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Dr. Zhen Yang
楊振 PhD Graduate (2023) 2023Now Tenure-track Assistant Professor, School of Computer Science and Technology, Shandong University, Qingdao, China
Personal website ↗About
Dr. Zhen Yang is a PhD graduate in the Department of Computer Science, City University of Hong Kong.
Now Tenure-track Assistant Professor, School of Computer Science and Technology, Shandong University, Qingdao, China.
Research Interests
Publications (27)
- R2ComSync: improving code-comment synchronization with in-context learning and reranking.
- Improving anomaly detection in software logs through hybrid language modeling and reduced reliance on parser.
- An Empirical Study of Parameter-Efficient Fine-Tuning in Code Change Learning and Beyond.
- 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.
- Practitioners' Expectations on Log Anomaly Detection.
- Identifying inconsistent software defect predictions with symmetry metamorphic relation pattern.
- Exploring continual learning in code intelligence with domain-wise distilled prompts.
- Effectiveness of symmetric metamorphic relations on validating the stability of code generation LLM.
- ClassEval-T: Evaluating Large Language Models in Class-Level Code Translation.
- Can Mamba Be Better? An Experimental Evaluation of Mamba in Code Intelligence.
- A Novel Semi-Supervised Model for Generalizing Log Anomaly Detection with Limited Labeled Data.
- Improving Learning Efficacy in Basic Programming Practice Based on Three-Stage Thinking
- TerGEC: A graph enhanced contrastive approach for program termination analysis.
- SimAC: simulating agile collaboration to generate acceptance criteria in user story elaboration.
- Improving domain-specific neural code generation with few-shot meta-learning.
- Exploring and Unleashing the Power of Large Language Models in Automated Code Translation.
- Delving into Parameter-Efficient Fine-Tuning in Code Change Learning: An Empirical Study.
- Data preparation for Deep Learning based Code Smell Detection: A systematic literature review.
- Agile Requirements Engineering in a Distributed Environment: Experiences from the Software Industry During Unprecedented Global Challenges.