Kaibo Liu — accepted AI-conference papers

2025

LLM-Powered Test Case Generation for Detecting Bugs in Plausible Programs

Kaibo Liu, Zhenpeng Chen, Yiyang Liu, Jie M. Zhang, Mark Harman, Yudong Han +4

ACL 2025long

Detecting tricky bugs in plausible programs, those that pass existing test suites yet still contain bugs, remains a significant challenge in software testing. To address this problem, we propose TrickCatcher, an LLM-powered approach to generating test cases for uncovering bugs in plausible programs.…

Cited by 0 Source ↗PDF ↗code ↗Scholar ↗

2024

DevEval: A Manually-Annotated Code Generation Benchmark Aligned with Real-World Code Repositories

Jia Li, Ge Li, Yunfei Zhao, Yongmin Li, Huanyu Liu, Hao Zhu +14

ACL 2024findings

How to evaluate the coding abilities of Large Language Models (LLMs) remains an open question. We find that existing benchmarks are poorly aligned with real-world code repositories and are insufficient to evaluate the coding abilities of LLMs.To address the knowledge gap, we propose a new benchmark…

Cited by 19 Source ↗PDF ↗code ↗Scholar ↗