%0 Conference Proceedings
%T Active Learning of Industrial Software with Data
%+ Eindhoven University of Technology [Eindhoven] (TU/e)
%+ ASML [VELDHOVEN] (ASML)
%A Sanchez, Lisette
%A Groote, Jan, Friso
%A Schiffelers, Ramon
%Z Part 3: Learning
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 8th International Conference on Fundamentals of Software Engineering (FSEN)
%C Tehran, Iran
%Y Hossein Hojjat
%Y Mieke Massink
%I Springer International Publishing
%3 Fundamentals of Software Engineering
%V LNCS-11761
%P 95-110
%8 2019-05-01
%D 2019
%R 10.1007/978-3-030-31517-7_7
%K Active automata learning
%K SL$$^{*}$$
%K Industrial environment
%Z Computer Science [cs]Conference papers
%X 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.
%G English
%Z TC 2
%Z WG 2.2
%2 https://inria.hal.science/hal-03769114/document
%2 https://inria.hal.science/hal-03769114/file/490001_1_En_7_Chapter.pdf
%L hal-03769114
%U https://inria.hal.science/hal-03769114
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-TC2
%~ IFIP-WG2-2
%~ IFIP-FSEN
%~ IFIP-LNCS-11761