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Conference Papers Year : 2019

Active Learning of Industrial Software with Data

Abstract

Active automata learning allows to learn software in the form of an automaton representing its behavior. The algorithm SL$$^{*}$$, as implemented in RALib, is one of few algorithms today that allows learning automata with data parameters. In this paper we investigate the suitability of SL$$^{*}$$ to learn software in an industrial environment.For this purpose we learned a number of industrial systems, with and without data. Our conclusion is that SL$$^{*}$$ appears to be very suitable for learning systems of limited size with data parameters in an industrial environment. However, as it stands, SL$$^{*}$$ is not scalable enough to deal with more complex systems. Moreover, having more data theories available will increase practical usability.
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hal-03769114 , version 1 (05-09-2022)

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Lisette Sanchez, Jan Friso Groote, Ramon Schiffelers. Active Learning of Industrial Software with Data. 8th International Conference on Fundamentals of Software Engineering (FSEN), May 2019, Tehran, Iran. pp.95-110, ⟨10.1007/978-3-030-31517-7_7⟩. ⟨hal-03769114⟩
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