Feature selection optimisation of software product line using metaheuristic techniques

Abstract

The role of software product line (SPL) is very important in representing the same system with multiple variants. Feature models are used to define SPL. In this paper, genetic algorithm (GA), hyper-heuristic algorithm and particle swarm optimisation (PSO) have been applied for feature selection optimisation in SPL. Also, an improved fitness function is applied for optimisation of features in SPL. The objective function is designed by taking reusability and consistency of features (components) into consideration. Furthermore, we have used a case study and discussed about software product line in detail. A non-parametric test, i.e., Kruskal-Wallis test has been performed to analyse performance and computation time of 20 to 1,000 features sets and identify core features. Through extensive experimental analysis, it is observed that PSO outperforms GA and hyper-heuristic algorithm.