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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 (print)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: 2019-03-22Bibliographically approved
In thesis
1. Learning-based Testing for Automotive Embedded Systems: A requirements modeling and Fault injection study
Open this publication in new window or tab >>Learning-based Testing for Automotive Embedded Systems: A requirements modeling and Fault injection study
2019 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis concerns applications of learning-based testing (LBT) in the automotive domain. In this domain, LBT is an attractive testing solution, since it offers a highly automated technology to conduct safety critical requirements testing based on machine learning. Furthermore, as a black-box testing technique, LBT can manage the complexity of modern automotive software applications such as advanced driver assistance systems. Within the automotive domain, three relevant software testing questions for LBT are studied namely: effectiveness of requirements modeling, learning efficiency and error discovery capabilities.

Besides traditional requirements testing, this thesis also considers fault injection testing starting from the perspective of automotive safety standards, such as ISO26262. For fault injection testing, a new methodology is developed based on the integration of LBT technologies with virtualized hardware emulation to implement both test case generation and fault injection. This represents a novel application of machine learning to fault injection testing. Our approach is flexible, non-intrusive and highly automated. It can therefore provide a complement to traditional fault injection methodologies such as hardware-based fault injection.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2019. p. 76
Series
TRITA-EECS-AVL ; 2019:19
Keywords
Machine learning, fault injection, requirements testing, embedded systems, model checking, automotive software, requirements modeling
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:kth:diva-247506 (URN)978-91-7873-121-3 (ISBN)
Presentation
2019-05-17, E2, KTH Campus, Main building, Stockholm, 15:50 (English)
Opponent
Supervisors
Note

QC 20190325

Available from: 2019-03-28 Created: 2019-03-22 Last updated: 2019-04-01Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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Output format
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