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Learning-Based Testing of Cyber-Physical Systems-of-Systems: A Platooning Study
KTH, School of Electrical Engineering and Computer Science (EECS), Theoretical Computer Science, TCS.ORCID iD: 0000-0002-9706-5008
2017 (English)In: / [ed] Reinecke P., Di Marco A., 2017, Vol. 10497, p. 135-151Conference paper, Published paper (Refereed)
Abstract [en]

Learning-based testing (LBT) is a paradigm for fully automated requirements testing that combines machine learning withmodel-checking techniques. LBT has been shown to be effective for unit and integrationtesting of safety critical components in cyber-physical systems, e.g. automotive ECU software.We consider the challenges faced, and some initial results obtained in an effort to scale up LBTto testing co-operative open cyber-physical systems-of-systems (CO-CPS).For this we focus on a case study of testing safety and performance properties of multi-vehicle platoons.

Place, publisher, year, edition, pages
2017. Vol. 10497, p. 135-151
Series
Springer Lecture Notes in Computer Science ; 10497
Keywords [en]
cyber-physical system, system-of-systems, platooning, model-based testing, learning-based testing, machine learning, requirements testing
National Category
Computer Systems
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-224184DOI: 10.1007/978-3-319-66583-2_9ISBN: 978-3-319-66582-5 (print)OAI: oai:DiVA.org:kth-224184DiVA, id: diva2:1189728
Conference
Computer Performance Engineering - 14th European Workshop, (EPEW) 2017
Projects
Electronic Component Systems for European Leadership Joint Undertaking under grant agreement No 692529 project SafeCOP.
Funder
EU, Horizon 2020, 692529
Note

QC 20180327

Available from: 2018-03-12 Created: 2018-03-12 Last updated: 2018-05-14Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
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Language
  • de-DE
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  • Other locale
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Output format
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