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New Opportunities for Software Reuse International Conference on Software Reuse

ICSR 2018: New Opportunities for Software Reuse pp 23-40 | Cite as

Towards Estimating and Predicting User Perception on Software Product Variants

Part of the Lecture Notes in Computer Science book series (LNCS, volume 10826)


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.


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



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







Martinez J. et al. (2018) Towards Estimating and Predicting User Perception on Software Product Variants. In: Capilla R., Gallina B., Cetina C. (eds) New Opportunities for Software Reuse. ICSR 2018. Lecture Notes in Computer Science, vol 10826. Springer, Cham



