Author

Yi-Cheng Sun

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Preprint Oct 2026

Executable but Unfaithful: Mock Object, Stub, and Assertion Generation by LLMs and Coding Agents

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 generation of mock objects, stubs, and mock assertions has been a long-standing research goal. However, capture-and-replay approaches rely on...

Hao Ning, Jia-Hui Dong, Zhen Yang et al. · 0 citations

Conference Jul 2026

Designing Psychologically Safe AI Tutors for Students: An Emotion-Aware Post-Hoc Intervention for LLM-Assisted Learning

Large language models (LLMs) are increasingly integrated into students' learning processes, serving not only as cognitive tools for academic support but also as conversational partners that influence learners' emotional experience. However, existing LLM-based learning systems are not explicitly designed to account for...

Yi-Cheng Sun, J. Keung, Hi-Kuen Yu et al. · Annual International Compute... · 0 citations

Review Aug 2026

Refine After Generation: Toward Correct and Concise Patches in LLM-based Program Repair

This paper identifies patch verbosity as a major yet overlooked concern in LLM-based APR and proposes RECAP, a lightweight, plug-and-play adapter that attaches to existing repair frameworks after generation that achieves a substantially better size-correctness tradeoff.

Wen-Qiang Luo, J. Keung, Xiaoyu Shi et al. · 1 citation