Class-Level Code Generation from Natural Language Using Iterative, Tool-Enhanced Reasoning over Repository

TL;DR

This paper introduces RepoClassBench, a benchmark for evaluating large language models in generating class-level code within real-world repositories, and proposes RRR, a tool-enhanced iterative reasoning approach that significantly improves performance.

Contribution

The paper presents a new benchmark for class-level code generation in repositories and a novel tool-augmented method that enhances LLMs' ability to handle complex, real-world software dependencies.

Findings

RRR outperforms existing models on RepoClassBench

Models struggle with repository-level context without specialized tools

Tool-enhanced reasoning improves code generation accuracy

Abstract

LLMs have demonstrated significant potential in code generation tasks, achieving promising results at the function or statement level across various benchmarks. However, the complexities associated with creating code artifacts like classes, particularly within the context of real-world software repositories, remain underexplored. Prior research treats class-level generation as an isolated task, neglecting the intricate dependencies & interactions that characterize real-world software environments. To address this gap, we introduce RepoClassBench, a comprehensive benchmark designed to rigorously evaluate LLMs in generating complex, class-level code within real-world repositories. RepoClassBench includes "Natural Language to Class generation" tasks across Java, Python & C# from a selection of repositories. We ensure that each class in our dataset not only has cross-file dependencies within the repository but also includes corresponding test cases to verify its functionality. We find that current models struggle with the realistic challenges posed by our benchmark, primarily due to their limited exposure to relevant repository contexts. To address this shortcoming, we introduce Retrieve-Repotools-Reflect (RRR), a novel approach that equips LLMs with static analysis tools to iteratively navigate & reason about repository-level context in an agent-based framework. Our experiments demonstrate that RRR significantly outperforms existing baselines on RepoClassBench, showcasing its effectiveness across programming languages & under various settings. Our findings emphasize the critical need for code-generation benchmarks to incorporate repo-level dependencies to more accurately reflect the complexities of software development. Our work shows the benefits of leveraging specialized tools to enhance LLMs' understanding of repository context. We plan to make our dataset & evaluation harness public.