Reference : Testing Autonomous Cars for Feature Interaction Failures using Many-Objective Search
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
Security, Reliability and Trust
http://hdl.handle.net/10993/36094
Testing Autonomous Cars for Feature Interaction Failures using Many-Objective Search
English
Ben Abdessalem (helali), Raja [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Panichella, Annibale [[University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]]
Nejati, Shiva mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Briand, Lionel [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Stifter, Thomas [[International Electronics & Engineering (IEE), Contern, Luxembourg]]
2018
Proceedings of the 33rd IEEE/ACM International Conference on Automated Software Engineering (ASE 2018)
Yes
International
33rd IEEE/ACM International Conference on Automated Software Engineering (ASE 2018)
from 03-09-2018 to 07-09-2018
Montpellier
France
[en] Search-based Software Testing ; Many-Objective Optimization ; Automotive Systems ; Feature Interaction Problem
[en] 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 our approach 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.
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Software Verification and Validation Lab (SVV Lab)
Researchers ; Professionals ; Students ; General public ; Others
http://hdl.handle.net/10993/36094
H2020 ; 694277 - TUNE - Testing the Untestable: Model Testing of Complex Software-Intensive Systems

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