  Models play an essential role in the design process of cyber-physical
systems. They form the basis for simulation and analysis and help in
identifying design problems as early as possible. However, the construction of
models that comprise physical and digital behavior is challenging. Therefore,
there is considerable interest in learning such hybrid behavior by means of
machine learning which requires sufficient and representative training data
covering the behavior of the physical system adequately. In this work, we
exploit a combination of automata learning and model-based testing to generate
sufficient training data fully automatically.
  Experimental results on a platooning scenario show that recurrent neural
networks learned with this data achieved significantly better results compared
to models learned from randomly generated data. In particular, the
classification error for crash detection is reduced by a factor of five and a
similar F1-score is obtained with up to three orders of magnitude fewer
training samples.
