Biblio

Cyber-Physical Systems (CPSs) are often tested at different test levels following "X-in-the-Loop" configurations: Model-, Software- and Hardware-in-the-loop (MiL, SiL and HiL). While MiL and SiL test levels aim at testing functional requirements at the system level, the HiL test level tests functional as well as non-functional requirements by performing a real-time simulation. As testing CPS product line configurations is costly due to the fact that there are many variants to test, test cases are long, the physical layer has to be simulated and co-simulation is often necessary. It is therefore extremely important to select the appropriate test cases that cover the objectives of each level in an allowable amount of time. We propose an efficient test case selection approach adapted to the "X-in-the-Loop" test levels. Search algorithms are employed to reduce the amount of time required to test configurations of CPS product lines while achieving the test objectives of each level. We empirically evaluate three commonly-used search algorithms, i.e., Genetic Algorithm (GA), Alternating Variable Method (AVM) and Greedy (Random Search (RS) is used as a baseline) by employing two case studies with the aim of integrating the best algorithm into our approach. Results suggest that as compared with RS, our approach can reduce the costs of testing CPS product line configurations by approximately 80% while improving the overall test quality.

A self-adaptive system (SAS) can reconfigure to adapt to potentially adverse conditions that can manifest in the environment at run time. However, the SAS may not have been explicitly developed with such conditions in mind, thereby requiring additional configuration states or updates to the requirements specification for the SAS to provide assurance that it continually satisfies its requirements and delivers acceptable behavior. By discovering both adverse environmental conditions and the SAS configuration states that can mitigate those conditions at design time, an SAS can be hardened against uncertainty prior to deployment, effectively extending its lifetime. This paper introduces two search-based techniques, Ragnarok and Valkyrie, for hardening an SAS against uncertainty. Ragnarok automatically discovers adverse conditions that negatively impact an SAS by searching for environmental conditions that explicitly cause requirements violations. Valkyrie then searches for SAS configurations that improve requirements satisficement throughout execution in response to discovered adverse environmental conditions. Together, these techniques can be used to improve the design and implementation of an SAS. We apply each technique to an industry-provided remote data mirroring application that can self-reconfigure in response to unknown or adverse conditions, such as network message delays, network link failures, and sensor noise.