50 Int. J. Embedded Systems, Vol. 13, No. 1, 2020 Copyright © 2020 Inderscience Enterprises Ltd. Feature selection optimisation of software product line using metaheuristic techniques Hitesh Yadav* Department of Computer Science and Engineering, The NorthCap University, Gurugram, India Email: hiteshi.yadav@gmail.com *Corresponding author A. Charan Kumari Faculty of Engineering, Dayalbagh Educational Institute, Dayalbagh, Agra, India Email: charankumari@yahoo.co.in Rita Chhikara Department of Computer Science and Engineering, The NorthCap University, Gurugram, India Email: ritachhikara@ncuindia.edu 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. Reference to this paper should be made as follows: Yadav, H., Kumari, A.C. and Chhikara, R. (2020) ‘Feature selection optimisation of software product line using metaheuristic techniques’, Int. J. Embedded Systems, Vol. 13, No. 1, pp.50–64. Biographical notes: Hitesh Yadav is pursuing her PhD and is an MTech holder in Computer Science. She has 6+ years’ experience. She got second position in BE at university level. She was also awarded scholarship in BE. She is a CISCO certified Training Instructor for CCNA module-1, 2, 3 and 4. She has been awarded as an Advanced Level Instructor in 2017. She has published many research papers in international conferences including IEEE, Elsevier. Her areas of interests are on software engineering, computer network and optimisation technique. She has guided many BTech Projects and MTech Thesis. A. Charan Kumari received her PhD from Dayalbagh Educational Institute, in collaboration with Indian Institute of Technology, Delhi, India, under their MOU. She has an excellent teaching experience of 17 years in various esteemed institutions and received various accolades including the best teacher award. Her current research interests include search based software engineering, evolutionary computation and soft computing techniques. She has published papers in journals and conferences of national and international repute. She has also served as reviewer of various journals and conferences. She has delivered an invited talk at 43rd CREST Open Workshop on Hyper-heuristics at University College London, London. She served as a Technical Committee Chair at International Conference on Computational Intelligence and Data Science (ICCIDS 2018). She is a member of IEEE, Computer Society of India (CSI) and Systems Society of India (SSI).