Abstract

Models play an essential role in the design process of cyber-physical systems. They form the basis for simulation and analysis and help in identifying design problems as early as possible. However, the construction of models that comprise physical and digital behavior is challenging. Therefore, there is considerable interest in learning such hybrid behavior by means of machine learning which requires sufficient and representative training data covering the behavior of the physical system adequately. In this work, we exploit a combination of automata learning and model-based testing to generate sufficient training data fully automatically.

Experimental results on a platooning scenario show that recurrent neural networks learned with this data achieved significantly better results compared to models learned from randomly generated data. In particular, the classification error for crash detection is reduced by a factor of five and a similar F1-score is obtained with up to three orders of magnitude fewer training samples.

Original languageEnglish
Title of host publicationTesting Software and Systems
Subtitle of host publication31st IFIP WG 6.1 International Conference, ICTSS 2019, Paris, France, October 15–17, 2019, Proceedings
EditorsChristophe Gaston, Nikolai Kosmatov, Pascale Le Gall
Place of PublicationCham
PublisherSpringer International Publishing AG
Pages3-21
Number of pages19
ISBN (Print)978-3-030-31280-0
DOIs
Publication statusPublished - 2019
EventIFIP-ICTSS 2019: 31st IFIP International Conference on Testing Software and Systems - Paris, France
Duration: 15 Oct 201917 Oct 2019

Publication series

NameLecture Notes in Computer Science
Volume11812

Conference

ConferenceIFIP-ICTSS 2019
CountryFrance
CityParis
Period15/10/1917/10/19

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Keywords

  • Hybrid systems
  • Behavior modeling
  • Automata learning
  • Model-Based Testing
  • Machine learning
  • Autonomous vehicle
  • Platooning

Cite this

Aichernig, B. K., Bloem, R., Ebrahimi, M., Horn, M., Pernkopf, F., Roth, W., ... Tranninger, M. (2019). Learning a Behavior Model of Hybrid Systems Through Combining Model-Based Testing and Machine Learning. In C. Gaston, N. Kosmatov, & P. Le Gall (Eds.), Testing Software and Systems: 31st IFIP WG 6.1 International Conference, ICTSS 2019, Paris, France, October 15–17, 2019, Proceedings (pp. 3-21). (Lecture Notes in Computer Science; Vol. 11812). Cham: Springer International Publishing AG . https://doi.org/10.1007/978-3-030-31280-0_1