Active learning of industrial software with data

Jan Friso Groote; L. Sanchez; Ramon Schiffelers

doi:10.1007/978-3-030-31517-7_7

Active learning of industrial software with data

Jan Friso Groote, L. Sanchez, Ramon Schiffelers

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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.

Original language	English
Title of host publication	Fundamentals of Software Engineering - 8th International Conference, FSEN 2019, Revised Selected Papers
Editors	Hossein Hojjat, Mieke Massink
Place of Publication	Cham
Publisher	Springer
Pages	95-110
Number of pages	16
ISBN (Electronic)	978-3-030-31517-7
ISBN (Print)	978-3-030-31516-0
DOIs	https://doi.org/10.1007/978-3-030-31517-7_7 https://doi.org/10.1007/978-3-030-31517-7_7
Publication status	Published - 2019
Event	FSEN 2019 8th International Conference - Tehran, Iran, Islamic Republic of Duration: 1 May 2019 → 3 May 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11761 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	FSEN 2019 8th International Conference
Country/Territory	Iran, Islamic Republic of
City	Tehran
Period	1/05/19 → 3/05/19

Keywords

Active automata learning
SL*
Industrial Environment
Industrial environment
SL

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Groote, J. F., Sanchez, L., & Schiffelers, R. (2019). Active learning of industrial software with data. In H. Hojjat, & M. Massink (Eds.), Fundamentals of Software Engineering - 8th International Conference, FSEN 2019, Revised Selected Papers (pp. 95-110). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11761 LNCS). Springer. https://doi.org/10.1007/978-3-030-31517-7_7, https://doi.org/10.1007/978-3-030-31517-7_7

Groote, Jan Friso ; Sanchez, L. ; Schiffelers, Ramon. / Active learning of industrial software with data. Fundamentals of Software Engineering - 8th International Conference, FSEN 2019, Revised Selected Papers. editor / Hossein Hojjat ; Mieke Massink. Cham : Springer, 2019. pp. 95-110 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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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Groote, JF, Sanchez, L & Schiffelers, R 2019, Active learning of industrial software with data. in H Hojjat & M Massink (eds), Fundamentals of Software Engineering - 8th International Conference, FSEN 2019, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11761 LNCS, Springer, Cham, pp. 95-110, FSEN 2019 8th International Conference, Tehran, Iran, Islamic Republic of, 1/05/19. https://doi.org/10.1007/978-3-030-31517-7_7, https://doi.org/10.1007/978-3-030-31517-7_7

Active learning of industrial software with data. / Groote, Jan Friso; Sanchez, L.; Schiffelers, Ramon.

Fundamentals of Software Engineering - 8th International Conference, FSEN 2019, Revised Selected Papers. ed. / Hossein Hojjat; Mieke Massink. Cham : Springer, 2019. p. 95-110 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11761 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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T1 - Active learning of industrial software with data

AU - Groote, Jan Friso

AU - Sanchez, L.

AU - Schiffelers, Ramon

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N2 - 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.

AB - 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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Groote JF, Sanchez L, Schiffelers R. Active learning of industrial software with data. In Hojjat H, Massink M, editors, Fundamentals of Software Engineering - 8th International Conference, FSEN 2019, Revised Selected Papers. Cham: Springer. 2019. p. 95-110. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-31517-7_7, https://doi.org/10.1007/978-3-030-31517-7_7

Active learning of industrial software with data

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