1 citations · 1 across the 3 of their papers we have counts for
5 papers
Executable but Unfaithful: Mock Object, Stub, and Assertion Generation by LLMs and Coding Agents
Hao Ning, Jiahui Dong, Zhen Yang +7
Mocking is a foundational practice in unit testing, enabling developers to isolate the code under test by replacing real dependencies with simulated alternatives. Automating the ge…
DebugRepair: Enhancing LLM-Based Automated Program Repair via Self-Directed Debugging
Linhao Wu, Yifei Pei, Zhen Yang +9
Automated Program Repair (APR) has benefited from the code understanding and generation capabilities of Large Language Models (LLMs). Existing feedback-based APR methods iterativel…
Fixturize: Bridging the Fixture Gap in Test Generation
Chengyi Wang, Pengyu Xue, Zhen Yang +8
Current Large Language Models (LLMs) have advanced automated unit test generation but face a critical limitation: they often neglect to construct the necessary test fixtures, which…
TransLibEval: Demystify Large Language Models' Capability in Third-party Library-targeted Code Translation
Pengyu Xue, Kunwu Zheng, Zhen Yang +11
In recent years, Large Language Models (LLMs) have been widely studied in the code translation field on the method, class, and even repository levels. However, most of these benchm…
ClassEval-T: Evaluating Large Language Models in Class-Level Code Translation
Pengyu Xue, Linhao Wu, Zhen Yang +9
In recent years, Large Language Models (LLMs) have dramatically advanced the performance of automated code translation, making their computational accuracy score reach up to over 8…