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Learning-based test...
Learning-based testing for safety critical automotive applications
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- Khosrowjerdi, Hojat (author)
- KTH,Teoretisk datalogi, TCS
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- Meinke, Karl (author)
- KTH,Teoretisk datalogi, TCS
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Rasmusson, A. (author)
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(creator_code:org_t)
- 2017-08-02
- 2017
- English.
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In: 5th International Symposium on Model-Based Safety and Assessment, IMBSA 2017. - Cham : Springer. - 9783319641188 ; , s. 197-211
- Related links:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Subject headings
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- 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.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Datorsystem (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Computer Systems (hsv//eng)
Keyword
- Automotive software
- Black-box testing
- Learning-based testing
- Machine learning
- Model-based testing
- Requirements testing
- Temporal logic
Publication and Content Type
- ref (subject category)
- kon (subject category)
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