Mutation testing is a widely used approach for measuring test-suite quality. A critical problem in mutation testing is equivalent mutant detection (EMD), i.e., determining if a mutant semantically behaves the same as the original code despite some syntactic differences. A recent study has shown that LLM-based EMD techn...
Arjun Tandon, Mehmet Fırat Dündar, Milkiyas Gebremichael Gebru et al.· Proceedings of the ACM on So...· 0 citations
LLM-based agentic systems are emerging as a new software paradigm. Modern agents are typically composed of backbone LLMs and a surrounding harness that serves as the operational software infrastructure for agent execution. As agent harnesses grow increasingly complex, agents suffer from diverse harness implementation b...
Yi-Fan Xiong, Jing-Yi Ge, Zhen-Peng Chen et al.· 0 citations
This work introduces Mubric, a mutation testing-guided approach to rubric generation, which achieves the highest overall evaluation accuracy, outperforming the strongest baseline by 7.48 percentage points.
Jiayuxuan Yang, Jie M. Zhang, Yiling Lou et al.· 0 citations
This workshop aims to define a roadmap for a world where AI Teammates and human developers build the future together, anchored by the launch of the AIDev dataset, which provides the empirical evidence needed to understand the behaviors of AI Teammates.
Hao Li, Haoxiang Zhang, Jie M. Zhang et al.· Proceedings of the 32nd ACM...· 2 citations
We are witnessing the emergence of Agentic Software Engineering (SE~3.0), where AI agents act as autonomous AI Teammates performing complex tasks such as coding, debugging, and testing. As AI Teammates generate a vast new category of digital artifacts, they introduce unique opportunities and challenges related to human...
Hao Li, Haoxiang Zhang, Jie M. Zhang et al.· Proceedings of the 32nd ACM...· 0 citations
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