Print Email Facebook Twitter Single and Multi-objective Test Cases Prioritization for Self-driving Cars in Virtual Environments Title Single and Multi-objective Test Cases Prioritization for Self-driving Cars in Virtual Environments Author Birchler, Christian (Zurich University of Applied Science (ZHAW)) Khatiri, Sajad (Zurich University of Applied Science (ZHAW)) Derakhshanfar, P. (TU Delft Software Engineering) Panichella, Sebastiano (Zurich University of Applied Science (ZHAW)) Panichella, A. (TU Delft Software Engineering) Date 2023 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 article, 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 (GA), 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. Subject Autonomous SystemsSoftware SimulationTest Case PrioritizationSelf-driving carsSearch-based Software EngineeringSoftware TestingEvolutionary computation To reference this document use: http://resolver.tudelft.nl/uuid:f0393396-0044-4cc4-8d44-68950eeaade5 DOI https://doi.org/10.1145/3533818 ISSN 1049-331X Source ACM Transactions on Software Engineering and Methodology, 32 (2), 1-30 Part of collection Institutional Repository Document type journal article Rights © 2023 Christian Birchler, Sajad Khatiri, P. Derakhshanfar, Sebastiano Panichella, A. Panichella Files PDF 3533818.pdf 1.87 MB Close viewer /islandora/object/uuid:f0393396-0044-4cc4-8d44-68950eeaade5/datastream/OBJ/view