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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Shahbaz, M., Groz, R. (2009). Inferring Mealy Machines. In: Cavalcanti, A., Dams, D.R. (eds) FM 2009: Formal Methods. FM 2009. Lecture Notes in Computer Science, vol 5850. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05089-3_14
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DOI: https://doi.org/10.1007/978-3-642-05089-3_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-05088-6
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