2 citations · 4 across the 4 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…
EffiSkill: Agent Skill Based Automated Code Efficiency Optimization
Zimu Wang, Yuling Shi, Mengfan Li +4
Code efficiency is a fundamental aspect of software quality, yet how to harness large language models (LLMs) to optimize programs remains challenging. Prior approaches have sought…
Boosting LLMs for Mutation Generation
Bo Wang, Ming Deng, Mingda Chen +5
LLM-based mutation testing is a promising testing technology, but existing approaches typically rely on a fixed set of mutations as few-shot examples or none at all. This can resul…
YATE: The Role of Test Repair in LLM-Based Unit Test Generation
Michael Konstantinou, Renzo Degiovanni, Jie M. Zhang +2
Recent advances in automated test generation utilises language models to produce unit tests. While effective, language models tend to generate many incorrect tests with respect to…
A Comprehensive Study on Large Language Models for Mutation Testing
Bo Wang, Mingda Chen, Ming Deng +4
Large Language Models (LLMs) have recently been used to generate mutants in both research work and in industrial practice. However, there has been no comprehensive empirical study…