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: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Tech Science Press
2022
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| Subjects: | |
| 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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| Summary: | 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). |
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