Machine learning-based test selection for simulation-based testing of self-driving cars software

Birchler, Christian; Khatiri, Sajad; Bosshard, Bill; Gambi, Alessio; Panichella, Sebastiano

doi:10.48550/arXiv.2212.04769

Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-27365

Publication type:	Working paper – expertise – study
Title:	Machine learning-based test selection for simulation-based testing of self-driving cars software
Authors:	Birchler, Christian Khatiri, Sajad Bosshard, Bill Gambi, Alessio Panichella, Sebastiano
et. al:	No
DOI:	10.48550/arXiv.2212.04769 10.21256/zhaw-27365
Extent:	59
Issue Date:	9-Dec-2022
Publisher / Ed. Institution:	arXiv
Other identifiers:	arXiv:2212.04769
Language:	English
Subject (DDC):	005: Computer programming, programs and data 006: Special computer methods
Abstract:	Simulation platforms facilitate the development of emerging Cyber-Physical Systems (CPS) like self-driving cars (SDC) because they are more efficient and less dangerous than eld operational test cases. Despite this, thoroughly testing SDCs in simulated environments remains challenging because SDCs must be tested in a sheer amount of long-running test cases. Past results on software testing optimization have shown that not all the test cases contribute equally to establishing con dence in test subjects' quality and reliability, and the execution of \safe and uninformative" test cases can be skipped to reduce testing effort. However, this problem is only partially addressed in the context of SDC simulation platforms. In this paper, we investigate test selection strategies to increase the cost-effectiveness of simulation-based testing in the context of SDCs. We propose an approach called SDC-Scissor (SDC coSt-effeCtIve teSt SelectOR) that leverages Machine Learning (ML) strategies to identify and skip test cases that are unlikely to detect faults in SDCs before executing them.
URI:	https://digitalcollection.zhaw.ch/handle/11475/27365
License (according to publishing contract):	Licence according to publishing contract
Departement:	School of Engineering
Organisational Unit:	Institute of Computer Science (InIT)
Published as part of the ZHAW project:	COSMOS – DevOps for Complex Cyber-physical Systems of Systems
Appears in collections:	Publikationen School of Engineering

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Birchler, C., Khatiri, S., Bosshard, B., Gambi, A., & Panichella, S. (2022). Machine learning-based test selection for simulation-based testing of self-driving cars software. arXiv. https://doi.org/10.48550/arXiv.2212.04769

Birchler, C. et al. (2022) Machine learning-based test selection for simulation-based testing of self-driving cars software. arXiv. Available at: https://doi.org/10.48550/arXiv.2212.04769.

C. Birchler, S. Khatiri, B. Bosshard, A. Gambi, and S. Panichella, “Machine learning-based test selection for simulation-based testing of self-driving cars software,” arXiv, Dec. 2022. doi: 10.48550/arXiv.2212.04769.

BIRCHLER, Christian, Sajad KHATIRI, Bill BOSSHARD, Alessio GAMBI und Sebastiano PANICHELLA, 2022. Machine learning-based test selection for simulation-based testing of self-driving cars software. arXiv

Birchler, Christian, Sajad Khatiri, Bill Bosshard, Alessio Gambi, and Sebastiano Panichella. 2022. “Machine Learning-Based Test Selection for Simulation-Based Testing of Self-Driving Cars Software.” arXiv. https://doi.org/10.48550/arXiv.2212.04769.

Birchler, Christian, et al. Machine Learning-Based Test Selection for Simulation-Based Testing of Self-Driving Cars Software. arXiv, 9 Dec. 2022, https://doi.org/10.48550/arXiv.2212.04769.