Can LLMs Generate High-Quality Test Cases for Algorithm Problems? TestCase-Eval: A Systematic Evaluation of Fault Coverage and Exposure
Zheyuan Yang, Zexi Kuang, Xue Xia, Yilun Zhao · 2025
We introduce TestCase-Eval, a new benchmark for systematic evaluation of LLMs in test-case generation.TestCase-Eval includes 500 algorithm problems and 100,000 human-crafted solutions from the Codeforces platform.It focuses on two pivotal tasks: (1) Fault Coverage, which measures how well LLM-generated test sets probe diverse input scenarios and cover a wide range of potential failure modes.(2) Fault Exposure, which evaluates whether LLMs can craft a tailored test input that reveals a specific incorrect code implementation.We provide a comprehensive assessment of 19 stateof-the-art open-source and proprietary LLMs on TestCase-Eval, offering insights into their strengths and limitations in generating effective test cases for algorithm problems.