@inproceedings{liu-etal-2025-llm,
title = "{LLM}-Powered Test Case Generation for Detecting Bugs in Plausible Programs",
author = "Liu, Kaibo and
Chen, Zhenpeng and
Liu, Yiyang and
Zhang, Jie M. and
Harman, Mark and
Han, Yudong and
Ma, Yun and
Dong, Yihong and
Li, Ge and
Huang, Gang",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.20/",
doi = "10.18653/v1/2025.acl-long.20",
pages = "430--440",
ISBN = "979-8-89176-251-0",
abstract = "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. TrickCatcher operates in three stages: First, it uses an LLM to generate program variants based on the program under test (PUT) and its specification. Second, it employs an LLM to construct an input generator from the specification for producing test inputs. Finally, these inputs are executed on both the PUT and its program variants to detect inconsistencies in their outputs. We evaluate TrickCatcher on two datasets, TrickyBugs and EvalPlus, which include 366 human-written and 151 AI-generated plausible programs with tricky bugs. TrickCatcher achieves recall, precision, and F1 scores that are 1.80{\texttimes}, 2.65{\texttimes}, and 1.66{\texttimes} those of the state-of-the-art baselines, respectively. Code and data used are available at https://github.com/RinCloud/TrickCatcher/."
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<abstract>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. TrickCatcher operates in three stages: First, it uses an LLM to generate program variants based on the program under test (PUT) and its specification. Second, it employs an LLM to construct an input generator from the specification for producing test inputs. Finally, these inputs are executed on both the PUT and its program variants to detect inconsistencies in their outputs. We evaluate TrickCatcher on two datasets, TrickyBugs and EvalPlus, which include 366 human-written and 151 AI-generated plausible programs with tricky bugs. TrickCatcher achieves recall, precision, and F1 scores that are 1.80×, 2.65×, and 1.66× those of the state-of-the-art baselines, respectively. Code and data used are available at https://github.com/RinCloud/TrickCatcher/.</abstract>
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%0 Conference Proceedings
%T LLM-Powered Test Case Generation for Detecting Bugs in Plausible Programs
%A Liu, Kaibo
%A Chen, Zhenpeng
%A Liu, Yiyang
%A Zhang, Jie M.
%A Harman, Mark
%A Han, Yudong
%A Ma, Yun
%A Dong, Yihong
%A Li, Ge
%A Huang, Gang
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F liu-etal-2025-llm
%X 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. TrickCatcher operates in three stages: First, it uses an LLM to generate program variants based on the program under test (PUT) and its specification. Second, it employs an LLM to construct an input generator from the specification for producing test inputs. Finally, these inputs are executed on both the PUT and its program variants to detect inconsistencies in their outputs. We evaluate TrickCatcher on two datasets, TrickyBugs and EvalPlus, which include 366 human-written and 151 AI-generated plausible programs with tricky bugs. TrickCatcher achieves recall, precision, and F1 scores that are 1.80×, 2.65×, and 1.66× those of the state-of-the-art baselines, respectively. Code and data used are available at https://github.com/RinCloud/TrickCatcher/.
%R 10.18653/v1/2025.acl-long.20
%U https://aclanthology.org/2025.acl-long.20/
%U https://doi.org/10.18653/v1/2025.acl-long.20
%P 430-440
Markdown (Informal)
[LLM-Powered Test Case Generation for Detecting Bugs in Plausible Programs](https://aclanthology.org/2025.acl-long.20/) (Liu et al., ACL 2025)
ACL
- Kaibo Liu, Zhenpeng Chen, Yiyang Liu, Jie M. Zhang, Mark Harman, Yudong Han, Yun Ma, Yihong Dong, Ge Li, and Gang Huang. 2025. LLM-Powered Test Case Generation for Detecting Bugs in Plausible Programs. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 430–440, Vienna, Austria. Association for Computational Linguistics.