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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-24017
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dc.contributor.authorBirchler, Christian-
dc.contributor.authorGanz, Nicolas-
dc.contributor.authorKhatiri, Sajad-
dc.contributor.authorGambi, Alessio-
dc.contributor.authorPanichella, Sebastiano-
dc.date.accessioned2022-01-27T15:41:29Z-
dc.date.available2022-01-27T15:41:29Z-
dc.date.issued2022-
dc.identifier.isbn978-1-6654-3786-8de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/24017-
dc.description.abstractSimulation platforms facilitate the continuous development of complex systems such as self-driving cars (SDCs). However, previous results on testing SDCs using simulations have shown that most of the automatically generated tests do not strongly contribute to establishing confidence in the quality and reliability of the SDC. Therefore, those tests can be characterized as “uninformative”, and running them generally means wasting precious computational resources. We address this issue with SDC-Scissor, a framework that leverages Machine Learning to identify simulation-based tests that are unlikely to detect faults in the SDC software under test and skip them before their execution. Consequently, by filtering out those tests, SDC-Scissor reduces the number of long-running simulations to execute and drastically increases the cost-effectiveness of simulation-based testing of SDCs software. Our evaluation concerning two large datasets and around 12’000 tests showed that SDC-Scissor achieved a higher classification F1-score (between 47% and 90%) than a randomized baseline in identifying tests that lead to a fault and reduced the time spent running uninformative tests (speedup between 107% and 170%). Webpage & Video: https://github.com/ChristianBirchler/sdc-scissorde_CH
dc.language.isoende_CH
dc.publisherIEEEde_CH
dc.rightsLicence according to publishing contractde_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.subject.ddc006: Spezielle Computerverfahrende_CH
dc.titleCost-effective simulation-based test selection in self-driving cars software with SDC-Scissorde_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Informatik (InIT)de_CH
dc.identifier.doi10.1109/SANER53432.2022.00030de_CH
dc.identifier.doi10.21256/zhaw-24017-
zhaw.conference.details29th IEEE International Conference on Software Analysis, Evolution, and Reengineering, Honolulu, USA (online), 15-18 March 2022de_CH
zhaw.funding.euinfo:eu-repo/grantAgreement/EC/H2020/957254//DevOps for Complex Cyber-physical Systems/COSMOSde_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end168de_CH
zhaw.pages.start164de_CH
zhaw.publication.statusacceptedVersionde_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.title.proceedings2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)de_CH
zhaw.webfeedSoftware Systemsde_CH
zhaw.funding.zhawCOSMOS – DevOps for Complex Cyber-physical Systems of Systemsde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Birchler, C., Ganz, N., Khatiri, S., Gambi, A., & Panichella, S. (2022). Cost-effective simulation-based test selection in self-driving cars software with SDC-Scissor [Conference paper]. 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER), 164–168. https://doi.org/10.1109/SANER53432.2022.00030
Birchler, C. et al. (2022) ‘Cost-effective simulation-based test selection in self-driving cars software with SDC-Scissor’, in 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER). IEEE, pp. 164–168. Available at: https://doi.org/10.1109/SANER53432.2022.00030.
C. Birchler, N. Ganz, S. Khatiri, A. Gambi, and S. Panichella, “Cost-effective simulation-based test selection in self-driving cars software with SDC-Scissor,” in 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER), 2022, pp. 164–168. doi: 10.1109/SANER53432.2022.00030.
BIRCHLER, Christian, Nicolas GANZ, Sajad KHATIRI, Alessio GAMBI und Sebastiano PANICHELLA, 2022. Cost-effective simulation-based test selection in self-driving cars software with SDC-Scissor. In: 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER). Conference paper. IEEE. 2022. S. 164–168. ISBN 978-1-6654-3786-8
Birchler, Christian, Nicolas Ganz, Sajad Khatiri, Alessio Gambi, and Sebastiano Panichella. 2022. “Cost-Effective Simulation-Based Test Selection in Self-Driving Cars Software with SDC-Scissor.” Conference paper. In 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER), 164–68. IEEE. https://doi.org/10.1109/SANER53432.2022.00030.
Birchler, Christian, et al. “Cost-Effective Simulation-Based Test Selection in Self-Driving Cars Software with SDC-Scissor.” 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER), IEEE, 2022, pp. 164–68, https://doi.org/10.1109/SANER53432.2022.00030.


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