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Models play an essential role in the design process of cyber-physicalsystems. They form the basis for simulation and analysis and help inidentifying design problems as early as possible. However, the construction ofmodels that comprise physical and digital behavior is challenging. Therefore,there is considerable interest in learning such hybrid behavior by means ofmachine learning which requires sufficient and representative training datacovering the behavior of the physical system adequately. In this work, weexploit a combination of automata learning and model-based testing to generatesufficient training data fully automatically.  Experimental results on a platooning scenario show that recurrent neuralnetworks learned with this data achieved significantly better results comparedto models learned from randomly generated data. In particular, theclassification error for crash detection is reduced by a factor of five and asimilar F1-score is obtained with up to three orders of magnitude fewertraining samples.

Learning a Behavior Model of Hybrid Systems Through Combining Model-Based Testing and Machine Learning (Full Version)