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 | Preproceedings of Fundamentals of Software Engineering (FSEN) 2019 |
| Editors | Hossein Hojjat, Mieke Massink |
| Place of Publication | Tehran |
| Publisher | Institute for Studies in Theoretical Physics and Mathematics (IPM), School of Mathematics |
| Pages | 51-65 |
| Number of pages | 14 |
| Publication status | Published - 2019 |
| Event | 8th IPM International Conference on Fundamentals of Software Engineering - Tehran, Iran, Islamic Republic of Duration: 1 May 2019 → 3 May 2019 |
Conference
| Conference | 8th IPM International Conference on Fundamentals of Software Engineering |
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| Country | Iran, Islamic Republic of |
| City | Tehran |
| Period | 1/05/19 → 3/05/19 |