Revisiting AI and Testing Methods to Infer FSM Models of Black-Box Systems

Machine learning in the form of inference of state machine models has gained popularity in model-based testing as a means of retrieving models from software systems. By combining an old idea from machine inference with methods from automata testing in a heuristic approach, we propose a new promising direction for inferring black box systems that cannot be reset. Preliminary experiments show that this heuristic approach scales up well and outperforms more systematic approaches.