This paper addresses the problem of automatizing the production of test sequences for reactive systems. We particularly focus on two points: (1) generating relevant inputs, with respect to some knowledge about the environment in which the system is intended to run; (2) checking the correctness of the test results, according to the expected behavior of the system. We propose to use synchronous observers to express both the relevance and the correctness of the test sequences. In particular, the relevance observer is used to randomly choose inputs satisfying temporal assumptions about the environment. These assumptions may involve both Boolean and linear numerical constraints. A prototype tool, called LURETTE, has been developed and experimented, which works on observers written in the LUSTRE programming language.

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The OpenModelica Microgrid Gym (OMG) toolbox provides a transient simulation framework for local energy grids based on power electronic converters. OpenModelica is used as the backend, allowing users to set up arbitrary electric grid designs via its well-known graphical user interface in a plug-and-play fashion (Fritzson et al., 2018). Simulations can be configured using a python interface, making it easy to integrate software modules for the realization and testing of closed control loops. In addition, the OpenAI Gym interface is provided to connect data-driven reinforcement learning algorithms for investigating intelligent microgrid control approaches (Brockman et al., 2016).