@article {25456,
	title = {Uncertainty-Wise Cyber-Physical System Test Modeling},
	journal = {Software \& Systems Modeling},
	year = {2017},
	publisher = { Springer},
	abstract = {It is important that a Cyber-Physical System (CPS) deals with uncertainty in its behavior caused by its unpredictable operating environment, to ensure its reliable operation. One method to ensure that the CPS will handle such uncertainty during its operation is by testing the CPS with Model-based Testing (MBT) techniques. However, existing MBT techniques do not explicitly capture uncertainty in test ready models i.e., capturing the uncertain expected behavior of a CPS in the presence of environment uncertainty. To fill this gap, we present an Uncertainty-Wise test-modeling framework, named as Uncertum, to create test ready models to support MBT of CPSs facing uncertainty. Uncertum relies on the definition of a UML profile (the UML Uncertainty Profile (UUP)) and a set of UML model libraries extending the UML profile for Modeling and Analysis of Real-Time and Embedded Systems (MARTE). Uncertum also benefits from the UML Testing Profile (UTP) V.2 to support standard-based MBT. Uncertum was evaluated with two industrial CPS case studies, one real-world case study, and one open source CPS case study from the following four perspectives: 1) Completeness and Coverage of the profiles and model libraries in terms of concepts defined in their underlying uncertainty conceptual model for CPSs (i.e., U-Model and MARTE, 2) Effort required to model uncertainty with Uncertum, and 3) Correctness of the developed test ready models, which was assessed via model execution. Based on the evaluation, we can conclude that we were successful in modeling all the uncertainties identified in the four case studies, which gives us an indication that Uncertum is sufficiently complete. In terms of modeling effort, we concluded that on average Uncertum requires18.5\% more time to apply stereotypes from UUP on test ready models.},
	keywords = {Cyber-Physical System; UML, Model-based Testing, Uncertainty},
	issn = {1619-1374},
	doi = {10.1007/s10270-017-0609-6},
	author = {Zhang, Man and Ali, Shaukat and Yue, Tao and Norgren, Roland and Okariz, Oscar}
}
@article {25187,
	title = {Uncertainty-Wise Evolution of Test Ready Models},
	journal = {Information and Software Technology (IST)},
	volume = {87},
	year = {2017},
	pages = {140-159},
	publisher = {Elsevier},
	abstract = {Context: Cyber-Physical Systems (CPSs), when deployed for operation, are inherently prone to uncertainty. Considering their applications in critical domains (e.g., healthcare), it is important that such CPSs are tested sufficiently, with the explicit consideration of uncertainty. Model-based testing (MBT) involves creating test ready models capturing the expected behavior of a CPS and its operating environment. These test ready models are then used for generating executable test cases. It is, therefore, necessary to develop methods that can continuously evolve, based on real operational data collected during the operation of CPSs, test ready models and uncertainty captured in them, all together termed as Belief Test Ready Models (BMs)Objective: Our objective is to propose a model evolution framework that can interactively improve the quality of BMs, based on operational data. Such BMs are developed by one or more test modelers (belief agents) with their assumptions about the expected behavior of a CPS, its expected physical environment, and potential future deployments. Thus, these models explicitly contain subjective uncertainty of the test modelers.Method: We propose a framework (named as UncerTolve) for interactively evolving BMs (specified with extended UML notations) of CPSs with subjective uncertainty developed by test modelers. The key inputs of UncerTolve include initial BMs of CPSs with known subjective uncertainty and real data collected from the operation of CPSs. UncerTolve has three key features: 1) Validating the syntactic correctness and conformance of BMs against real operational data via model execution, 2) Evolving objective uncertainty measurements of BMs via model execution, and 3) Evolving state invariants (modeling test oracles) and guards of transitions (modeling constraints for test data generation) of BMs with a machine learning technique.Results: As a proof-of-concept, we evaluated UncerTolve with one industrial CPS case study, i.e., GeoSports from the healthcare domain. Using UncerTolve, we managed to evolve 51\% of belief elements, 18\% of states, and 21\% of transitions as compared to the initial BM developed in an industrial setting.Conclusion: UncerTolve can successfully evolve model elements of the initial BM, in addition to objective uncertainty measurements using real operational data. The evolved model can be used to generate additional test cases covering evolved model elements and objective uncertainty. These additional test cases can be used to test the current and future deployments of a CPS to ensure that it will handle uncertainty gracefully during its operations.\&nbsp;\&nbsp;\&nbsp;\&nbsp;\&nbsp;},
	keywords = {Belief Model, Belief Test Ready Model, Model Evolution, Model-based Testing, Uncertainty},
	issn = {0950-5849},
	doi = {10.1016/j.infsof.2017.03.003},
	author = {Zhang, Man and Ali, Shaukat and Yue, Tao and Norgren, Roland}
}
@techreport {24660,
	title = {Interactively Evolving Test Ready Models with Uncertainty Developed for Testing Cyber-Physical Systems},
	year = {2016},
	note = {This work is supported by U-Test project (Testing Cyber-Physical Systems under Uncertainty).},
	publisher = {Simula Research Laboratory},
	abstract = {Context: Cyber-Physical Systems (CPSs), when deployed for operation, are inherently prone to uncertainty. Considering their applications in critical domains (e.g., healthcare), it is important that such CPSs are tested sufficiently, with the explicit consideration of uncertainty. Model-based testing (MBT) involves creating test ready models capturing the expected behavior of a CPS and its operating environment. These test ready models are then used for generating executable test cases. It is, therefore, necessary to develop methods that can continuously evolve, based on real operational data collected during the operation of CPSs, test ready models and uncertainty captured in them, all together termed as Belief Test Ready Models (BMs)Objective: Our objective is to propose a model evolution framework that can interactively improve the quality of BMs, based on operational data. Such BMs are developed by one or more test modelers (belief agents) with their assumptions about the expected behavior of a CPS, its expected physical environment, and potential future deployments. Thus, these models explicitly contain subjective uncertainty of the test modelers.Method: We propose a framework (named as UncerTolve) for interactively evolving BMs (specified with extended UML notations) of CPSs with subjective uncertainty developed by test modelers. The key inputs of UncerTolve include initial BMs of CPSs with known subjective uncertainty and real data collected from the operation of CPSs. UncerTolve has three key features: 1) Validating the syntactic correctness and conformance of BMs against real operational data via model execution, 2) Evolving objective uncertainty measurements of BMs via model execution, and 3) Evolving state invariants (modeling test oracles) and guards of transitions (modeling constraints for test data generation) of BMs with a machine learning technique.Results: As a proof-of-concept, we evaluated UncerTolve with one industrial CPS case study, i.e., GeoSports from the healthcare domain. Using UncerTolve, we managed to evolve 51\% of belief elements, 18\% of states, and 21\% of transitions as compared to the initial BM developed in an industrial setting.Conclusion: UncerTolve can successfully evolve model elements of the initial BM, in addition to objective uncertainty measurements using real operational data. The evolved model can be used to generate additional test cases covering evolved model elements and objective uncertainty. These additional test cases can be used to test the current and future deployments of a CPS to ensure that it will handle uncertainty gracefully during its operations.\&nbsp;\&nbsp;\&nbsp;\&nbsp;\&nbsp;},
	keywords = {Belief State Machine, Cyber-Physical System, Model Evolution, Model-based Testing, Uncertainty},
	author = {Zhang, Man and Ali, Shaukat and Yue, Tao and Norgren, Roland}
}
@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}
}
