Empirical Assessment of Generating Adversarial Configurations for Software Product Lines

Titre traduit de la contribution: Evaluation empirique de générations de configurations malignes pour les lignes de produits logicielles

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

Résultats de recherche: Contribution à un journal/une revueArticleRevue par des pairs

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Résumé

Software product line (SPL) engineering allows the derivation ofproducts tailored to stakeholders’ needs through the setting of a large numberof configuration options.Unfortunately, options and their interactions create a huge configurationspace which is either intractable or too costly to explore exhaustively. Insteadof covering all products, machine learning (ML) approximates the set of ac-ceptable products (e.g.,successful builds, passing tests) out of a training set (asample of configurations). However, ML techniques can make prediction errorsyielding non-acceptable products wasting time, energy and other resources.We applyadversarial machine learning techniquesto the world of SPLs andcraft new configurations faking to be acceptable configurations but that arenot and vice-versa. It allows to diagnose prediction errors and take appropriateactions. We develop two adversarial configuration generators on top of state-of-the-art attack algorithms and capable of synthesizing configurations thatare both adversarial and conform to logical constraints.We empirically assess our generators within two case studies: an industrialvideo synthesizer (MOTIV) and an industry-strength, open-source Web-appconfigurator (JHipster). For the two cases, our attacks yield (up to) a 100%misclassification rate without sacrificing the logical validity of adversarial con-figurations. This work lays the foundations of a quality assurance frameworkfor ML-based SPLs.
Titre traduit de la contributionEvaluation empirique de générations de configurations malignes pour les lignes de produits logicielles
langue originaleAnglais
Nombre de pages57
journalEmpirical Software Engineering
Etat de la publicationAccepté/sous presse - 2020

mots-clés

  • ligne de produits logiciels
  • systèmes configurables
  • variabilité logicielle
  • test logiciel
  • machine learning
  • assurance qualité

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