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Incorporating user preferences in multi-objective feature selection in software product lines using multi-criteria decision analysis

Saber, Takfarinas orcid logoORCID: 0000-0003-2958-7979, Bendechache, Malika orcid logoORCID: 0000-0003-0069-1860 and Ventresque, Anthony orcid logoORCID: 0000-0003-2064-1238 (2021) Incorporating user preferences in multi-objective feature selection in software product lines using multi-criteria decision analysis. In: Optimization and Learning 4th International Conference, OLA 2021, 21-23 June 2021, Catania, Italy. ISBN 978-3-030-85671-7

Abstract
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.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:Feature Selection; Software Product Line; Multi-Objective Evolution Algorithm; Multi-Criteria Decision Analysis.
Subjects:Computer Science > Algorithms
Computer Science > Artificial intelligence
Computer Science > Software engineering
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Institutes and Centres > Lero: The Irish Software Engineering Research Centre
Published in: Dorronsoro, Bernabé, Amodeo, Lionel, Pavone, Mario and Ruiz, Patricia, (eds.) Optimization and Learning. Communications in Computer and Information Science (CCIS) 1443. Springer, Cham. ISBN 978-3-030-85671-7
Publisher:Springer, Cham
Official URL:https://dx.doi.org/10.1007/978-3-030-85672-4_27
Copyright Information:© 2021 Springer
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Science Foundation Ireland grants No. 13/RC/2094 P2 (Lero), Science Foundation Ireland grants No. 13/RC/2106 P2 (ADAPT)
ID Code:26265
Deposited On:15 Sep 2021 09:49 by Takfarinas Saber . Last Modified 15 Sep 2021 10:47
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