Machine learning testing in an ADAS case study using simulation-integrated bio-inspired search-based testing

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Helali Moghadam, MahshidMälardalens 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 (author)

Machine learning testing in an ADAS case study using simulation-integrated bio-inspired search-based testing

Article/chapterEnglish2024

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John Wiley and Sons Ltd,2024
electronicrdacarrier

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LIBRIS-ID:oai:DiVA.org:ri-65687
https://urn.kb.se/resolve?urn=urn:nbn:se:ri:diva-65687URI
https://doi.org/10.1002/smr.2591DOI
https://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-63851URI
https://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-50605URI

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Language:English
Summary in:English

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Subject category:ref swepub-contenttype
Subject category:art swepub-publicationtype

Notes

Correspondence Address: M.H. Moghadam; Smart Industrial Automation, RISE Research Institutes of Sweden, Västerås, Stora Gatan 36, 722 12, Sweden; This work has been funded by Vinnova through the ITEA3 European IVVES ( https://itea3.org/project/ivves.html ) and H2020‐ECSEL European AIDOaRT ( https://www.aidoart.eu/ ) and InSecTT ( https://www.insectt.eu/ ) projects. Furthermore, the project received partially financial support from the SMILE III project financed by Vinnova, FFI, Fordonsstrategisk forskning och innovation under the grant number: 2019‐05871.
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.

Subject headings and genre

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
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

Added entries (persons, corporate bodies, meetings, titles ...)

Borg, MarkusRISE,Humanized Autonomy, RISE Research Institutes of Sweden, Lund, Sweden(Swepub:ri)markus.borg@ri.se (author)
Saadatmand, Mehrdad,1980-RISE,Smart Industrial Automation, RISE Research Institutes of Sweden, Västerås, Sweden(Swepub:ri)MehrdadSaa@ri.se (author)
Seyed Jalaleddin, MousaviradUniversidade da Beira Interior Covilhã Portugal(Swepub:miun)sevmou2300 (author)
Bohlin, Markus,1976-Mälardalens universitet,RISE,School of Innovation, Design and Engineering Mälardalen University Västerås Sweden,Innovation och produktrealisering(Swepub:mdh)mbn05 (author)
Lisper, BjörnMälardalens universitet,Inbyggda system,School of Innovation, Design and Engineering Mälardalen University Västerås Sweden(Swepub:mdh)blr01 (author)
RISESmart Industrial Automation RISE Research Institutes of Sweden Västerås Sweden;School of Innovation, Design and Engineering Mälardalen University Västerås Sweden (creator_code:org_t)

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