Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-24017
Publication type: Conference paper
Type of review: Peer review (publication)
Title: Cost-effective simulation-based test selection in self-driving cars software with SDC-Scissor
Authors: Birchler, Christian
Ganz, Nicolas
Khatiri, Sajad
Gambi, Alessio
Panichella, Sebastiano
et. al: No
DOI: 10.1109/SANER53432.2022.00030
10.21256/zhaw-24017
Proceedings: 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)
Page(s): 164
Pages to: 168
Conference details: 29th IEEE International Conference on Software Analysis, Evolution, and Reengineering, Honolulu, USA (online), 15-18 March 2022
Issue Date: 2022
Publisher / Ed. Institution: IEEE
ISBN: 978-1-6654-3786-8
Language: English
Subjects: Self-driving car; Software simulation; Regression testing; Test case selection; Continuous integration
Subject (DDC): 005: Computer programming, programs and data
006: Special computer methods
Abstract: Simulation 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-scissor
URI: https://digitalcollection.zhaw.ch/handle/11475/24017
Fulltext version: Accepted version
License (according to publishing contract): Licence according to publishing contract
Departement: School of Engineering
Organisational Unit: Institute of Applied Information Technology (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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