Efficient test-based model generation for legacy reactive systems

We present the effects of using an efficient algorithm for behavior-based model synthesis which is specifically tailored to reactive (legacy) system behaviors. Conceptual backbone is the classical automata learning procedure L*, which we adapt according to the considered application profile. The resulting learning procedure L*Meal , which directly synthesizes generalized Mealy automata from behavioral observations gathered via an automated test environment, drastically outperforms the classical learning algorithm for deterministic finite automata. Thus it marks a milestone towards opening industrial legacy systems to model-based test suite enhancement, test coverage analysis, and online testing.