Standard

Testing Autonomous Cars for Feature Interaction Failures using Many-Objective Search. / Abdessalem, Raja Ben; Panichella, Annibale; Nejati, Shiva; Briand, Lionel C.; Stifter, Thomas.

Proceedings of the 33rd IEEE/ACM International Conference on Automated Software Engineering. New York, NY : Association for Computing Machinery (ACM), 2018. p. 143-154.

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Harvard

Abdessalem, RB, Panichella, A, Nejati, S, Briand, LC & 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. Association for Computing Machinery (ACM), New York, NY, pp. 143-154, ASE 2018, Montpellier, France, 3/07/18. https://doi.org/10.1145/3238147.3238192

APA

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). New York, NY: Association for Computing Machinery (ACM). https://doi.org/10.1145/3238147.3238192

Vancouver

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

Author

Abdessalem, Raja Ben ; Panichella, Annibale ; Nejati, Shiva ; Briand, Lionel C. ; Stifter, Thomas. / Testing Autonomous Cars for Feature Interaction Failures using Many-Objective Search. Proceedings of the 33rd IEEE/ACM International Conference on Automated Software Engineering. New York, NY : Association for Computing Machinery (ACM), 2018. pp. 143-154

BibTeX

@inproceedings{38b81178299d4cd28f682a1aeab7af19,
title = "Testing Autonomous Cars for Feature Interaction Failures using Many-Objective Search",
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.",
keywords = "Software testing and debugging, Search-based software engineering, Autonomous Cars, Many-Objective Search",
author = "Abdessalem, {Raja Ben} and Annibale Panichella and Shiva Nejati and Briand, {Lionel C.} and Thomas Stifter",
year = "2018",
doi = "10.1145/3238147.3238192",
language = "English",
pages = "143--154",
booktitle = "Proceedings of the 33rd IEEE/ACM International Conference on Automated Software Engineering",
publisher = "Association for Computing Machinery (ACM)",
address = "United States",

}

RIS

TY - GEN

T1 - Testing Autonomous Cars for Feature Interaction Failures using Many-Objective Search

AU - Abdessalem, Raja Ben

AU - Panichella, Annibale

AU - Nejati, Shiva

AU - Briand, Lionel C.

AU - Stifter, Thomas

PY - 2018

Y1 - 2018

N2 - 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.

AB - 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.

KW - Software testing and debugging

KW - Search-based software engineering

KW - Autonomous Cars

KW - Many-Objective Search

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

U2 - 10.1145/3238147.3238192

DO - 10.1145/3238147.3238192

M3 - Conference contribution

SP - 143

EP - 154

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

PB - Association for Computing Machinery (ACM)

CY - New York, NY

ER -

ID: 45811364