Please use this identifier to cite or link to this item:
|Publication type:||Article in scientific journal|
|Type of review:||Peer review (publication)|
|Title:||Single and multi-objective test cases prioritization for self-driving cars in virtual environments|
|Published in:||ACM Transactions on Software Engineering and Methodology|
|Publisher / Ed. Institution:||Association for Computing Machinery|
|Subjects:||Autonomous system; Software simulation; Test case prioritization|
|Subject (DDC):||005: Computer programming, programs and data |
006: Special computer methods
|Abstract:||Testing with simulation environments helps to identify critical failing scenarios for self-driving cars (SDCs). Simulation-based tests are safer than in-field operational tests and allow detecting software defects before deployment. However, these tests are very expensive and are too many to be run frequently within limited time constraints. In this paper, we investigate test case prioritization techniques to increase the ability to detect SDC regression faults with virtual tests earlier. Our approach, called SDC-Prioritizer, prioritizes virtual tests for SDCs according to static features of the roads we designed to be used within the driving scenarios. These features can be collected without running the tests, which means that they do not require past execution results. We introduce two evolutionary approaches to prioritize the test cases using diversity metrics (black-box heuristics) computed on these static features. These two approaches, called SO-SDC-Prioritizer and MO-SDC-Prioritizer, use single-objective and multi objective genetic algorithms, respectively, to find trade-offs between executing the less expensive tests and the most diverse test cases earlier. Our empirical study conducted in the SDC domain shows that MO-SDC-Prioritizer significantly (p-value<= 0.1𝑒 − 10) improves the ability to detect safety-critical failures at the same level of execution time compared to baselines: random and greedy-based test case orderings. Besides, our study indicates that multi-objective meta-heuristics outperform single-objective approaches when prioritizing simulation-based tests for SDCs. MO-SDC-Prioritizer prioritizes test cases with a large improvement in fault detection while its overhead (up to 0.45% of the test execution cost) is negligible.|
|Related research data:||https://doi.org/10.5281/zenodo.4761405|
|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|
Files in This Item:
|2022_Birchler-etal_Test-case-prioritization-self-driving-cars.pdf||Accepted Version||947.89 kB||Adobe PDF|
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Birchler, C., Khatiri, S., Derakhshanfar, P., Panichella, S., & Panichella, A. (2022). Single and multi-objective test cases prioritization for self-driving cars in virtual environments. ACM Transactions on Software Engineering and Methodology, 37(4), 111. https://doi.org/10.1145/3533818
Birchler, C. et al. (2022) ‘Single and multi-objective test cases prioritization for self-driving cars in virtual environments’, ACM Transactions on Software Engineering and Methodology, 37(4), p. 111. Available at: https://doi.org/10.1145/3533818.
C. Birchler, S. Khatiri, P. Derakhshanfar, S. Panichella, and A. Panichella, “Single and multi-objective test cases prioritization for self-driving cars in virtual environments,” ACM Transactions on Software Engineering and Methodology, vol. 37, no. 4, p. 111, 2022, doi: 10.1145/3533818.
Birchler, Christian, et al. “Single and Multi-Objective Test Cases Prioritization for Self-Driving Cars in Virtual Environments.” ACM Transactions on Software Engineering and Methodology, vol. 37, no. 4, 2022, p. 111, https://doi.org/10.1145/3533818.
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