Abstract
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 |
|---|---|
| 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 |
|---|---|
| Volume | 11761 |
Conference
| Conference | FSEN 2019 8th International Conference |
|---|---|
| Country | Iran, Islamic Republic of |
| City | Tehran |
| Period | 1/05/19 → 3/05/19 |
Fingerprint
Keywords
- Active automata learning
- SL*
- Industrial Environment
- Industrial environment
- SL
Cite this
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Active learning of industrial software with data. / Groote, Jan Friso; Sanchez, L.; Schiffelers, Ramon.
Fundamentals of Software Engineering - 8th International Conference, FSEN 2019, Revised Selected Papers. ed. / Hossein Hojjat; Mieke Massink. Cham : Springer, 2019. p. 95-110 (Lecture notes in computer science; Vol. 11761).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review
TY - GEN
T1 - Active learning of industrial software with data
AU - Groote, Jan Friso
AU - Sanchez, L.
AU - Schiffelers, Ramon
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Active automata learning
KW - SL
KW - Industrial Environment
KW - Industrial environment
KW - SL
UR - http://www.scopus.com/inward/record.url?scp=85076094539&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-31517-7_7
DO - 10.1007/978-3-030-31517-7_7
M3 - Conference contribution
SN - 978-3-030-31516-0
T3 - Lecture notes in computer science
SP - 95
EP - 110
BT - Fundamentals of Software Engineering - 8th International Conference, FSEN 2019, Revised Selected Papers
A2 - Hojjat, Hossein
A2 - Massink, Mieke
PB - Springer
CY - Cham
ER -