Paper Info
Title
Towards Estimating and Predicting User Perception on Software Product Variants.
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.
Year
DOI
Venue
2018
10.1007/978-3-319-90421-4_2
Lecture Notes in Computer Science
Keywords
Field
DocType
Software product lines,Quality attributes,Quality estimation,Computer-generated art,Product variants
Ranking,Computer science,Software,Artificial intelligence,Software product line,The arts,Combinatorial explosion,Perception,Genetic algorithm,Machine learning
Conference
Volume
ISSN
Citations 
10826
0302-9743
1
PageRank 
References 
Authors
0.35
14
9
Name
Order
Citations
PageRank
Jabier Martinez114113.68
Jean-Sébastien Sottet2978.25
Alfonso García Frey3656.40
Tegawendé F. Bissyandé486363.90
Tewfik Ziadi538429.11
Jacques Klein62498112.20
Paul Temple731.72
Mathieu Acher874752.36
Yves Le Traon915514.08