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Towards Estimating and Predicting User Perception on Software Product Variants

  • Jabier Martinez
  • Jean-Sébastien Sottet
  • Alfonso García Frey
  • Tegawendé F. Bissyandé
  • Tewfik Ziadi
  • Jacques Klein
  • Paul Temple
  • Mathieu Acher
  • Yves le Traon
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10826)

Abstract

Estimating and predicting user subjective perceptions on software products is a challenging, yet increasingly important, endeavour. As an extreme case study, we consider the problem of exploring computer-generated art object combinations that will please the maximum number of people. Since it is not feasible to gather feedbacks for all art products because of a combinatorial explosion of possible configurations as well as resource and time limitations, the challenging objective is to rank and identify optimal art product variants that can be generated based on their average likability. We present the use of Software Product Line (SPL) techniques for gathering and leveraging user feedbacks within the boundaries of a variability model. Our approach is developed in two phases: (1) the creation of a data set using a genetic algorithm and real feedback and (2) the application of a data mining technique on this data set to create a ranking enriched with confidence metrics. We perform a case study of a real-world computer-generated art system. The results of our approach on the arts domain reveal interesting directions for the analysis of user-specific qualities of SPLs.

Keywords

Software product lines Quality attributes Quality estimation Computer-generated art Product variants 

Notes

Acknowledgments

Martinez and Tewfik’s work is supported by the ITEA3 15010 REVaMP\(^2\) project: FUI the Île-de-France region and BPI in France.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Jabier Martinez
    • 1
  • Jean-Sébastien Sottet
    • 2
  • Alfonso García Frey
    • 4
  • Tegawendé F. Bissyandé
    • 3
  • Tewfik Ziadi
    • 1
  • Jacques Klein
    • 3
  • Paul Temple
    • 5
  • Mathieu Acher
    • 5
  • Yves le Traon
    • 3
  1. 1.Sorbonne University, UPMCParisFrance
  2. 2.Luxembourg Institute of Science and TechnologyEsch-sur-AlzetteLuxembourg
  3. 3.University of LuxembourgLuxembourgLuxembourg
  4. 4.YotakoLuxembourgLuxembourg
  5. 5.Univ Rennes, Inria, CNRS, IRISARennesFrance

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