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Learning-Based testing of cyber-physical systems-of-systems: A platooning study
KTH, School of Computer Science and Communication (CSC), Theoretical Computer Science, TCS.ORCID iD: 0000-0002-9706-5008
2017 (English)In: 14th European Workshop on Computer Performance Engineering, EPEW 2017, Springer, 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 with model-checking techniques. LBT has been shown to be effective for unit and integration testing 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 LBT to 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
Springer, 2017. Vol. 10497, p. 135-151
Series
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN 0302-9743 ; 10497
Keyword [en]
Cyber-physical system, Learning-based testing, Machine learning, Model-based testing, Platooning, Requirements testing, System-of-systems
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-216358DOI: 10.1007/978-3-319-66583-2_9Scopus ID: 2-s2.0-85029453120ISBN: 9783319665825 (print)OAI: oai:DiVA.org:kth-216358DiVA, id: diva2:1150803
Conference
14th European Workshop on Computer Performance Engineering, EPEW 2017, Berlin, Germany, 7 September 2017 through 8 September 2017
Funder
VINNOVA, 2013-05608 Virtues
Note

QC 20171020

Available from: 2017-10-20 Created: 2017-10-20 Last updated: 2018-04-12Bibliographically approved

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CiteExportLink to record
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