TY - GEN
T1 - Towards Estimating and Predicting User Perception on Software Product Variants
AU - Martinez, Jabier
AU - Sotet, Jean-Sébastien
AU - Frey, Alfonso Garcia
AU - Bissyandé, Tegawendé F.
AU - Ziadi, Tewfik
AU - Klein, Jacques
AU - Temple, Paul
AU - Acher, Mathieu
AU - Le Traon, Yves
N1 - Publisher Copyright:
© 2018, Springer International Publishing AG, part of Springer Nature.
PY - 2018/4/17
Y1 - 2018/4/17
N2 - 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.
AB - 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.
KW - Computer-generated art
KW - Product variants
KW - Quality attributes
KW - Quality estimation
KW - Software product lines
UR - https://hal.sorbonne-universite.fr/hal-01720519
UR - http://www.scopus.com/inward/record.url?scp=85047137471&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-90421-4_2
DO - 10.1007/978-3-319-90421-4_2
M3 - Conference contribution
SN - 9783319904207
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 23
EP - 40
BT - New Opportunities for Software Reuse - 17th International Conference, ICSR 2018, Proceedings
A2 - Capilla, Rafael
A2 - Cetina, Carlos
A2 - Gallina, Barbara
ER -
