Active Learning of Extended Finite State Machines

Abstract : Once they have high-level models of the behavior of software components, engineers can construct better software in less time. A key problem in practice, however, is the construction of models for existing software components, for which no or only limited documentation is available. In this talk, I will present an overview of recent work by my group — done in close collaboration with the Universities of Dortmund and Uppsala — in which we use machine learning to infer state diagram models of embedded controllers and network protocols fully automatically through observation and test, that is, through black box reverse engineering.
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Frits Vaandrager. Active Learning of Extended Finite State Machines. 24th International Conference on Testing Software and Systems (ICTSS), Nov 2012, Aalborg, Denmark. pp.5-7, ⟨10.1007/978-3-642-34691-0_2⟩. ⟨hal-01482424⟩

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