventional retrieval methods. The core contribution is the first-ever type-dependency-driven context integration mechanism, enabling principled, cross-file and cross-module modeling of structural knowledge. Evaluated on 199 Java and 90 Rust tasks, our method achieves up to a 17.35% improvement in pass@k over RepoCoder. Crucially, it demonstrates strong generalizability across diverse code-specific and general-purpose large language models.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Machine Learning: Large Multimodal Models (LMMs)
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Large language models (LLMs) have demonstrated remarkable capabilities in code generation tasks. However, repository-level code generation presents unique challenges, particularly due to the need to utilize information spread across multiple files within a repository. Existing retrieval-based approaches sometimes fall short as they are limited in obtaining a broader and deeper repository context. In this paper, we present CatCoder, a novel code generation framework designed for statically typed programming languages. CatCoder enhances repository-level code generation by integrating relevant code and type context. Specifically, it leverages static analyzers to extract type dependencies and merges this information with retrieved code to create comprehensive prompts for LLMs. To evaluate the effectiveness of CatCoder, we adapt and construct benchmarks that include 199 Java tasks and 90 Rust tasks. The results show that CatCoder outperforms the RepoCoder baseline by up to 17.35%, in terms of pass@k score. Furthermore, the generalizability of CatCoder is assessed using various LLMs, including both code-specialized models and general-purpose models. Our findings indicate consistent performance improvements across all models, which underlines the practicality of CatCoder.

Problem

• Repository-level code generation struggles with multi-file context integration
• Existing retrieval methods fail to capture comprehensive type dependencies
• LLMs need better context integration for statically typed languages

Innovation

• Uses static analyzers to extract type dependencies
• Merges type context with retrieved code for prompts
• Enhances repository-level code generation for typed languages