Active learning of industrial software with data

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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 publicationFundamentals of Software Engineering - 8th International Conference, FSEN 2019, Revised Selected Papers
EditorsHossein Hojjat, Mieke Massink
Place of PublicationCham
Number of pages16
ISBN (Electronic)978-3-030-31517-7
ISBN (Print)978-3-030-31516-0
Publication statusPublished - 2019
EventFSEN 2019 8th International Conference - Tehran, Iran, Islamic Republic of
Duration: 1 May 20193 May 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11761 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceFSEN 2019 8th International Conference
Country/TerritoryIran, Islamic Republic of


  • Active automata learning
  • SL*
  • Industrial Environment
  • Industrial environment
  • SL


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