Dr. Jacky Wai Keung

City Univeristy of Hong Kong, Hong Kong (China)

Email: [email protected]

Qualifications

2007  Ph.D., University of New South Wales

2003  B.Sc., Hons, The University of Sydney

Publications (Selected)

1. Luo, W., Keung, J. W., Yang, B., et al. (2026). When fine-tuning LLMs meets data privacy: An empirical study of federated learning in LLM-based program repair. ACM Transactions on Software Engineering and Methodology, 35(3), Article 61.
2. Yang, Z., Lin, H., Yu, X., et al. (2026). R2ComSync: Improving code-comment synchronization with in-context learning and reranking. Empirical Software Engineering, 31(4), Article 84.
3. Sun, Y., Keung, J. W., & Yu, H. K. (2026). LogMeta: A few-shot model-agnostic meta-learning framework for robust and adaptive log anomaly detection. Journal of Systems and Software, 235, Article 112781.
4. Liu, S., Keung, J., & Jin, Z. (2026). An empirical study of parameter-efficient fine-tuning in code change learning and beyond. IEEE Transactions on Software Engineering, 52(1), 3–21.
5. Sun, Y., Keung, J., & Yang, Z. (2025). Improving anomaly detection in software logs through hybrid language modeling and reduced reliance on parser. Automated Software Engineering, 33(1), Article 12.
6. Ma, X., Li, Y., Keung, J., et al. (2025). Practitioners' expectations on log anomaly detection. IEEE Transactions on Software Engineering, 51(9), 2455–2471.
7. Sun, Y., Keung, J. W., Yang, Z., et al. (2025). SemiSMAC: A semi-supervised framework for log anomaly detection with automated hyperparameter tuning. Information and Software Technology, 187, Article 107869.
8. Ma, X., He, Y., Keung, J., et al. (2025). On the value of imbalance loss functions in enhancing deep learning-based vulnerability detection. Expert Systems with Applications, 291, Article 128504.
9. Liu, S., Keung, J., Yang, Z., et al. (2025). Exploring continual learning in code intelligence with domain-wise distilled prompts. Information and Software Technology, 185, Article 107775.
10. Chan, P. Y. P., Keung, J., & Yang, Z. (2025). Identifying inconsistent software defect predictions with symmetry metamorphic relation pattern. Journal of Systems and Software, 227, Article 112449.
11. Sun, Y., Keung, J. W., Yang, Z., et al. (2025). SemiRALD: A semi-supervised hybrid language model for robust anomalous log detection. Information and Software Technology, 183, Article 107743.
12. Zhao, D., Liu, Z., Zhang, F., et al. (2025). NegCPARBP: Enhancing privacy protection for cross-project aging-related bug prediction based on negative database. IEEE Transactions on Emerging Topics in Computing, 13(2), 283–298.
13. Yu, X., Lin, G., Hu, X., et al. (2025). Less is more: Unlocking semi-supervised deep learning for vulnerability detection. ACM Transactions on Software Engineering and Methodology, 34(3), Article 62.
14. Chan, P. Y. P., Keung, J., & Yang, Z. (2025). Effectiveness of symmetric metamorphic relations on validating the stability of code generation LLM. Journal of Systems and Software, 222, Article 112330.
15. Ma, X., Zou, H., He, P., et al. (2025). On the influence of data resampling for deep learning-based log anomaly detection: Insights and recommendations. IEEE Transactions on Software Engineering, 51(1), 243–261.
16. Yu, X., Liu, L., Hu, X., et al. (2024). Fight fire with fire: How much can we trust ChatGPT on source code-related tasks? IEEE Transactions on Software Engineering, 50(12), 3435–3453.
17. Li, Y., Keung, J., Yang, Z., et al. (2024). SimAC: Simulating agile collaboration to generate acceptance criteria in user story elaboration. Automated Software Engineering, 31(2), Article 55.
18. Li, J., Li, L., Liu, J., et al. (2025). Large language model ChatGPT versus small deep learning models for self-admitted technical debt detection: Why not together? Software: Practice and Experience, 55(1), 3–28.
19. Zhang, F., Zhang, Z., Keung, J. W., et al. (2024). Data preparation for deep learning based code smell detection: A systematic literature review. Journal of Systems and Software, 216, Article 112131.
20. Liu, S., Keung, J. W., Yang, Z., et al. (2024). TerGEC: A graph enhanced contrastive approach for program termination analysis. Science of Computer Programming, 237, Article 103141.
21. Chan, P. Y. P., & Keung, J. (2024). Validating unsupervised machine learning techniques for software defect prediction with generic metamorphic testing. IEEE Access, 12, 165155–165172.

Profile Details:

https://scholars.cityu.edu.hk/en/persons/jwkeung/
https://scholar.google.com/citations?user=UpuYcQoAAAAJ&hl=en
https://www.researchgate.net/scientific-contributions/Jacky-Wai-Keung-2182905019

WOS ResearcherID: ABA-7089-2020