Can LLMs Generate High-Quality Test Cases for Algorithm Problems?
  TestCase-Eval: A Systematic Evaluation of Fault Coverage and Exposure
Authors: name: Zheyuan Yang, name: Zexi Kuang, name: Xue Xia, name: Yilun Zhao

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 state-of-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.