Inferring State Models Using Feedback Directed Random Testing

State models are widely used as specification or design artifacts and form the basis of various analysis techniques. In this paper, we make use of the advances in the area of random test generation to propose a novel approach to infer state models of black-box components from their executions. We also present an implementation and the results of applying our approach on a number of examples.