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Uncertainty-Wise Cyber-Physical System test modeling

机译:不确定性明智的物理网络测试模型

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It is important that a Cyber-Physical System (CPS) 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 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 requires 18.5% more time to apply stereotypes from UUP on test ready models.
机译:重要的是,由于其不可预测的运行环境而导致其行为具有不确定性的网络物理系统(CPS),以确保其可靠的运行。确保CPS在其运行期间能够处理此类不确定性的一种方法是通过使用基于模型的测试(MBT)技术测试CPS。但是,现有的MBT技术不能在准备就绪的模型中明确捕获不确定性,即在存在环境不确定性的情况下捕获CPS的不确定预期行为。为了填补这一空白,我们提出了一个名为UncerTum的不确定性明智的测试建模框架,以创建准备就绪的模型来支持面临不确定性的CPS的MBT。 UncerTum依赖于UML概要文件的定义[UML不确定性概要文件(UUP)]和一组扩展了UML概要文件的UML模型库,用于实时和嵌入式系统的建模和分析(MARTE)。 UncerTum还受益于UML Testing Profile V.2,以支持基于标准的MBT。从以下四个方面对UncerTum进行了两个行业CPS案例研究,一个真实案例和一个开源CPS案例评估:(1)根据配置文件和模型库中定义的概念的完整性和覆盖范围CPS的潜在不确定性概念模型,即U模型和MARTE,(2)用UncerTum对不确定性进行建模所需的努力,以及(3)已开发的可用于测试的模型的正确性,通过模型执行来评估。根据评估,我们可以得出结论,我们成功地对四个案例研究中确定的所有不确定性进行了建模,这表明我们UncerTum足够完整。在建模工作方面,我们得出的结论是,平均而言,UncerTum需要多出18.5%的时间才能将UUP中的原型应用到可测试的模型上。

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