Active learning of industrial software with data

L. Sanchez, Jan Friso Groote, Ramon Schiffelers

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review


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
StatusGepubliceerd - feb. 2019
Evenement8th IPM International Conference on Fundamentals of Software Engineering - Tehran, Iran
Duur: 1 mei 20193 mei 2019


Congres8th IPM International Conference on Fundamentals of Software Engineering


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