Uncertainty-Wise Model Evolution with Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Zhang:2023:QRS-C,

• author = "Man Zhang and Shaukat Ali and Tao Yue",
• booktitle = "2023 IEEE 23rd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)",
• title = "Uncertainty-Wise Model Evolution with Genetic Programming",
• year = "2023",
• pages = "843--844",
• 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 28.6percent new uncertainties in 10 repetitions.",
• keywords = "genetic algorithms, genetic programming, Uncertainty, Software quality, Software reliability, Security, Testing, model evolution",
• DOI = "doi:10.1109/QRS-C60940.2023.00062",
• ISSN = "2693-9371",
• month = oct,
• notes = "Also known as \cite{10430061}",

}

Genetic Programming entries for Man Zhang Shaukat Ali Tao Yue

Citations