Authors: Hitesh Yadav; A. Charan Kumari; Rita Chhikara
Addresses: Department of Computer Science and Engineering, The NorthCap University, Gurugram, India ' Faculty of Engineering, Dayalbagh Educational Institute, Dayalbagh, Agra, India ' Department of Computer Science and Engineering, The NorthCap University, Gurugram, India
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.
Keywords: genetic algorithm; product line; feature model; particle swarm optimisation; PSO; software product line; SPL; hyper-heuristic evolutionary algorithm.
International Journal of Embedded Systems, 2020 Vol.13 No.1, pp.50 - 64
Received: 10 Oct 2018
Accepted: 28 Feb 2019
Published online: 11 May 2020 *