TESTEVAL: Benchmarking Large Language Models for Test Case Generation

TL;DR

This paper introduces TESTEVAL, a comprehensive benchmark for evaluating large language models' ability to generate test cases for Python programs, revealing current limitations in understanding program logic.

Contribution

We created a new benchmark dataset and evaluation framework for LLMs in test case generation, enabling fair comparison across models.

Findings

LLMs struggle with targeted coverage tasks

Current models have limited understanding of program logic

Benchmark datasets are now publicly available

Abstract

Testing plays a crucial role in the software development cycle, enabling the detection of bugs, vulnerabilities, and other undesirable behaviors. To perform software testing, testers need to write code snippets that execute the program under test. Recently, researchers have recognized the potential of large language models (LLMs) in software testing. However, there remains a lack of fair comparisons between different LLMs in terms of test case generation capabilities. In this paper, we propose TESTEVAL, a novel benchmark for test case generation with LLMs. We collect 210 Python programs from an online programming platform, LeetCode, and design three different tasks: overall coverage, targeted line/branch coverage, and targeted path coverage. We further evaluate sixteen popular LLMs, including both commercial and open-source ones, on TESTEVAL. We find that generating test cases to cover specific program lines/branches/paths is still challenging for current LLMs, indicating a lack of ability to comprehend program logic and execution paths. We have open-sourced our dataset and benchmark pipelines at https://github.com/LLM4SoftwareTesting/TestEval.