1. ZHAW digitalcollection
  2. School of Engineering
  3. Publikationen
  4. Publikationen School of Engineering
Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-26912
Full metadata record
DC FieldValueLanguage
dc.contributor.authorBirchler, Christian-
dc.contributor.authorGanz, Nicolas-
dc.contributor.authorKhatiri, Sajad-
dc.contributor.authorGambi, Alessio-
dc.contributor.authorPanichella, Sebastiano-
dc.date.accessioned2023-02-11T10:13:53Z-
dc.date.available2023-02-11T10:13:53Z-
dc.date.issued2023-
dc.identifier.issn0167-6423de_CH
dc.identifier.issn1872-7964de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/26912-
dc.description.abstractSimulation environments are essential for the continuous development of complex cyber-physical systems such as self-driving cars (SDCs). Previous results on simulation-based testing for SDCs have shown that many automatically generated tests do not strongly contribute to identification of SDC faults, hence do not contribute towards increasing the quality of SDCs. Because running such "uninformative" tests generally leads to a waste of computational resources and a drastic increase in the testing cost of SDCs, testers should avoid them. However, identifying "uninformative" tests before running them remains an open challenge. Hence, this paper proposes SDCScissor, a framework that leverages Machine Learning (ML) to identify SDC tests that are unlikely to detect faults in the SDC software under test, thus enabling testers to skip their execution and drastically increase the cost-effectiveness of simulation-based testing of SDCs software. Our evaluation concerning the usage of six ML models on two large datasets characterized by 22'652 tests showed that SDC-Scissor achieved a classification F1-score up to 96%. Moreover, our results show that SDC-Scissor outperformed a randomized baseline in identifying more failing tests per time unit. Webpage & Video: https://github.com/ChristianBirchler/sdc-scissorde_CH
dc.language.isoende_CH
dc.publisherElsevierde_CH
dc.relation.ispartofScience of Computer Programmingde_CH
dc.rightshttp://creativecommons.org/licenses/by/4.0/de_CH
dc.subjectSelf-driving carde_CH
dc.subjectSoftware simulationde_CH
dc.subjectRegression testingde_CH
dc.subjectTest case selectionde_CH
dc.subjectContinuous integrationde_CH
dc.subject.ddc005: Computerprogrammierung, Programme und Datende_CH
dc.titleCost-effective simulation-based test selection in self-driving cars softwarede_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Informatik (InIT)de_CH
dc.identifier.doi10.1016/j.scico.2023.102926de_CH
dc.identifier.doi10.21256/zhaw-26912-
zhaw.funding.euinfo:eu-repo/grantAgreement/EC/H2020/957254//DevOps for Complex Cyber-physical Systems/COSMOSde_CH
zhaw.issue102926de_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume226de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.webfeedSoftware Engineeringde_CH
zhaw.funding.zhawCOSMOS – DevOps for Complex Cyber-physical Systems of Systemsde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
Appears in collections:Publikationen School of Engineering

Files in This Item:
File Description SizeFormat 
2023_Birchler-etal_Cost-effective-simulation-based-test-selection-self-driving-car-software.pdf982.75 kBAdobe PDFThumbnail
View/Open
Show simple item record
Birchler, C., Ganz, N., Khatiri, S., Gambi, A., & Panichella, S. (2023). Cost-effective simulation-based test selection in self-driving cars software. Science of Computer Programming, 226(102926). https://doi.org/10.1016/j.scico.2023.102926
Birchler, C. et al. (2023) ‘Cost-effective simulation-based test selection in self-driving cars software’, Science of Computer Programming, 226(102926). Available at: https://doi.org/10.1016/j.scico.2023.102926.
C. Birchler, N. Ganz, S. Khatiri, A. Gambi, and S. Panichella, “Cost-effective simulation-based test selection in self-driving cars software,” Science of Computer Programming, vol. 226, no. 102926, 2023, doi: 10.1016/j.scico.2023.102926.
BIRCHLER, Christian, Nicolas GANZ, Sajad KHATIRI, Alessio GAMBI und Sebastiano PANICHELLA, 2023. Cost-effective simulation-based test selection in self-driving cars software. Science of Computer Programming. 2023. Bd. 226, Nr. 102926. DOI 10.1016/j.scico.2023.102926
Birchler, Christian, Nicolas Ganz, Sajad Khatiri, Alessio Gambi, and Sebastiano Panichella. 2023. “Cost-Effective Simulation-Based Test Selection in Self-Driving Cars Software.” Science of Computer Programming 226 (102926). https://doi.org/10.1016/j.scico.2023.102926.
Birchler, Christian, et al. “Cost-Effective Simulation-Based Test Selection in Self-Driving Cars Software.” Science of Computer Programming, vol. 226, no. 102926, 2023, https://doi.org/10.1016/j.scico.2023.102926.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.