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Efficient Inference of Mealy Machines

Automata learning is increasingly being applied to ease the testing and comparing of complex systems. We formally reconstruct an efficient algorithm for the inference of Mealy machines, prove its correctness, and show that equivalence queries are not required for nonminimal hypotheses. In fact, we are able to evade those by applying a minor optimization to the algorithm. As a corollary, standard conformance testing methods can be used directly for equivalence query approximations within this algorithm.