Publications by members of the reading group on related topics.
Han, X. and Zhu, H. (Nov. 2025), MASTEST: A LLM-Based Multi-Agent System For RESTful API Tests. arXiv:2511.18038. 2025 Nov 22.
Paul, Debalina Ghosh, et al. (Oct. 2025), Investigating The Smells of LLM Generated Code. Arxiv: arXiv:2510.03029, Oct. 3rd, 2025.
Miah, T., & Zhu, H. (2024, July). User Centric Evaluation of Code Generation Tools. In Proceedings of 2024 IEEE International Conference on Artificial Intelligence Testing (IEEE AITest 2024), pp. 109-119, IEEE.
Paul, D.G , Zhu, H., and Bayley, I. (2024, July). Benchmarks and Metrics for Evaluations of Code Generation: A Critical Review, in Proceedings of 2024 IEEE International Conference on Artificial Intelligence Testing (IEEE AITest 2024), pp. 87-94, IEEE.
Debalina Ghosh Paul, Hong Zhu, Ian Bayley, ScenEval: A Benchmark for Scenario-Based Evaluation of Code Generation, in Proceedings of 2024 IEEE International Conference on Artificial Intelligence Testing (IEEE AITest 2024), pp. 55-63, IEEE.
Hong Zhu, Thi Minh Tam Tran, Aduen Benjumea, Andrew Bradley, (2023, July) A Scenario-Based Functional Testing Approach to Improving DNN Performance, in Proceedings of 2023 IEEE International Conference on Service-Oriented System Engineering (SOSE 2023), pp. 199-207, IEEE.
Zhu, H., Bayley, I., & Green, M. (2022, August). Metrics for measuring error extents of machine learning classifiers. In Proceedings of 2022 IEEE International Conference On Artificial Intelligence Testing (AITest 2022), pp. 48-55, IEEE.
Hong Zhu and Ian Bayley, Discovering Boundary Values of Feature-based Machine Learning Classifiers through Exploratory Datamorphic Testing, Journal of Systems and Software, Jan 2022 (Accepted); (Preprint version available on Arxiv at URL: http://arxiv.org/abs/2110.00330).
Hong Zhu and Ian Bayley, Exploratory Datamorphic Testing of Classification Applications, The 1st IEEE/ACM International Conference on Automation of Software Test (AST 2020), Seoul, South Korea (online), May 25-26, 2020.
Hong Zhu, Ian Bayley, Dongmei Liu and Xiaoyu Zheng, Automation of Datamorphic Testing, The Second IEEE International Conference on Artificial Intelligence Testing (AITest 2020), Oxford, UK, (online), April 13 - 16, 2020.
Hong Zhu, Dongmei Liu, Ian Bayley, Rachel Harrison and Fabio Cuzzolin, Datamorphic Testing: A Method for Testing Intelligent Applications, The 1st IEEE Int’l Conf. On Artificial Intelligence Testing (IEEE AITest 2019), San Francisco, USA, April, 4 - 9, 2019.
Hong Zhu, Software Testing as A Problem of Machine Learning: Towards a Foundation on Computational Learning Theory, (Keynote Speech), Proc. of the 2018 IEEE/ACM 13th Int’l Workshop on Automation of Software Test (AST 2018) at ICSE 2018, May 2018.