Learning Parameterized State Machine Model for Integration Testing

Although many of the software engineering activities can now be model-supported, the model is often missing in software development. We are interested in retrieving state- machine models from black-box software components. We assume that the details of the development process of such components (third-party software or COTS) are not available. To adequately support software engineering activities, we need to learn more complex models than simple automata. Our model is an extension of finite state machines that incorporates the notions of predicates and parameters on transitions. We argue that such a model can offer a suitable trade-off between expressivity of the model and complexity of model learning. We have been able to extend polynomial learning algorithms to extract such models in an incremental testing approach. In turn, the models can be used to derive tests or for component documentation.