ClassEval: A Manually-Crafted Benchmark for Evaluating LLMs on Class-level Code Generation

TL;DR

This paper introduces ClassEval, a manually crafted benchmark for evaluating large language models on class-level Python code generation, revealing significant performance gaps compared to method-level tasks and analyzing model capabilities and strategies.

Contribution

The paper presents the first class-level code generation benchmark, ClassEval, and provides a comprehensive study of 11 state-of-the-art LLMs on this challenging task.

Findings

All LLMs perform worse on class-level than method-level code generation.

GPT-4 and GPT-3.5 outperform other models significantly.

Holistic generation works best for GPT-4 and GPT-3.5, while incremental methods suit smaller models.

Abstract

In this work, we make the first attempt to evaluate LLMs in a more challenging code generation scenario, i.e. class-level code generation. We first manually construct the first class-level code generation benchmark ClassEval of 100 class-level Python code generation tasks with approximately 500 person-hours. Based on it, we then perform the first study of 11 state-of-the-art LLMs on class-level code generation. Based on our results, we have the following main findings. First, we find that all existing LLMs show much worse performance on class-level code generation compared to on standalone method-level code generation benchmarks like HumanEval; and the method-level coding ability cannot equivalently reflect the class-level coding ability among LLMs. Second, we find that GPT-4 and GPT-3.5 still exhibit dominate superior than other LLMs on class-level code generation, and the second-tier models includes Instruct-Starcoder, Instruct-Codegen, and Wizardcoder with very similar performance. Third, we find that generating the entire class all at once (i.e. holistic generation strategy) is the best generation strategy only for GPT-4 and GPT-3.5, while method-by-method generation (i.e. incremental and compositional) is better strategies for the other models with limited ability of understanding long instructions and utilizing the middle information. Lastly, we find the limited model ability of generating method-dependent code and discuss the frequent error types in generated classes. Our benchmark is available at https://github.com/FudanSELab/ClassEval.