Active learning of industrial software with data

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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.
Original languageEnglish
Title of host publicationFundamentals of Software Engineering - 8th International Conference, FSEN 2019, Revised Selected Papers
EditorsHossein Hojjat, Mieke Massink
Place of PublicationCham
PublisherSpringer
Pages95-110
Number of pages16
ISBN (Electronic)978-3-030-31517-7
ISBN (Print)978-3-030-31516-0
DOIs
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
Volume11761

Conference

ConferenceFSEN 2019 8th International Conference
CountryIran, Islamic Republic of
CityTehran
Period1/05/193/05/19

Fingerprint

Learning systems
Large scale systems
Problem-Based Learning

Keywords

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

Cite this

Groote, J. F., Sanchez, L., & Schiffelers, R. (2019). Active learning of industrial software with data. In H. Hojjat, & M. Massink (Eds.), Fundamentals of Software Engineering - 8th International Conference, FSEN 2019, Revised Selected Papers (pp. 95-110). (Lecture notes in computer science; Vol. 11761). Cham: Springer. https://doi.org/10.1007/978-3-030-31517-7_7
Groote, Jan Friso ; Sanchez, L. ; Schiffelers, Ramon. / Active learning of industrial software with data. Fundamentals of Software Engineering - 8th International Conference, FSEN 2019, Revised Selected Papers. editor / Hossein Hojjat ; Mieke Massink. Cham : Springer, 2019. pp. 95-110 (Lecture notes in computer science).
@inproceedings{12ec70a0953f43fbb383d6e21ee423e6,
title = "Active learning of industrial software with data",
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.",
keywords = "Active automata learning, SL*, Industrial Environment, Industrial environment, SL",
author = "Groote, {Jan Friso} and L. Sanchez and Ramon Schiffelers",
year = "2019",
doi = "10.1007/978-3-030-31517-7_7",
language = "English",
isbn = "978-3-030-31516-0",
series = "Lecture notes in computer science",
publisher = "Springer",
pages = "95--110",
editor = "Hossein Hojjat and Massink, {Mieke }",
booktitle = "Fundamentals of Software Engineering - 8th International Conference, FSEN 2019, Revised Selected Papers",
address = "Germany",

}

Groote, JF, Sanchez, L & Schiffelers, R 2019, Active learning of industrial software with data. in H Hojjat & M Massink (eds), Fundamentals of Software Engineering - 8th International Conference, FSEN 2019, Revised Selected Papers. Lecture notes in computer science, vol. 11761, Springer, Cham, pp. 95-110, FSEN 2019 8th International Conference, Tehran, Iran, Islamic Republic of, 1/05/19. https://doi.org/10.1007/978-3-030-31517-7_7

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 proceedingConference contributionAcademicpeer-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 -

Groote JF, Sanchez L, Schiffelers R. Active learning of industrial software with data. In Hojjat H, Massink M, editors, Fundamentals of Software Engineering - 8th International Conference, FSEN 2019, Revised Selected Papers. Cham: Springer. 2019. p. 95-110. (Lecture notes in computer science). https://doi.org/10.1007/978-3-030-31517-7_7