Sökning: WFRF:(Borg Markus) >
Machine learning te...
Machine learning testing in an ADAS case study using simulation-integrated bio-inspired search-based testing
-
- Helali Moghadam, Mahshid (författare)
- Mälardalens universitet,RISE,Smart Industrial Automation RISE Research Institutes of Sweden Västerås Sweden;School of Innovation, Design and Engineering Mälardalen University Västerås Sweden,Inbyggda system,Smart Industrial Automation, RISE Research Institutes of Sweden, Västerås, Sweden
-
- Borg, Markus (författare)
- RISE,Humanized Autonomy, RISE Research Institutes of Sweden, Lund, Sweden
-
- Saadatmand, Mehrdad, 1980- (författare)
- RISE,Smart Industrial Automation, RISE Research Institutes of Sweden, Västerås, Sweden
-
visa fler...
-
- Seyed Jalaleddin, Mousavirad (författare)
- Universidade da Beira Interior Covilhã Portugal
-
- Bohlin, Markus, 1976- (författare)
- Mälardalens universitet,RISE,School of Innovation, Design and Engineering Mälardalen University Västerås Sweden,Innovation och produktrealisering
-
- Lisper, Björn (författare)
- Mälardalens universitet,Inbyggda system,School of Innovation, Design and Engineering Mälardalen University Västerås Sweden
-
visa färre...
-
(creator_code:org_t)
- John Wiley and Sons Ltd, 2024
- 2024
- Engelska.
-
Ingår i: Journal of Software. - : John Wiley and Sons Ltd. - 2047-7473 .- 2047-7481. ; :5
- Relaterad länk:
-
https://doi.org/10.1...
-
visa fler...
-
https://ri.diva-port... (primary) (Raw object)
-
https://urn.kb.se/re...
-
https://doi.org/10.1...
-
https://urn.kb.se/re...
-
https://urn.kb.se/re...
-
visa färre...
Abstract
Ämnesord
Stäng
- This paper presents an extended version of Deeper, a search-based simulation-integrated test solution that generates failure-revealing test scenarios for testing a deep neural network-based lane-keeping system. In the newly proposed version, we utilize a new set of bio-inspired search algorithms, genetic algorithm (GA), (Formula presented.) and (Formula presented.) evolution strategies (ES), and particle swarm optimization (PSO), that leverage a quality population seed and domain-specific crossover and mutation operations tailored for the presentation model used for modeling the test scenarios. In order to demonstrate the capabilities of the new test generators within Deeper, we carry out an empirical evaluation and comparison with regard to the results of five participating tools in the cyber-physical systems testing competition at SBST 2021. Our evaluation shows the newly proposed test generators in Deeper not only represent a considerable improvement on the previous version but also prove to be effective and efficient in provoking a considerable number of diverse failure-revealing test scenarios for testing an ML-driven lane-keeping system. They can trigger several failures while promoting test scenario diversity, under a limited test time budget, high target failure severity, and strict speed limit constraints.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Programvaruteknik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Software Engineering (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering (hsv//eng)
Nyckelord
- advanced driver assistance systems
- deep learning
- evolutionary computation
- lane-keeping system
- machine learning testing
- search-based testing
- Automobile drivers
- Biomimetics
- Budget control
- Deep neural networks
- Embedded systems
- Genetic algorithms
- Learning systems
- Particle swarm optimization (PSO)
- Software testing
- Case-studies
- Lane keeping
- Machine-learning
- Software Evolution
- Software process
- Test scenario
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
Hitta via bibliotek
Till lärosätets databas