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Inferring Mealy Machines

Muzammil Shahbaz Roland Groz 1
1 VASCO [?-2015] - Validation de Systèmes, Composants et Objets logiciels [?-2015]
LIG [2007-2015] - Laboratoire d'Informatique de Grenoble [2007-2015]
Abstract : Automata learning techniques are getting significant importance for their applications in a wide variety of software engineering problems, especially in the analysis and testing of complex systems. In recent studies, a previous learning approach [1] has been extended to synthesize Mealy machine models which are specifically tailored for I/O based systems. In this paper, we discuss the inference of Mealy machines and propose improvements that reduces the worst-time learning complexity of the existing algorithm. The gain over the complexity of the proposed algorithm has also been confirmed by experimentation on a large set of finite state machines.
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https://hal.inria.fr/hal-00953587
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Submitted on : Friday, February 28, 2014 - 1:54:23 PM
Last modification on : Monday, July 20, 2020 - 4:24:02 PM

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Muzammil Shahbaz, Roland Groz. Inferring Mealy Machines. Formal Methods 2009, 2009, Eindhoven, Netherland, pp.207-222, ⟨10.1007/978-3-642-05089-3_14⟩. ⟨hal-00953587⟩

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