Abstract
Reactive systems interact continuously with their environments. In order to test such systems, one needs to design executable environment models. Such models are intrinsically stochastic, because environment may vary a lot, and also because they are not perfectly known. We propose an environment-model based testing framework optimized for reactive systems, where Differential Evolution (de ) is used to fine-tune the environment model and to optimize test input generation. In order to evaluate the proposed method, we present a case study involving a real-world scade system from the domain of railway automation. The problem specification was proposed by our industrial partner, Siemens. Our experimental data shows that de can be used efficiently to increase the structural coverage of the System Under Test.
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Acknowledgement
This material is based upon work supported by the Siemens international Railway Automation Graduate School (iRAGS) and the scade Academic Program.
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Szenkovits, A., Gaskó, N., Jahier, E. (2016). Environment-Model Based Testing with Differential Evolution in an Industrial Setting. In: Squillero, G., Burelli, P. (eds) Applications of Evolutionary Computation. EvoApplications 2016. Lecture Notes in Computer Science(), vol 9597. Springer, Cham. https://doi.org/10.1007/978-3-319-31204-0_52
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DOI: https://doi.org/10.1007/978-3-319-31204-0_52
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