Empirical Assessment of Generating Adversarial Configurations for Software Product Lines - Archive ouverte HAL Access content directly
Journal Articles Empirical Software Engineering Year : 2020

Empirical Assessment of Generating Adversarial Configurations for Software Product Lines

(1) , (1) , (2) , (3) , (2) , (3)
1
2
3
Paul Temple
  • Function : Author
  • PersonId : 16528
  • IdHAL : paul-temple
Gilles Perrouin
  • Function : Author
  • PersonId : 973792
Mathieu Acher

Abstract

Software product line (SPL) engineering allows the derivation of products tailored to stakeholders' needs through the setting of a large number of configuration options. Unfortunately, options and their interactions create a huge configuration space which is either intractable or too costly to explore exhaustively. Instead of covering all products, machine learning (ML) approximates the set of acceptable products (e.g., successful builds, passing tests) out of a training set (a sample of configurations). However, ML techniques can make prediction errors yielding non-acceptable products wasting time, energy and other resources. We apply adversarial machine learning techniques to the world of SPLs and craft new configurations faking to be acceptable configurations but that are not and vice-versa. It allows to diagnose prediction errors and take appropriate actions. We develop two adversarial configuration generators on top of state-of-the-art attack algorithms and capable of synthesizing configurations that are both adversarial and conform to logical constraints. We empirically assess our generators within two case studies: an industrial video synthesizer (MOTIV) and an industry-strength, open-source Web-app configurator (JHipster). For the two cases, our attacks yield (up to) a 100% misclassification rate without sacrificing the logical validity of adversarial configurations. This work lays the foundations of a quality assurance framework for ML-based SPLs.
Fichier principal
Vignette du fichier
Extension_SPLC2019_AdversConfig_EMSE.pdf (5 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-03045797 , version 1 (08-12-2020)

Identifiers

Cite

Paul Temple, Gilles Perrouin, Mathieu Acher, Battista Biggio, Jean-Marc Jézéquel, et al.. Empirical Assessment of Generating Adversarial Configurations for Software Product Lines. Empirical Software Engineering, 2020, 26 (6), pp.1-57. ⟨10.1007/s10664-020-09915-7⟩. ⟨hal-03045797⟩
146 View
251 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More