@article {26027,
	title = {Specifying Uncertainty in Use Case Models},
	journal = {Journal of Systems and Software},
	volume = {144},
	year = {2018},
	pages = {573-603},
	publisher = {Elsevier},
	abstract = {Context: Latent uncertainty in the context of software-intensive systems (e.g., Cyber-Physical Systems (CPSs)) demands explicit attention right from the start of development. Use case modeling{\textemdash}a commonly used method for specifying requirements in practice, should also be extended for explicitly specifying uncertainty.Objective: Since uncertainty is a common phenomenon in requirements engineering, it is best to address it explicitly by identifying, qualifying, and, where possible, quantifying uncertainty at the beginning stage. The ultimate aim, though not within the scope of this paper, was to use these use cases as the starting point to create test-ready models to support automated testing of CPSs under uncertainty.Method: We extend the Restricted Use Case Modeling (RUCM) methodology and its supporting tool to specify uncertainty as part of system requirements. Such uncertainties include those caused by insufficient domain expertise of stakeholders, disagreements among them, and known uncertainties about assumptions about the environment of the system. The extended RUCM, called U-RUCM, inherits the features of RUCM, such as automated analyses and generation of models, to mention but a few. Consequently, U-RUCM provides all the key benefits offered by RUCM (i.e., reducing ambiguities in requirements), but also, it allows specification of uncertainties with the possibilities of reasoning and refining existing ones and even uncovering unknown ones.Results: We evaluated U-RUCM with two industrial CPS case studies. After refining RUCM models (specifying initial requirements), by applying the U-RUCM methodology, we successfully identified and specified additional 306\% and 512\% (previously unknown) uncertainty requirements, as compared to the initial requirements specified in RUCM. This showed that, with U-RUCM, we were able to get a significantly better and more precise characterization of uncertainties in requirement engineering.Conclusion: Evaluation results show that U-RUCM is an effective methodology (with tool support) for dealing with uncertainty in requirements engineering. We present our experience, lessons learned, and future challenges, based on the two industrial case studies.},
	keywords = {Belief, Uncertainty, Use Case Modeling},
	doi = {10.1016/j.jss.2018.06.075},
	author = {Zhang, Man and Yue, Tao and Ali, Shaukat and Selic, Bran and Okariz, Oscar and Norgren, Roland and Intxausti, Karmele}
}
@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}
}
@techreport {24609,
	title = {An Integrated Modeling Framework to Facilitate Model-Based Testing of Cyber-Physical Systems under Uncertainty},
	year = {2016},
	note = {This work is supported by U-Test project (Testing Cyber-Physical Systems under Uncertainty).},
	publisher = {Simula Research Laboratory},
	abstract = {Undertaking innate uncertainty during the operation of Cyber-Physical Systems (CPSs) is crucial for their intact behavior. One method to handle such uncertainty is using Model-based Testing (MBT); however, existing MBT techniques do not explicitly capture uncertainty in test ready models that capture expected behavior of a CPS and its operating environment. 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 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.},
	keywords = {Uncertainty; Cyber-Physical System; UML Profile; Model-based Testing},
	author = {Zhang, Man and Ali, Shaukat and Yue, Tao and Norgren, Roland and Okariz, Oscar}
}
@techreport {24867,
	title = {Specifying Uncertainty in Use Case Models in Industrial Settings},
	year = {2016},
	publisher = {Simula Research Laboratory},
	abstract = {Latent uncertainty in the context of software-intensive systems (e.g., Cyber-Physical Systems (CPSs)) demands explicit attention right from the start of development. Use case modeling\&mdash;a commonly used method for specifying requirements in practice, should also be extended for explicitly specifying uncertainty. To this end, we extend the Restricted Use Case Modeling (RUCM) methodology and its supporting tool to specify uncertainty as part of system requirements. Such uncertainties include those caused by insufficient domain expertise of stakeholders, disagreements among them, and known uncertainties pertaining to assumptions about the environment of the system. The extended RUCM, called U-RUCM, inherits the features of RUCM, such as automated analyses and generation of models, to mention but a few. Consequently, U-RUCM provides all the key benefits offered by RUCM (i.e., reducing ambiguities in requirements), but in addition, it allows specification of uncertainties with the possibilities of reasoning and refining existing ones and even uncovering unknown ones.We evaluated U-RUCM in the context of the U-Test project (www.u-test.eu) with two industrial CPS case studies. Evaluation results showed that, with U-RUCM, we were able to get a significantly better and more precise characterization of the uncertainties involved compared to RUCM. This suggests that U-RUCM is an effective tool for dealing with uncertainty in requirements engineering. We present our experience, lessons learned, and future challenges based on the two industrial case studies.},
	keywords = {Belief, Uncertainty, Use Case Modeling},
	author = {Zhang, Man and Yue, Tao and Ali, Shaukat and Selic, Bran and Okariz, Oscar and Norgren, Roland and Intxausti, Karmele}
}
@inproceedings {24517,
	title = {Understanding Uncertainty in Cyber-Physical Systems: A Conceptual Model},
	journal = {European Conference on Modelling Foundations and Applications(ECMFA)},
	year = {2016},
	note = {This work is supported by U-Test project (Testing Cyber-Physical Systems under Uncertainty).},
	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 towards dealing with uncertainty is to identify, define, and classify uncertainties at various levels of CPS. This will help develop a systematic and comprehensive 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 conceptual model is mapped to the three logical levels of CPS: Application, Infrastructure, and Integration. It is captured using UML class diagrams, including relevant OCL constraints. To validate the conceptual model, we identified, classified, and specified uncertainties in two distinct industrial case studies},
	keywords = {Cyber-Physical Systems, Testing, Uncertainty},
	author = {Zhang, Man and Selic, Bran and Ali, Shaukat and Yue, Tao and Okariz, Oscar 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}
}
