一作ACL 2026Poster· Natural Language Processing
C ode H acker: Automated Test Case Generation for Detecting Vulnerabilities in Competitive Programming Solutions
Jingwei Shi · Xinxiang Yin · Jing Huang · Shengyu Tao · Jinman Zhao
摘要
The evaluation of Large Language Models (LLMs) for code generation relies heavily on the quality and robustness of test cases. However, existing benchmarks often lack coverage for subtle corner cases, allowing incorrect solutions to pass. To bridge this gap, we propose CodeHacker, an automated ag...