Active learning of industrial software with data

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

4 Citations (Scopus)

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 languageEnglish
Title of host publicationPreproceedings of Fundamentals of Software Engineering (FSEN) 2019
EditorsHossein Hojjat, Mieke Massink
Place of PublicationTehran
PublisherInstitute for Studies in Theoretical Physics and Mathematics (IPM), School of Mathematics
Pages51-65
Number of pages14
Publication statusPublished - 2019
Event8th IPM International Conference on Fundamentals of Software Engineering - Tehran, Iran, Islamic Republic of
Duration: 1 May 20193 May 2019

Conference

Conference8th IPM International Conference on Fundamentals of Software Engineering
CountryIran, Islamic Republic of
CityTehran
Period1/05/193/05/19

Fingerprint

Learning systems
Large scale systems
Problem-Based Learning

Cite this

Sanchez, L., Groote, J. F., & Schiffelers, R. (2019). Active learning of industrial software with data. In H. Hojjat, & M. Massink (Eds.), Preproceedings of Fundamentals of Software Engineering (FSEN) 2019 (pp. 51-65). Tehran: Institute for Studies in Theoretical Physics and Mathematics (IPM), School of Mathematics.
Sanchez, L. ; Groote, Jan Friso ; Schiffelers, Ramon. / Active learning of industrial software with data. Preproceedings of Fundamentals of Software Engineering (FSEN) 2019. editor / Hossein Hojjat ; Mieke Massink. Tehran : Institute for Studies in Theoretical Physics and Mathematics (IPM), School of Mathematics, 2019. pp. 51-65
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Sanchez, L, Groote, JF & Schiffelers, R 2019, Active learning of industrial software with data. in H Hojjat & M Massink (eds), Preproceedings of Fundamentals of Software Engineering (FSEN) 2019. Institute for Studies in Theoretical Physics and Mathematics (IPM), School of Mathematics, Tehran, pp. 51-65, 8th IPM International Conference on Fundamentals of Software Engineering, Tehran, Iran, Islamic Republic of, 1/05/19.

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

Preproceedings of Fundamentals of Software Engineering (FSEN) 2019. ed. / Hossein Hojjat; Mieke Massink. Tehran : Institute for Studies in Theoretical Physics and Mathematics (IPM), School of Mathematics, 2019. p. 51-65.

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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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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Sanchez L, Groote JF, Schiffelers R. Active learning of industrial software with data. In Hojjat H, Massink M, editors, Preproceedings of Fundamentals of Software Engineering (FSEN) 2019. Tehran: Institute for Studies in Theoretical Physics and Mathematics (IPM), School of Mathematics. 2019. p. 51-65