Please use this identifier to cite or link to this item:
https://doi.org/10.21256/zhaw-26912| Publication type: | Article in scientific journal |
| Type of review: | Peer review (publication) |
| Title: | Cost-effective simulation-based test selection in self-driving cars software |
| Authors: | Birchler, Christian Ganz, Nicolas Khatiri, Sajad Gambi, Alessio Panichella, Sebastiano |
| et. al: | No |
| DOI: | 10.1016/j.scico.2023.102926 10.21256/zhaw-26912 |
| Published in: | Science of Computer Programming |
| Volume(Issue): | 226 |
| Issue: | 102926 |
| Issue Date: | 2023 |
| Publisher / Ed. Institution: | Elsevier |
| ISSN: | 0167-6423 1872-7964 |
| Language: | English |
| Subjects: | Self-driving car; Software simulation; Regression testing; Test case selection; Continuous integration |
| Subject (DDC): | 005: Computer programming, programs and data |
| Abstract: | Simulation 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-scissor |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/26912 |
| Fulltext version: | Published version |
| License (according to publishing contract): | CC BY 4.0: Attribution 4.0 International |
| 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 |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2023_Birchler-etal_Cost-effective-simulation-based-test-selection-self-driving-car-software.pdf | 982.75 kB | Adobe PDF |  View/Open |
Show full 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.