Machine learning-based test selection for simulation-based testing of self-driving cars software

Simulation platforms facilitate the development of emerging Cyber-Physical Systems (CPS) like self-driving cars (SDC) because they are more efficient and less dangerous than field operational test cases. Despite this, thoroughly testing SDCs in simulated environments remains challenging because SDCs...

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Veröffentlicht in:	Empirical software engineering : an international journal Jg. 28; H. 3; S. 71
Hauptverfasser:	Birchler, Christian, Khatiri, Sajad, Bosshard, Bill, Gambi, Alessio, Panichella, Sebastiano
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.06.2023 Springer Nature B.V
Schlagworte:	Autonomous cars Autonomous vehicles Compilers Computer Science Context Cost analysis Cyber-physical systems Effectiveness Fault detection Interpreters Machine learning Optimization Platforms Programming Languages Simulation Software Engineering/Programming and Operating Systems Software testing Regression testing Test case selection Self-driving cars Industrial integration Software simulation
ISSN:	1382-3256, 1573-7616
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Abstract	Simulation platforms facilitate the development of emerging Cyber-Physical Systems (CPS) like self-driving cars (SDC) because they are more efficient and less dangerous than field operational test cases. Despite this, thoroughly testing SDCs in simulated environments remains challenging because SDCs must be tested in a sheer amount of long-running test cases. Past results on software testing optimization have shown that not all the test cases contribute equally to establishing confidence in test subjects’ quality and reliability, and the execution of “safe and uninformative” test cases can be skipped to reduce testing effort. However, this problem is only partially addressed in the context of SDC simulation platforms. In this paper, we investigate test selection strategies to increase the cost-effectiveness of simulation-based testing in the context of SDCs. We propose an approach called SDC-Scissor ( SDC co S t-effe C t I ve te S t S elect OR ) that leverages Machine Learning (ML) strategies to identify and skip test cases that are unlikely to detect faults in SDCs before executing them. Our evaluation shows that SDC-Scissor outperforms the baselines. With the Logistic model, we achieve an accuracy of 70%, a precision of 65%, and a recall of 80% in selecting tests leading to a fault and improved testing cost-effectiveness. Specifically, SDC-Scissor avoided the execution of 50% of unnecessary tests as well as outperformed two baseline strategies. Complementary to existing work, we also integrated SDC-Scissor into the context of an industrial organization in the automotive domain to demonstrate how it can be used in industrial settings.
AbstractList	Simulation platforms facilitate the development of emerging Cyber-Physical Systems (CPS) like self-driving cars (SDC) because they are more efficient and less dangerous than field operational test cases. Despite this, thoroughly testing SDCs in simulated environments remains challenging because SDCs must be tested in a sheer amount of long-running test cases. Past results on software testing optimization have shown that not all the test cases contribute equally to establishing confidence in test subjects’ quality and reliability, and the execution of “safe and uninformative” test cases can be skipped to reduce testing effort. However, this problem is only partially addressed in the context of SDC simulation platforms. In this paper, we investigate test selection strategies to increase the cost-effectiveness of simulation-based testing in the context of SDCs. We propose an approach called SDC-Scissor ( SDC co S t-effe C t I ve te S t S elect OR ) that leverages Machine Learning (ML) strategies to identify and skip test cases that are unlikely to detect faults in SDCs before executing them. Our evaluation shows that SDC-Scissor outperforms the baselines. With the Logistic model, we achieve an accuracy of 70%, a precision of 65%, and a recall of 80% in selecting tests leading to a fault and improved testing cost-effectiveness. Specifically, SDC-Scissor avoided the execution of 50% of unnecessary tests as well as outperformed two baseline strategies. Complementary to existing work, we also integrated SDC-Scissor into the context of an industrial organization in the automotive domain to demonstrate how it can be used in industrial settings. Simulation platforms facilitate the development of emerging Cyber-Physical Systems (CPS) like self-driving cars (SDC) because they are more efficient and less dangerous than field operational test cases. Despite this, thoroughly testing SDCs in simulated environments remains challenging because SDCs must be tested in a sheer amount of long-running test cases. Past results on software testing optimization have shown that not all the test cases contribute equally to establishing confidence in test subjects’ quality and reliability, and the execution of “safe and uninformative” test cases can be skipped to reduce testing effort. However, this problem is only partially addressed in the context of SDC simulation platforms. In this paper, we investigate test selection strategies to increase the cost-effectiveness of simulation-based testing in the context of SDCs. We propose an approach called SDC-Scissor (SDC coS t-effeC tI ve teS t S electOR) that leverages Machine Learning (ML) strategies to identify and skip test cases that are unlikely to detect faults in SDCs before executing them. Our evaluation shows that SDC-Scissor outperforms the baselines. With the Logistic model, we achieve an accuracy of 70%, a precision of 65%, and a recall of 80% in selecting tests leading to a fault and improved testing cost-effectiveness. Specifically, SDC-Scissor avoided the execution of 50% of unnecessary tests as well as outperformed two baseline strategies. Complementary to existing work, we also integrated SDC-Scissor into the context of an industrial organization in the automotive domain to demonstrate how it can be used in industrial settings.
ArticleNumber	71
Author	Bosshard, Bill Gambi, Alessio Birchler, Christian Khatiri, Sajad Panichella, Sebastiano
Author_xml	– sequence: 1 givenname: Christian orcidid: 0000-0003-3987-0276 surname: Birchler fullname: Birchler, Christian email: birc@zhaw.ch organization: Zurich University of Applied Science – sequence: 2 givenname: Sajad orcidid: 0000-0003-0354-9747 surname: Khatiri fullname: Khatiri, Sajad organization: Zurich University of Applied Science, Software Institute - USI – sequence: 3 givenname: Bill surname: Bosshard fullname: Bosshard, Bill organization: Meier Planungsdienste GmbH – sequence: 4 givenname: Alessio orcidid: 0000-0002-0132-6497 surname: Gambi fullname: Gambi, Alessio organization: IMC University of Applied Science Krems, University of Passau – sequence: 5 givenname: Sebastiano orcidid: 0000-0003-4120-626X surname: Panichella fullname: Panichella, Sebastiano organization: Zurich University of Applied Science
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SubjectTerms	Autonomous cars Autonomous vehicles Compilers Computer Science Context Cost analysis Cyber-physical systems Effectiveness Fault detection Interpreters Machine learning Optimization Platforms Programming Languages Simulation Software Engineering/Programming and Operating Systems Software testing
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Title	Machine learning-based test selection for simulation-based testing of self-driving cars software
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