Jamil, M.A.; Nour, M.K.; Alotaibi, S.S.; Hussain, M.J.; Hussaini, S.M.; Naseer, A. Software Product Line Maintenance Using Multi-Objective Optimization Techniques. Appl. Sci.2023, 13, 9010.
Jamil, M.A.; Nour, M.K.; Alotaibi, S.S.; Hussain, M.J.; Hussaini, S.M.; Naseer, A. Software Product Line Maintenance Using Multi-Objective Optimization Techniques. Appl. Sci. 2023, 13, 9010.
Jamil, M.A.; Nour, M.K.; Alotaibi, S.S.; Hussain, M.J.; Hussaini, S.M.; Naseer, A. Software Product Line Maintenance Using Multi-Objective Optimization Techniques. Appl. Sci.2023, 13, 9010.
Jamil, M.A.; Nour, M.K.; Alotaibi, S.S.; Hussain, M.J.; Hussaini, S.M.; Naseer, A. Software Product Line Maintenance Using Multi-Objective Optimization Techniques. Appl. Sci. 2023, 13, 9010.
Abstract
Currently, software development is more associated with families of configurable software rather than the single implementation of a product. Due to the numerous possible combinations in a software product line, testing these families of software product lines (SPLs) is a difficult undertaking (SPL). Moreover, the presence of optional features makes the testing of SPLs impractical. Several features are presented in SPLs, but due to the environment's time and financial constraints, these features are rendered unfeasible. Testing subsets of configured products is thus one approach to solving this issue. In order to reduce testing effort and get better results, alternative methods of testing SPLs are required, such as the combinatorial interaction testing (CIT) technique. Unfortunately CIT method produces unscalable solutions for large size SPLs with excessive constraints. The CIT method costs more because of feature combinations. The optimization of the various conflicting testing objectives, such as reducing the cost and configuration number, have also been considered. In this article, we have proposed a search-based software engineering solution using multi-objective optimization algorithms (MOEAs). In particular, the research is applied to different types of MOEA methods; Indicator-Based Evolutionary Algorithm (IBEA), Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D), Non-Dominated Sorting Genetic Algorithm II (NSGAII), NSGAIII, and Strength Pareto Evolutionary Algorithm 2 (SPEA2). The results of the algorithms are examined in the context of distinct objectives and two quality indicators. The results revealed how feature model attributes, implementation context and the number of objectives affect the performance of the algorithms.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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