TY - GEN
T1 - Incorporating User Preferences in Multi-objective Feature Selection in Software Product Lines Using Multi-Criteria Decision Analysis
AU - Saber, Takfarinas
AU - Bendechache, Malika
AU - Ventresque, Anthony
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - Software Product Lines Engineering has created various tools that assist with the standardisation in the design and implementation of clusters of equivalent software systems with an explicit representation of variability choices in the form of Feature Models, making the selection of the most ideal software product a Feature Selection problem. With the increase in the number of properties, the problem needs to be defined as a multi-objective optimisation where objectives are considered independently one from another with the goal of finding and providing decision-makers a large and diverse set of non-dominated solutions/products. Following the optimisation, decision-makers define their own (often complex) preferences on how does the ideal software product look like. Then, they select the unique solution that matches their preferences the most and discard the rest of the solutions—sometimes with the help of some Multi-Criteria Decision Analysis technique. In this work, we study the usability and the performance of incorporating preferences of decision-makers by carrying-out Multi-Criteria Decision Analysis directly within the multi-objective optimisation to increase the chances of finding more solutions that match preferences of the decision-makers the most and avoid wasting execution time searching for non-dominated solutions that are poor with respect to decision-makers’ preferences.
AB - Software Product Lines Engineering has created various tools that assist with the standardisation in the design and implementation of clusters of equivalent software systems with an explicit representation of variability choices in the form of Feature Models, making the selection of the most ideal software product a Feature Selection problem. With the increase in the number of properties, the problem needs to be defined as a multi-objective optimisation where objectives are considered independently one from another with the goal of finding and providing decision-makers a large and diverse set of non-dominated solutions/products. Following the optimisation, decision-makers define their own (often complex) preferences on how does the ideal software product look like. Then, they select the unique solution that matches their preferences the most and discard the rest of the solutions—sometimes with the help of some Multi-Criteria Decision Analysis technique. In this work, we study the usability and the performance of incorporating preferences of decision-makers by carrying-out Multi-Criteria Decision Analysis directly within the multi-objective optimisation to increase the chances of finding more solutions that match preferences of the decision-makers the most and avoid wasting execution time searching for non-dominated solutions that are poor with respect to decision-makers’ preferences.
KW - Feature selection
KW - Multi-Criteria Decision Analysis
KW - Multi-objective evolution algorithm
KW - Software product line
UR - https://www.scopus.com/pages/publications/85115163540
U2 - 10.1007/978-3-030-85672-4_27
DO - 10.1007/978-3-030-85672-4_27
M3 - Conference Publication
AN - SCOPUS:85115163540
SN - 9783030856717
T3 - Communications in Computer and Information Science
SP - 361
EP - 373
BT - Optimization and Learning - 4th International Conference, OLA 2021, Proceedings
A2 - Dorronsoro, Bernabé
A2 - Ruiz, Patricia
A2 - Amodeo, Lionel
A2 - Pavone, Mario
PB - Springer Science and Business Media Deutschland GmbH
T2 - 4th International Conference on Optimization and Learning, OLA 2021
Y2 - 21 June 2021 through 23 June 2021
ER -