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Authors: Olfa Ferchichi 1 ; Raoudha Beltaifa 2 ; 3 and Lamia Labed 4

Affiliations: 1 Laboratoire Riadhi Ecole Nationale des Sciences de l’Informatiques, Tunisia ; 2 Sesame University, Tunisia ; 3 Tunis University, Tunisia ; 4 Carthage University, Tunisia

Keyword(s): Software Product Lines, Variability, Feature Models, Configuration, Advanced Apriori Algorithms, Association Rule Learning.

Abstract: The evolution the Software Product Line processes requires targeted support to address emerging customer functionals and non functionals requirements, evolving technology platforms, and new business strategies. Enhancing the features of core assets is a particularly promising avenue in Software Product Line evolution.The manual configuration of SPLs is already highly complex and error-prone. The key challenge of using feature models is to derive a product configuration that satisfies all business and customer requirements. However, proposing a unsupervised learning-based solution to facilitate this evolution is a growing challenge. To address this challenge, in this paper we use association rules learning to support business during product configuration in SPL. Based on extended feature models, advanced apriori algorithm automatically finds an optimal product configuration that maximizes the customer satisfaction. Our proposal is applied on a practical case involving the feature mode l of a Mobile Phone Product Line. (More)

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Paper citation in several formats:
Ferchichi, O.; Beltaifa, R. and Labed, L. (2024). Association Rule Learning Based Approach to Automatic Generation of Feature Model Configurations. In Proceedings of the 19th International Conference on Evaluation of Novel Approaches to Software Engineering - ENASE; ISBN 978-989-758-696-5; ISSN 2184-4895, SciTePress, pages 262-272. DOI: 10.5220/0012742000003687

@conference{enase24,
author={Olfa Ferchichi. and Raoudha Beltaifa. and Lamia Labed.},
title={Association Rule Learning Based Approach to Automatic Generation of Feature Model Configurations},
booktitle={Proceedings of the 19th International Conference on Evaluation of Novel Approaches to Software Engineering - ENASE},
year={2024},
pages={262-272},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012742000003687},
isbn={978-989-758-696-5},
issn={2184-4895},
}

TY - CONF

JO - Proceedings of the 19th International Conference on Evaluation of Novel Approaches to Software Engineering - ENASE
TI - Association Rule Learning Based Approach to Automatic Generation of Feature Model Configurations
SN - 978-989-758-696-5
IS - 2184-4895
AU - Ferchichi, O.
AU - Beltaifa, R.
AU - Labed, L.
PY - 2024
SP - 262
EP - 272
DO - 10.5220/0012742000003687
PB - SciTePress

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