@inproceedings {23473,
	title = {U-Test: Evolving, Modelling and Testing Realistic Uncertain Behaviours of Cyber-Physical Systems},
	journal = {Abstract for the Testing in Practice track of International Conference on Software Testing (ICST)},
	year = {2015},
	note = {Extended abstract for industry\&nbsp;track presentation\&nbsp;},
	publisher = {IEEE},
	address = {Graz, Austria},
	abstract = {Short Abstract:The goal of the U-Test project (a recently funded project under the EU Horizon2020 program (http://ec.europa.eu/programmes/horizon2020/) is to improve the dependability of Cyber-Physical Systems (CPSs), via cost-effective, model-based and search-based testing of CPSs under unknown uncertainty. Unknown uncertainty is the state of a CPS that can only be determined at the runtime as opposed to known uncertainty that is known at the design time and outcome from uncertainty is undesirable. To achieve our goal, we will advance the current state-of-art of testing CPSs by developing a novel- solution based on sound theoretical foundation for uncertainty testing in the following steps: 1) Developing a light-weight modelling solution with rich formalism to support minimal modelling of known uncertainty with risk information; 2) Intelligently evolving known uncertainty models towards realistic and risky unknown uncertainty models (evolved models) using search algorithms (e.g., genetic algorithms mimicking natural selection); and 3) Automatically generating test cases from the evolved models to test a CPS under unknown uncertainty to ensure that the CPS continues to operate properly and possibly at a reduced quality of operation, rather than failing completely.\&nbsp;},
	author = {Ali, Shaukat and Yue, Tao}
}
