人员

Dr. Wei obtained his Ph.D. degree in Computer Science from City University of Hong Kong in 2023 and his B.Eng. degree in Data and Computer Science from Sun Yat-Sen University in 2017. Before joining EdUHK, he was a post-doctoral fellow in the Faculty of Engineering, the University of Hong Kong.

His research interests primarily revolve around software engineering, with a current emphasis on the testing aspects of artificial intelligence (SE4AI & AI4SE).

For more details about his profile, please visit his personal website.

• Software Testing
• Program Analysis
• Software Management
• Deep Learning Compilation
• Blockchain Security
• AI in Education

Ten representative papers are shown here. The full list of publications can be found on his personal website and scholar pages.

• Wei, Z., Wang, H., Ashraf, I., & Chan, W. K. (2023). DeepPatch: Maintaining Deep Learning Model Programs to Retain Standard Accuracy with Substantial Robustness Improvement. ACM Transactions on Software Engineering and Methodology, 32(6), 1-49.
• Wang, H., Wei, Z., Zhou, Q., & Chan, W. K. (2024). Context-Aware Fuzzing for Robustness Enhancement of Deep Learning Models. ACM Transactions on Software Engineering and Methodology.
• Wei, Z., Lee, A. T., Lee, V. C., & Chan, W. K. (2024, July). Toward AI-facilitated Learning Cycle in Integration Course Through Pair Programming with AI Agents. In 2024 36th International Conference on Software Engineering Education and Training (CSEE&T) (pp. 1-5). IEEE.
• Wei, Z., Kiang, A., Lee, T. L., Yiu, S. M., Lee, C. S., & Lam, K. H. (2024) Introduction to Teaching and Learning with Chatbots Powered by Student-In-the-Loop Knowledge Bases. In 2024 Hong Kong Teaching Excellence Alliance (HKTEA).
• Zhou, Q., Wei, Z., Wang, H., Jiang, B., & Chan, W. K. (2024). CrossCert: A Cross-Checking Detection Approach to Patch Robustness Certification for Deep Learning Models. Proceedings of the ACM on Software Engineering, 1(FSE), 2725-2746.
• Zhou, Q., Wei, Z., Wang, H., & Chan, W. K. (2023, September). A Majority Invariant Approach to Patch Robustness Certification for Deep Learning Models. In 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE) (pp. 1790-1794). IEEE.
• Wei, Z., Wang, H., Ashraf, I., & Chan, W. K. (2022, December). Predictive mutation analysis of test case prioritization for deep neural networks. In 2022 IEEE 22nd International Conference on Software Quality, Reliability and Security (QRS) (pp. 682-693). IEEE.
• Wei, Z., Wang, H., Yang, Z., & Chan, W. K. (2022, May). SEbox4DL: A modular software engineering toolbox for deep learning models. In Proceedings of the ACM/IEEE 44th International Conference on Software Engineering (ICSE) (pp. 193-196).
• Wei, Z., & Chan, W. K. (2021, December). Fuzzing deep learning models against natural robustness with filter coverage. In 2021 IEEE 21st International Conference on Software Quality, Reliability and Security (QRS) (pp. 608-619). IEEE.
• Yang, Z., Keung, J., Yu, X., Gu, X., Wei, Z., Ma, X., & Zhang, M. (2021, May). A multi-modal transformer-based code summarization approach for smart contracts. In 2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC) (pp. 1-12). IEEE.

Selected Research Projects

• Ethereum Smart Contract Fuzz Testing for Security Vulnerability Detection
• Generative AI-based platform for enriching students' critical thinking