A framework for mining hybrid automata from input/output traces

Automata-based models of embedded systems are useful and attractive for many reasons: they are intuitive, precise, at a high level of abstraction, tool independent and can be simulated and analyzed. They also have the advantage of facilitating readability and system comprehension in the case of large systems. This paper proposes an approach for mining automata-based models from input/output execution traces of embedded control systems. The models mined by our approach are hybrid automata models, which capture discrete as well as continuous system behavior. Specifically this paper proposes a framework for analyzing multiple input/output traces by identifying steps like segmentation, clustering, generation of event traces, and automata inference. The framework is general enough to admit multiple techniques or future enhancements of these steps. We demonstrate the power of the framework by using some specific existing methods and tools in two case studies. Our initial results are encouraging and should spur further research in the domain.