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Maintain optimal configurations for large configurable systems using multi-objective optimization

To improve the maintenance and quality of software product lines, efficient configurations techniques have been proposed. Nevertheless, due to the complexity of derived and configured products in a product line, the configuration process of the software product line (SPL) becomes time consuming and...

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Main Authors: Jamil, Muhammad Abid, Alsadie, Deafallah, Nour, Mohamed K., Awang Abu Bakar, Normi Sham
Format: Article
Language:English
Published: Tech Science Press 2022
Subjects:
T Technology (General)
Online Access:http://irep.iium.edu.my/98548/7/98548_Maintain%20optimal%20configurations%20for%20large%20configurable%20systems.pdf
http://irep.iium.edu.my/98548/
https://www.techscience.com/cmc/v73n2/48382/pdf
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spelling my.iium.irep.985482022-06-28T07:59:37Z http://irep.iium.edu.my/98548/ Maintain optimal configurations for large configurable systems using multi-objective optimization Jamil, Muhammad Abid Alsadie, Deafallah Nour, Mohamed K. Awang Abu Bakar, Normi Sham T Technology (General) To improve the maintenance and quality of software product lines, efficient configurations techniques have been proposed. Nevertheless, due to the complexity of derived and configured products in a product line, the configuration process of the software product line (SPL) becomes time consuming and costly. Each product line consists of a various number of feature models that need to be tested. The different approaches have been presented by Search-based software engineering (SBSE) to resolve the software engineering issues into computational solutions using some metaheuristic approach. Hence, multiobjective evolutionary algorithms help to optimize the configuration process of SPL. In this paper, different multi-objective Evolutionary Algorithms like Non-Dominated Sorting Genetic algorithms II (NSGA-II) and NSGA-III and Indicator based Evolutionary Algorithm (IBEA) are applied to different feature models to generate optimal results for large configurable. The proposed approach is also used to generate the optimized test suites with the help of different multi-objective Evolutionary Algorithms (MOEAs). Tech Science Press 2022-05-06 Article PeerReviewed application/pdf en http://irep.iium.edu.my/98548/7/98548_Maintain%20optimal%20configurations%20for%20large%20configurable%20systems.pdf Jamil, Muhammad Abid and Alsadie, Deafallah and Nour, Mohamed K. and Awang Abu Bakar, Normi Sham (2022) Maintain optimal configurations for large configurable systems using multi-objective optimization. Computers,Materials & Continua, 73 (2). pp. 4407-4421. ISSN 1546-2218 E-ISSN 1546-2226 https://www.techscience.com/cmc/v73n2/48382/pdf DOI: 10.32604/cmc.2022.029096
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Jamil, Muhammad Abid
Alsadie, Deafallah
Nour, Mohamed K.
Awang Abu Bakar, Normi Sham
Maintain optimal configurations for large configurable systems using multi-objective optimization
description To improve the maintenance and quality of software product lines, efficient configurations techniques have been proposed. Nevertheless, due to the complexity of derived and configured products in a product line, the configuration process of the software product line (SPL) becomes time consuming and costly. Each product line consists of a various number of feature models that need to be tested. The different approaches have been presented by Search-based software engineering (SBSE) to resolve the software engineering issues into computational solutions using some metaheuristic approach. Hence, multiobjective evolutionary algorithms help to optimize the configuration process of SPL. In this paper, different multi-objective Evolutionary Algorithms like Non-Dominated Sorting Genetic algorithms II (NSGA-II) and NSGA-III and Indicator based Evolutionary Algorithm (IBEA) are applied to different feature models to generate optimal results for large configurable. The proposed approach is also used to generate the optimized test suites with the help of different multi-objective Evolutionary Algorithms (MOEAs).
format Article
author Jamil, Muhammad Abid
Alsadie, Deafallah
Nour, Mohamed K.
Awang Abu Bakar, Normi Sham
author_facet Jamil, Muhammad Abid
Alsadie, Deafallah
Nour, Mohamed K.
Awang Abu Bakar, Normi Sham
author_sort Jamil, Muhammad Abid
title Maintain optimal configurations for large configurable systems using multi-objective optimization
title_short Maintain optimal configurations for large configurable systems using multi-objective optimization
title_full Maintain optimal configurations for large configurable systems using multi-objective optimization
title_fullStr Maintain optimal configurations for large configurable systems using multi-objective optimization
title_full_unstemmed Maintain optimal configurations for large configurable systems using multi-objective optimization
title_sort maintain optimal configurations for large configurable systems using multi-objective optimization
publisher Tech Science Press
publishDate 2022
url http://irep.iium.edu.my/98548/7/98548_Maintain%20optimal%20configurations%20for%20large%20configurable%20systems.pdf
http://irep.iium.edu.my/98548/
https://www.techscience.com/cmc/v73n2/48382/pdf
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score 13.223943

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