@inproceedings{33c23756cce342fd8ee679bfdabbb268,
title = "Uncertainty-Wise Model Evolution with Genetic Programming",
abstract = "Model-based Testing (MBT) of a Cyber-Physical System (CPS) under uncertain environments relies on test models manually built based on testers' limited knowledge about the CPS and its operating environment, thereby requiring their continuous evolution. To this end, we propose an uncertainty-wise model evolution approach (UNCERPLORE) to systematically evolve these models with a novel exploration strategy using Genetic Programming while also incorporating CPS execution information. With a preliminary study with a CPS use case, Uncerplore manages to evolve models and explore, on average 28.6\% new uncertainties in 10 repetitions.",
keywords = "genetic programming, model evolution",
author = "Man Zhang and Shaukat Ali and Tao Yue",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 23rd IEEE International Conference on Software Quality, Reliability, and Security Companion, QRS-C 2023 ; Conference date: 22-10-2023 Through 26-10-2023",
year = "2023",
doi = "10.1109/QRS-C60940.2023.00062",
language = "英语",
series = "Proceedings - 2023 IEEE 23rd International Conference on Software Quality, Reliability, and Security Companion, QRS-C 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "843--844",
booktitle = "Proceedings - 2023 IEEE 23rd International Conference on Software Quality, Reliability, and Security Companion, QRS-C 2023",
address = "美国",
}