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Single and Multi-objective Test Cases Prioritization for Self-driving Cars in Virtual Environments

dc.contributor.authorBirchler, Christian
dc.contributor.authorKhatiri, Sajad
dc.contributor.authorDerakhshanfar, Pouria
dc.contributor.authorPanichella, Sebastiano
dc.contributor.authorPanichella, Annibale
dc.contributor.editorKoziolek, Anne
dc.contributor.editorLamprecht, Anna-Lena
dc.contributor.editorThüm, Thomas
dc.contributor.editorBurger, Erik
dc.date.accessioned2025-02-14T09:36:28Z
dc.date.available2025-02-14T09:36:28Z
dc.date.issued2025
dc.description.abstractIn this work, we propose an approach to prioritize simulation-based test cases for self-driving cars. The paper is published in ACM Transactions on Software Engineering and Methodology [Bi23]. 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.en
dc.identifier.doi10.18420/se2025-13
dc.identifier.eissn2944-7682
dc.identifier.issn2944-7682
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/45773
dc.language.isoen
dc.publisherGesellschaft für Informatik, Bonn
dc.relation.ispartofSoftware Engineering 2025
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-360
dc.subjectAutonomous Systems
dc.subjectSoftware Simulation
dc.subjectTest Case Prioritization
dc.titleSingle and Multi-objective Test Cases Prioritization for Self-driving Cars in Virtual Environmentsen
mci.conference.date22.-28. Februar 2025
mci.conference.locationKarlsruhe
mci.conference.sessiontitleScientific Programme
mci.reference.pages53

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