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ThinkMind // ICSEA 2021, The Sixteenth International Conference on Software Engineering Advances // View article icsea_2021_2_170_10094
Towards a Smart Feature Model Evolution
 Authors:
Olfa Ferchichi
Raoudha Beltaifa
Lamia Labed Jilani
Raúl Mazo Keywords: Software Product Line Engineering (SPLE), Variability Management, Variability Modeling, Feature Models (FM), FODA, non-functional features Abstract:
Abstract—Context and motivation]: With the proliferation of new technology platforms, new operational requirements, different contexts and so on, agility remains more and more solicitated for software evolution. In the context of Software Product Line Engineering (SPLE), the Feature Model (FM) is the basic instrument that supports the evolution of SPL at the variability level. [Problem]: Given the general context presented above, two key questions arise: 1) How to improve FM diagrams to make them understandable during the evolution of the corresponding product lines ? 2) How to make FM evolution more systematic and more intelligent? [Contribution]: In our work, we aim to evolve FMs by means of smart techniques. Hence, we represent feature models by an ontology. This latter will permit, among others, the inference of knowledge about the evolution of the FMs. By obtaining different versions of the FMs, these can be used as a learning base of a learning algorithm. So, for a given FM, a new version can be predicted as being an evolution version of the FM. In this paper, we present the FM metamodel extension necessary to represent the semantics of the evolution rules. Thus, with a model driven approach, FMs are transformed into FM ontologies. A running example about an Electric Brake Parking system extracted from the SPLOT repository is presented. Pages: 149 to 154 Copyright: Copyright (c) IARIA, 2021 Publication date: October 3, 2021 Published in: conference ISSN: 2308-4235 ISBN: 978-1-61208-894-5 Location: Barcelona, Spain Dates: from October 3, 2021 to October 7, 2021
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