People › Yicheng Sun
About
Yicheng Sun is a PhD Student in the Department of Computer Science, City University of Hong Kong.
Research Interests
Publications (28)
- Where Do Expectations Diverge? an Empirical Analysis of Software Engineering Graduate Preparedness in Industry.
- Understanding How Enterprises Adopt the Model Context Protocol for LLM-Driven Software Engineering.
- Refine After Generation: Toward Correct and Concise Patches in LLM-based Program Repair.
- R2ComSync: improving code-comment synchronization with in-context learning and reranking.
- Making Sense of Scams: Understanding Scam Conversations Through Multi-Level Alignment.
- LogMeta: A few-shot model-agnostic meta-learning framework for robust and adaptive log anomaly detection.
- Industrial log analysis revisited: A task-oriented evaluation of parsing and anomaly detection under real-world constraints.
- Improving anomaly detection in software logs through hybrid language modeling and reduced reliance on parser.
- HGARena: Budgeted Test-Driven Multi-Agent Repository Issue Resolution with Heterogeneous Graph-Augmented Retrieval.
- Designing Psychologically Safe AI Tutors for Students: An Emotion-Aware Post-Hoc Intervention for LLM-Assisted Learning.
- At the point of risk: Can AI-assisted decision support reduce digital fraud vulnerability across age groups and scam types?
- Artifact-Constrained Agentic Testing for Black-Box System Testing under Actuarial and Regulatory Constraints.
- Fostering Critical Thinking by Integrating Case-Based Learning and Generative AI in a Closed-Loop Reflective Learning Environment
- Building Bridges to Student Growth: An LLM-Powered Feedback Generation System for Holistic Development
- Understanding Industrial Log Analysis: A Multi-Dataset Evaluation of Parsing and Anomaly Detection.
- Towards Requirements Engineering for GenAI-Enabled Software: Bridging Responsibility Gaps through Human Oversight Requirements.
- Towards Lightweight LLM Software Solutions for InsurTech: A Framework for Scalable Question Answering Systems.
- StuLAC: An Adaptive LLM-Driven Framework for Scalable Student Feedback Analysis in Software-Driven Educational Systems.
- SemiSMAC: A semi-supervised framework for log anomaly detection with automated hyperparameter tuning.
- SemiRALD: A semi-supervised hybrid language model for robust Anomalous Log Detection.