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Virtualized-Fault Injection Testing: a Machine Learning Approach
KTH, School of Electrical Engineering and Computer Science (EECS), Theoretical Computer Science, TCS.
KTH, School of Electrical Engineering and Computer Science (EECS), Theoretical Computer Science, TCS.ORCID iD: 0000-0002-9706-5008
Scania CV AB, S-15187 Sodertalje, Sweden..
2018 (English)In: 2018 IEEE 11TH INTERNATIONAL CONFERENCE ON SOFTWARE TESTING, VERIFICATION AND VALIDATION (ICST), IEEE , 2018, p. 297-308Conference paper, Published paper (Refereed)
Abstract [en]

We introduce a new methodology for virtualized fault injection testing of safety critical embedded systems. This approach fully automates the key steps of test case generation, fault injection and verdict construction. We use machine learning to reverse engineer models of the system under test. We use model checking to generate test verdicts with respect to safety requirements formalised in temporal logic. We exemplify our approach by implementing a tool chain based on integrating the QEMU hardware emulator, the GNU debugger GDB and the LBTest requirements testing tool. This tool chain is then evaluated on two industrial safety critical applications from the automotive sector.

Place, publisher, year, edition, pages
IEEE , 2018. p. 297-308
Series
IEEE International Conference on Software Testing Verification and Validation, ISSN 2381-2834
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-231653DOI: 10.1109/ICST.2018.00037ISI: 000435006300027Scopus ID: 2-s2.0-85048401472ISBN: 978-1-5386-5012-7 (print)OAI: oai:DiVA.org:kth-231653DiVA, id: diva2:1245418
Conference
11th IEEE International Conference on Software Testing, Verification and Validation (ICST), APR 09-13, 2018, Vasteras, SWEDEN
Note

QC 20180905

Available from: 2018-09-05 Created: 2018-09-05 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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Khosrowjerdi, HojatMeinke, Karl

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