@inproceedings {43357,
	title = {Evolutionary Computation and Reinforcement Learning for Cyber-physical System Design},
	journal = {2023 IEEE/ACM 45th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)},
	year = {2023},
	month = {05/2023},
	pages = {264-266},
	publisher = {IEEE},
	address = {Melbourne, Australia},
	abstract = {Cyber-physical systems (CPSs) are designed to integrate computation and physical processes through constantly interacting with the physical environment. The complexity and uncertainty of the environment often come up with unpredictable situations, which place high demands on the dynamic adaptability of CPSs. Further, as the environment evolves, the CPS needs to constantly evolve itself to adapt to the changing environment. This paper presents a research plan that aims to develop a novel framework to address CPS design challenges under uncertain environments. We propose to utilize evolutionary computation and reinforcement learning techniques to design control policies that can adapt to the dynamic changes and uncertainties of the environment. Further, novel testing and evaluation approaches that can generate test cases while adapting to dynamic changes in the system and the environment will be explored.\&nbsp;},
	keywords = {Cyber-Physical System, evolutionary computation, reinforcement learning, Uncertainty},
	doi = {10.1109/ICSE-Companion58688.2023.00071},
	url = {https://ieeexplore.ieee.org/document/10172815/http://xplorestaging.ieee.org/ielx7/10172482/10172487/10172815.pdf?arnumber=10172815},
	author = {Lu, Chengjie}
}
@phdthesis {26043,
	title = {Uncertainty-wise Cyber-Physical Systems Testing},
	volume = {PhD},
	year = {2018},
	month = {26/06/2018},
	pages = {292},
	school = {The University of Oslo},
	address = {Norway},
	abstract = {A Cyber-Physical Systems (CPS), as an integration of computing, communication, and control for making intelligent and autonomous systems, has been widely applied in various safety-critical domains, e.g., avionics and automotive. However, uncertainty is inherent in CPSs due to various reasons such as unpredictable environment under which the CPSs are operated. And, uncertainties may cause irreparable accidents once they cannot be handled properly by CPSs. Therefore, it is crucial to identify uncertainties in CPSs and test CPSs under the uncertainties, to ensure that CPSs are capable of handling the uncertainties during their actual operations, i.e., making CPSs less uncertain.Towards this direction, five contributions were made in the thesis corresponding to five papers respectively: (C1) a conceptual model, named as U-Model, for helping develop a systematic and comprehensive understanding of uncertainty in CPSs; (C2) an use case modeling methodology, named as U-RUCM, for identifying, qualifying, and, where possible, quantifying uncertainty in requirements engineering; (C3) a test modeling methodology, named as UncerTum, for supporting the construction of test ready models with the explicit representation of uncertainties in CPSs; (C4) an evolution framework, named as UncerTolve, for interactively evolving test ready models specified with UncerTum based on real operational data; and (C5) a testing framework, named as UncerTest, for testing CPSs in the presence of uncertainties in their operating environments in a cost-effective manner using model-based and search-based testing techniques.Based on our evaluations of the five contributions with the industrial CPS case studies, we observed that U-Model, as the foundation for this research, is sufficiently complete for characterizing and classifying uncertainties in CPSs. Then, the U-Model based modeling methodologies U-RUCM and UncerTum offer solutions to enable the identification and specification of uncertainties at two critical phases of a system development lifecycle: requirements engineering and testing. Furthermore, UncerTolve can successfully evolve model elements of the test ready models specified with UncerTum and calculate objective uncertainty measurements based on real operational data. Last, UncerTest managed to cost-effectively test CPSs in the presence of uncertainties and proactively identify unknown uncertainties by introducing the sources of the uncertainties into the test environments during test case execution.},
	keywords = {Cyber-Physical System, Model-based Testing, Uncertainty},
	author = {Zhang, Man}
}
@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}
}
@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}
}
