@inproceedings{1b4dc6fad1ab4dbc8517466e2356a51c,
title = "Towards quality assurance of software product lines with adversarial configurations",
abstract = "Software product line (SPL) engineers put a lot of effort to ensure that, through the setting of a large number of possible configuration options, products are acceptable and well-tailored to customers' needs. Unfortunately, options and their mutual interactions create a huge configuration space which is intractable to exhaustively explore. Instead of testing all products, machine learning is increasingly employed to approximate the set of acceptable products out of a small training sample of configurations. Machine learning (ML) techniques can refine a software product line through learned constraints and a priori prevent non-acceptable products to be derived. In this paper, we use adversarial ML techniques to generate adversarial configurations fooling ML classifiers and pinpoint incorrect classifications of products (videos) derived from an industrial video generator. Our attacks yield (up to) a 100% misclassification rate and a drop in accuracy of 5%. We discuss the implications these results have on SPL quality assurance.",
keywords = "Machine learning, Quality assurance, Software product line, Software testing, Software variability",
author = "Paul Temple and Mathieu Acher and Gilles Perrouin and Battista Biggio and J{\'e}z{\'e}quel, {Jean Marc} and Fabio Roli",
note = "Funding Information: This work has been partially funded by the Ministry of Science and Technology of Spain through ECLIPSE (RTI2018-094283-B-C33), the Junta de Andaluc?a via the PIRAMIDE and METAMORFOSIS projects, the European Regional Development Fund (ERDF/FEDER), and the MINECO Juan de la Cierva postdoctoral program. The authors would like to thank the C?tedra de Telef?nica {"}Inteligencia en la Red{"} of the Universidad de Sevilla for its support. Funding Information: ∗Gilles Perrouin is an FNRS Research Associate. This research was partly supported by EOS Verilearn project grant no. O05518F-RG03. This research was also funded by the ANR-17-CE25-0010-01 VaryVary project. Publisher Copyright: {\textcopyright} 2019 Association for Computing Machinery. Copyright: Copyright 2019 Elsevier B.V., All rights reserved.; 23rd International Systems and Software Product Line Conference, SPLC 2019, co-located with the 13th European Conference on Software Architecture, ECSA 2019 ; Conference date: 09-09-2019 Through 13-09-2019",
year = "2019",
month = sep,
day = "9",
doi = "10.1145/3336294.3336309",
language = "English",
series = "ACM International Conference Proceeding Series",
publisher = "ACM Press",
editor = "Thorsten Berger and Philippe Collet and Laurence Duchien and Thomas Fogdal and Patrick Heymans and Timo Kehrer and Jabier Martinez and Raul Mazo and Leticia Montalvillo and Camille Salinesi and Xhevahire Ternava and Thomas Thum and Tewfik Ziadi",
booktitle = "SPLC 2019 - 23rd International Systems and Software Product Line Conference",
address = "United States",
}
