Active learning of industrial software with data

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

5 Citaten (Scopus)

Samenvatting

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.
Originele taal-2Engels
TitelPreproceedings of Fundamentals of Software Engineering (FSEN) 2019
RedacteurenHossein Hojjat, Mieke Massink
Plaats van productieTehran
UitgeverijInstitute for Studies in Theoretical Physics and Mathematics (IPM), School of Mathematics
Pagina's51-65
Aantal pagina's14
StatusGepubliceerd - 2019
Evenement8th IPM International Conference on Fundamentals of Software Engineering - Tehran, Iran
Duur: 1 mei 20193 mei 2019

Congres

Congres8th IPM International Conference on Fundamentals of Software Engineering
LandIran
StadTehran
Periode1/05/193/05/19

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  • Citeer dit

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