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
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
Volume	11761

Conference

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

Keywords

Active automata learning
SL*
Industrial Environment
Industrial environment
SL

Access to Document

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

Link to publication in Scopus

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Cite this

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title = "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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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, vol. 11761, 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

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; Vol. 11761).

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

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