Towards qality assurance of software product lines with adversarial configurations

Paul Temple, Mathieu Acher, Gilles Perrouin, Battista Biggio, Jean Marc Jézéquel, Fabio Roli

Research output: Contribution in Book/Catalog/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationSPLC 2019 - 23rd International Systems and Software Product Line Conference
EditorsThorsten Berger, Philippe Collet, Laurence Duchien, Thomas Fogdal, Patrick Heymans, Timo Kehrer, Jabier Martinez, Raul Mazo, Leticia Montalvillo, Camille Salinesi, Xhevahire Ternava, Thomas Thum, Tewfik Ziadi
PublisherACM Press
ISBN (Electronic)9781450371384
DOIs
Publication statusPublished - 9 Sep 2019
Event23rd International Systems and Software Product Line Conference, SPLC 2019, co-located with the 13th European Conference on Software Architecture, ECSA 2019 - Paris, France
Duration: 9 Sep 201913 Sep 2019

Publication series

NameACM International Conference Proceeding Series
VolumeA

Conference

Conference23rd International Systems and Software Product Line Conference, SPLC 2019, co-located with the 13th European Conference on Software Architecture, ECSA 2019
CountryFrance
CityParis
Period9/09/1913/09/19

Keywords

  • Machine learning
  • Quality assurance
  • Software product line
  • Software testing
  • Software variability

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  • Cite this

    Temple, P., Acher, M., Perrouin, G., Biggio, B., Jézéquel, J. M., & Roli, F. (2019). Towards qality assurance of software product lines with adversarial configurations. In T. Berger, P. Collet, L. Duchien, T. Fogdal, P. Heymans, T. Kehrer, J. Martinez, R. Mazo, L. Montalvillo, C. Salinesi, X. Ternava, T. Thum, & T. Ziadi (Eds.), SPLC 2019 - 23rd International Systems and Software Product Line Conference (ACM International Conference Proceeding Series; Vol. A). ACM Press. https://doi.org/10.1145/3336294.3336309