@misc {24941,
	title = {Model-Driven Testing of Cyber-Physical Systems with the Explicit Consideration of Uncertainty},
	howpublished = {MPM4CPS WG meetings in Malaga, Spain, 24-25 November 2016},
	year = {2016},
	abstract = {It is a well-recognized fact that Cyber-Physical Systems (CPSs) face both known and unknown uncertainty during their operation. This demands the development of testing techniques that must take into account known uncertainty both in the CPS and its environment with the final goal of discovering unknown uncertainty against which the CPS can be tested. Eventually, the implementation of the CPS can be improved to shield it against the newly discovered uncertainty. In this presentation, we will present some of the results that we have achieved in an EU Horizon2020 project (U-Test) in this regard. We, first, demonstrate the modeling of test ready models of CPSs together with subjective uncertainty using the Uncertainty Modeling Framework. Second, we present two uncertainty-based test case generation and four test case minimization techniques relying on the test ready models founded on Uncertainty theory and multi-objective search. Third, we present the evaluation of the test case generation and minimization techniques that was conducted to select the best strategy to be used in the practice to test a real CPS. Fourth, we present the results of testing a real CPS with the selected best strategy.},
	keywords = {Cyber-Physical System, Model-based Testing, Search-Based Testing, Uncertainty},
	author = {Ali, Shaukat}
}
@inproceedings {24633,
	title = {Search-Based Cost-Effective Test Case Selection within a Time Budget: An Empirical Study},
	journal = {Genetic and Evolutionary Computation Conference (GECCO)},
	year = {2016},
	month = {07/2016},
	pages = {1085-1092},
	address = {Denver, Colorado, USA},
	abstract = {Due to limited time and resources available for execution, test case selection always remains crucial for cost-effective testing. It is even more prominent when test cases require manual steps, e.g., operating physical equipment. Thus, test case selection must consider complicated trade-offs between cost (e.g., execution time) and effectiveness (e.g., fault detection capability). Based on our industrial collaboration within the Maritime domain, we identified a real-world and multi-objective test case selection problem in the context of robustness testing, where test case execution requires human involvement in certain steps, such as turning on the power supply to a device. The high-level goal is to select test cases for execution within a given time budget, where test engineers provide weights for a set of objectives, depending on testing requirements, standards, and regulations. To address the identified test case selection problem, we defined a fitness function including one cost measure, i.e., Time Difference (TD) and three effectiveness measures, i.e., Mean Priority (MPR), Mean Probability (MPO) and Mean Consequence (MC) that were identified together with test engineers. We further empirically evaluated eight multi-objective search algorithms, which include three weight-based search algorithms (e.g., Alternating Variable Method) and five Pareto-based search algorithms (e.g., Strength Pareto Evolutionary Algorithm 2 (SPEA2)) using two weight assignment strategies (WASs). Notice that Random Search (RS) was used as a comparison baseline. We conducted two sets of empirical evaluations: 1) Using a real world case study that was developed based on our industrial collaboration; 2) Simulating the real world case study to a larger scale to assess the scalability of the search algorithms. Results show that SPEA2 with either of the WASs performed the best for both the studies. Overall, SPEA2 managed to improve on average 32.7\%, 39\% and 33\% in terms of MPR, MPO and MC respectively as compared to RS.},
	keywords = {Search-Based Testing},
	author = {Pradhan, Dipesh and Wang, Shuai and Ali, Shaukat and Yue, Tao}
}
@misc {24942,
	title = {U-TCsGM: Generating and Minimizing Uncertainty-Based Test Cases for Cyber-Physical Systems (Tool Demo)},
	howpublished = {MPM4CPS WG meetings in Malaga, Spain, 24-25 November 2016},
	year = {2016},
	abstract = {In this tool demo, we will present the implementation of our recent research work on generating and minimizing executable test cases from the test models of a Cyber-Physical System tagged with subjective uncertainty. The algorithms are founded on uncertainty theory and NSGA-II\&mdash;the most commonly used multi-objective search algorithm. We will demonstrate the complete process starting from creating a test model with uncertainty, generating test cases, minimizing test cases, and finally executing the minimized test cases using a real CPS case study.},
	keywords = {Model-based Testing, Search-Based Testing, Uncertainty},
	author = {Zhang, Man}
}
@techreport {23476,
	title = {Understanding Uncertainty in Cyber-Physical Systems: A Conceptual Model},
	year = {2015},
	note = {This work is supported by U-Test project (Testing Cyber-Physical Systems under Uncertainty).},
	publisher = {Simula Research Laboratory},
	abstract = {Uncertainty is intrinsic in most technical systems, including Cyber-Physical Systems (CPS). Therefore, handling uncertainty in a graceful manner during the real operation of CPS is critical. Since designing, developing, and testing modern and highly sophisticated CPS is an expanding field, a step to-wards dealing with uncertainty is to identify, define, and classify uncertainties at various levels of CPS. This will help develop a systematic and comprehen-sive understanding of uncertainty. To that end, we propose a conceptual model for uncertainty specifically designed for CPS. Since the study of uncertainty in CPS development and testing is still irrelatively unexplored, this conceptual model was derived in a large part by reviewing existing work on uncertainty in other fields, including philosophy, physics, statistics, and healthcare. The con-ceptual model is mapped to the three logical levels of CPS: Application, Infra-structure, and Integration. It is captured using UML class diagrams, including relevant OCL constraints. To validate the conceptual model, we identified, clas-sified, and specified uncertainties in two distinct industrial case studies.},
	keywords = {Cyber-Physical Systems, Model-based Testing, Search-Based Testing, UML, Uncertainty},
	author = {Zhang, Man and Selic, Bran and Ali, Shaukat and Yue, Tao and Okariz, Oscar and Norgren, Roland}
}
