Comparing the performance of quantum-inspired evolutionary algorithms for the solution of software requirements selection problem

•Increase in number of requirements will increase complexity of selection process•Efficacy of quantum-inspired approaches for requirements selection were studied•MQHDE is effective in finding good solutions for requirements selection problem•The practical relevance of the study and applicability of...

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Bibliographic Details
Published in:Information and software technology 2016-08, Vol.76, p.31-64
Main Authors: Kumari, A. Charan, Srinivas, K.
Format: Article
Language:English
Subjects:
Algorithms
Boundaries
Comparative analysis
Computer programs
Effectiveness
Evolutionary algorithms
Genetic algorithms
Mathematical models
Pareto optimality
Quantum-inspired evolutionary algorithms
Requirements analysis
Requirements engineering
Search Based Software Engineering
Software
Software requirements selection
Studies
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Summary:•Increase in number of requirements will increase complexity of selection process•Efficacy of quantum-inspired approaches for requirements selection were studied•MQHDE is effective in finding good solutions for requirements selection problem•The practical relevance of the study and applicability of results was presented In requirements engineering phase of the software development life cycle, one of the main concerns of software engineers is to select a set of software requirements for implementation in the next release of the software from many requirements proposed by the customers, while balancing budget and customer satisfaction. To analyse the efficacy of Quantum-inspired Elitist Multi-objective Evolutionary Algorithm (QEMEA), Quantum-inspired Multi-objective Differential Evolution Algorithm (QMDEA) and Multi-objective Quantum-inspired Hybrid Differential Evolution (MQHDE) in solving the software requirements selection problem. The paper reports on empirical evaluation of the performance of three quantum-inspired multi-objective evolutionary algorithms along with Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The comparison includes the obtained Pareto fronts, the three performance metrics – Generational Distance, Spread and Hypervolume, attained boundary solutions, and size of the Pareto front. The results reveal that MQHDE outperformed other methods in producing high quality solutions; while QMDEA is able to produce well distributed solutions with extreme boundary solutions. The hybridization of Differential Evolution with Genetic Algorithms coupled with quantum computing concepts (MQHDE) provided a means to effectively balance the two issues of multi-objective optimization - convergence and diversity. [Display omitted]
ISSN:0950-5849
1873-6025
DOI:10.1016/j.infsof.2016.04.010