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.
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 |
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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 | |
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 (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 11761 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | FSEN 2019 8th International Conference |
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Country/Territory | Iran, Islamic Republic of |
City | Tehran |
Period | 1/05/19 → 3/05/19 |
Keywords
- Active automata learning
- SL*
- Industrial Environment
- Industrial environment
- SL