Mark Harman — 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 ↗

2022

Leveraging Automated Unit Tests for Unsupervised Code Translation

Baptiste Roziere, Jie Zhang, Francois Charton, Mark Harman, Gabriel Synnaeve, Guillaume Lample

ICLR 2022spotlight

With little to no parallel data available for programming languages, unsupervised methods are well-suited to source code translation. However, the majority of unsupervised machine translation approaches rely on back-translation, a method developed in the context of natural language translation and o…

Cited by 137 Source ↗PDF ↗Scholar ↗