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

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

Abdessalem, Raja Ben (University of Luxembourg)
Panichella, A. (TU Delft Software Engineering; University of Luxembourg)
Nejati, Shiva (University of Luxembourg)
Briand, Lionel C. (University of Luxembourg)
Stifter, Thomas (IEE S.A.)

2018

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.

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

http://resolver.tudelft.nl/uuid:38b81178-299d-4cd2-8f68-2a1aeab7af19

https://doi.org/10.1145/3238147.3238192

Association for Computing Machinery (ACM), New York, NY

978-1-4503-5937-5

Proceedings of the 33rd IEEE/ACM International Conference on Automated Software Engineering

ASE 2018, 2018-07-03 → 2018-07-07, Montpellier, France

Institutional Repository

conference paper

© 2018 Raja Ben Abdessalem, A. Panichella, Shiva Nejati, Lionel C. Briand, Thomas Stifter