Open Access

ARTICLE

Maintain Optimal Configurations for Large Configurable Systems Using Multi-Objective Optimization

Muhammad Abid Jamil^1,*, Deafallah Alsadie¹, Mohamed K. Nour¹, Normi Sham Awang Abu Bakar²

1 Department of Computer Science, Umm Al-Qura University, Makkah, Saudi Arabia
2 Kulliyyah of Information and Communication Technology, International Islamic University, Malaysia

* Corresponding Author: Muhammad Abid Jamil. Email:

Computers, Materials & Continua 2022, 73(2), 4407-4422. https://doi.org/10.32604/cmc.2022.029096

Received 25 February 2022; Accepted 06 May 2022; Issue published 16 June 2022

Abstract

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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Keywords

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