Search-based Similarity-driven Behavioural SPL Testing

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Abstract

Dissimilar test cases have been proven to be effective to reveal faults in software systems. In the Software Product Line (SPL) context, this criterion has been applied successfully to mimic combinatorial interaction testing in an efficient and scalable manner by selecting and prioritising most dissimilar configurations of feature models using evolutionary algorithms. In this paper, we extend dissimilarity to behavioural SPL models (FTS) in a search-based approach, and evaluate its effectiveness in terms of product and fault coverage. We investigate different distances as well as as single-objective algorithms, (dissimilarity on actions, random, all-actions). Our results on four case studies show the relevance of dissimilarity-based test generation for behavioural SPL models, especially on the largest case-study where no other approach can match it.
Original languageEnglish
Title of host publicationProceedings of the Tenth International Workshop on Variability Modelling of Software-intensive Systems
Place of PublicationSalvador of Bahia, Brazil
PublisherACM Press
Pages89-96
Number of pages8
ISBN (Print)978-1-4503-4019-9
DOIs
Publication statusPublished - 27 Jan 2016

Publication series

NameVaMoS '16
PublisherACM

Keywords

  • Dissimilarity Testing
  • Featured Transition System
  • Software Product Line Testing

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  • Projects

    Student Theses

    Behavioural model-based testing of software product lines

    Author: Devroey, X., 30 Aug 2017

    Supervisor: Schobbens, P. (Supervisor), Heymans, P. (Supervisor), Englebert, V. (President), Baudry, B. (External person) (Jury), Cohen, M. B. (External person) (Jury), Legay, A. (Jury) & Perrouin, G. (Jury)

    Student thesis: Doc typesDoctor of Sciences

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    Cite this

    Devroey, X., Perrouin, G., Legay, A., Schobbens, P-Y., & Heymans, P. (2016). Search-based Similarity-driven Behavioural SPL Testing. In Proceedings of the Tenth International Workshop on Variability Modelling of Software-intensive Systems (pp. 89-96). (VaMoS '16). ACM Press. https://doi.org/10.1145/2866614.2866627