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Learning-based testing for safety critical automotive applications
KTH, School of Computer Science and Communication (CSC), Theoretical Computer Science, TCS.
KTH, School of Computer Science and Communication (CSC), Theoretical Computer Science, TCS.
2017 (English)In: 5th International Symposium on Model-Based Safety and Assessment, IMBSA 2017, Springer, 2017, Vol. 10437, p. 197-211Conference paper (Refereed)
Abstract [en]

Learning-based testing (LBT) is an emerging paradigm for fully automated requirements testing. This approach combines machine learning and model-checking techniques for test case generation and verdict construction. LBT is well suited to requirements testing of low-latency safety critical embedded systems, such as can be found in the automotive sector. We evaluate the feasibility and effectiveness of applying LBT to two safety critical industrial automotive applications. We also benchmark our LBT tool against an existing industrial test tool that executes manually written test cases.

Place, publisher, year, edition, pages
Springer, 2017. Vol. 10437, p. 197-211
Series
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN 0302-9743 ; 10437
Keywords [en]
Automotive software, Black-box testing, Learning-based testing, Machine learning, Model-based testing, Requirements testing, Temporal logic
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:kth:diva-216336DOI: 10.1007/978-3-319-64119-5_13Scopus ID: 2-s2.0-85029520480ISBN: 9783319641188 OAI: oai:DiVA.org:kth-216336DiVA, id: diva2:1151262
Conference
5th International Symposium on Model-Based Safety and Assessment, IMBSA 2017, Trento, Italy, 11 September 2017 through 13 September 2017
Note

QC 20171023

Available from: 2017-10-23 Created: 2017-10-23 Last updated: 2017-10-23Bibliographically approved

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Citation style
  • apa
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  • nn-NO
  • nn-NB
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  • Other locale
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Output format
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