6 papers · 1 filter
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…
Refine After Generation: Toward Correct and Concise Patches in LLM-based Program Repair
Wenqiang Luo, Jacky Keung, Xiaoyu Shi +4
Large language models (LLMs) have advanced automatic program repair (APR) to the point where agentic systems routinely resolve real-world, repository-level issues. Yet the generate…
Understanding How Enterprises Adopt the Model Context Protocol for LLM-Driven Software Engineering
Kehui Chen, Yicheng Sun, Jacky Keung +2
Large Language Models (LLMs) are increasingly used in AI-based software engineering, but their limitations in complex task execution and multi-tool coordination have driven growing…
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…
Towards Requirements Engineering for GenAI-Enabled Software: Bridging Responsibility Gaps through Human Oversight Requirements
Zhenyu Mao, Jacky Keung, Yicheng Sun +3
Context: Responsibility gaps, long-recognized challenges in socio-technical systems where accountability becomes diffuse or ambiguous, have become increasingly pronounced in GenAI-…
R2ComSync: Improving Code-Comment Synchronization with In-Context Learning and Reranking
Zhen Yang, Hongyi Lin, Xiao Yu +5
Code-Comment Synchronization (CCS) aims to synchronize the comments with code changes in an automated fashion, thereby significantly reducing the workload of developers during soft…