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Learning-based Testing for Automotive Embedded Systems : A requirements modeling and Fault injection study
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- Khosrowjerdi, Hojat, 1984- (author)
- KTH,Teoretisk datalogi, TCS
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- Meinke, Karl, 1961- (thesis advisor)
- KTH,Teoretisk datalogi, TCS
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- Gurov, Dilian, 1964- (thesis advisor)
- KTH,Teoretisk datalogi, TCS
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- (creator_code:org_t)
- ISBN 9789178731213
- Stockholm : KTH Royal Institute of Technology, 2019
- English 76 s.
- Series: TRITA-EECS-AVL ; 2019:19
- Licentiate thesis (other academic/artistic)
Abstract
Subject headings
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- 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.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Keyword
- Machine learning
- fault injection
- requirements testing
- embedded systems
- model checking
- automotive software
- requirements modeling
- Computer Science
- Datalogi
Publication and Content Type
- vet (subject category)
- lic (subject category)
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