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Modern Cyber-Physical Systems (CPS) and Internet of Things (IoT) systems consist of both loosely and tightly interactions among various resources in IoT networks, edge servers and cloud data centers. These elements are being built atop virtualization layers and deployed in both edge and cloud infrastructures. They also deal with a lot of data through the interconnection of different types of networks and services. Therefore, several new types of uncertainties are emerging, such as data, actuation, and elasticity uncertainties. This triggers several challenges for testing uncertainty in such systems. However, there is a lack of novel ways to model and prepare the right infrastructural elements covering requirements for testing emerging uncertainties. In this paper, first we present techniques for modeling CPS/IoT Systems and their uncertainties to be tested. Second, we introduce techniques for determining and generating deployment configuration for testing in different IoT and cloud infrastructures. We illustrate our work with a real-world use case for monitoring and analysis of Base Transceiver Stations.

For developing software systems it is crucial to consider non-functional properties already in an early development stage to guarantee that the system will satisfy its non-functional requirements. Following the model-based engineering paradigm facilitates an early analysis of non-functional properties of the system being developed based on the elaborated design models. Although UML is widely used in model-based engineering, it is not suitable for model-based analysis directly due to its lack of formal semantics. Thus, current model-based analysis approaches transform UML models into formal languages dedicated for analyses purpose, which may introduce accidental complexity of implementing the required model transformations.The recently introduced fUML standard provides a formal semantics of a subset of UML enabling the execution of UML models. In this paper, we show how fUML can be utilized for analyzing UML models directly without having to transform them. We present a reusable framework for performing model-based analyses leveraging execution traces of UML models and integrating UML profiles heretofore unsupported by fUML. A case study in the performance analysis domain is used to illustrate the benefits of our framework.