Testing Autonomous Cars for Feature Interaction Failures using Many-Objective Search

Raja Ben Abdessalem, Annibale Panichella, Shiva Nejati, Lionel C. Briand, Thomas Stifter

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

15 Citations (Scopus)
460 Downloads (Pure)

Abstract

Complex systems such as autonomous cars are typically built as a composition of features that are independent units of functionality. Features tend to interact and impact one another’s behavior in unknown ways. A challenge is to detect and manage feature interactions, in particular, those that violate system requirements, hence leading to failures. In this paper, we propose a technique to detect feature interaction failures by casting this problem into a search-based test generation problem. We define a set of hybrid test objectives (distance functions) that combine traditional coverage-based heuristics with new heuristics specifically aimed at revealing feature interaction failures. We develop a new search-based test generation algorithm, called FITEST, that is guided by our hybrid test objectives. FITEST extends recently proposed many-objective evolutionary algorithms to reduce the time required to compute fitness values. We evaluate our approach using two versions of an industrial self-driving system. Our results show that our hybrid test objectives are able to identify more than twice as many feature interaction failures as two baseline test objectives used in the software testing literature (i.e., coverage-based and failure-based test objectives). Further, the feedback from domain experts indicates that the detected feature interaction failures represent real faults in their systems that were not previously identified based on analysis of the system features and their requirements.
Original languageEnglish
Title of host publicationProceedings of the 33rd IEEE/ACM International Conference on Automated Software Engineering
Place of PublicationNew York, NY
PublisherAssociation for Computing Machinery (ACM)
Pages143-154
Number of pages12
ISBN (Electronic)978-1-4503-5937-5
DOIs
Publication statusPublished - 2018
EventASE 2018: 33rd IEEE/ACM International Conference on Automated Software Engineering - Montpellier, France
Duration: 3 Jul 20187 Jul 2018
http://www.ase2018.com

Conference

ConferenceASE 2018
Abbreviated titleASE 2018
CountryFrance
CityMontpellier
Period3/07/187/07/18
Internet address

Keywords

  • Software testing and debugging
  • Search-based software engineering
  • Autonomous Cars
  • Many-Objective Search

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  • Research Output

    • 15 Citations
    • 1 Conference contribution

    Automated Repair of Feature Interaction Failures in Automated Driving Systems

    Abdessalem, R. B., Panichella, A., Nejati, S., Briand, L. & Stifter, T., 2020, The ACM SIGSOFT International Symposium on Software Testing and Analysis. Association for Computing Machinery (ACM), p. 88-100 13 p.

    Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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    Cite this

    Abdessalem, R. B., Panichella, A., Nejati, S., Briand, L. C., & Stifter, T. (2018). Testing Autonomous Cars for Feature Interaction Failures using Many-Objective Search. In Proceedings of the 33rd IEEE/ACM International Conference on Automated Software Engineering (pp. 143-154). Association for Computing Machinery (ACM). https://doi.org/10.1145/3238147.3238192