Boosted Whale Optimization Algorithm With Natural Selection Operators for Software Fault Prediction

Yousef Hassouneh, Hamza Turabieh, Thaer Thaher, Iyad Tumar, Hamouda Chantar, Jingwei Too
2021 IEEE Access  
Software fault prediction (SFP) is a challenging process that any successful software should go through it to make sure that all software components are free of faults. In general, soft computing and machine learning methods are useful in tackling this problem. The size of fault data is usually huge since it is obtained from mining software historical repositories. This data consists of a large number of features (metrics). Determining the most valuable features (i.e., Feature Selection (FS) is
more » ... an excellent solution to reduce data dimensionality. In this paper, we proposed an enhanced version of the Whale Optimization Algorithm (WOA) by combining it with a single point crossover method. The proposed enhancement helps the WOA to escape from local optima by enhancing the exploration process. Five different selection methods are employed: Tournament, Roulette wheel, Linear rank, Stochastic universal sampling, and random-based. To evaluate the performance of the proposed enhancement, 17 available SFP datasets are adopted from the PROMISE repository. The deep analysis shows that the proposed approach outperformed the original WOA and the other six state-of-the-art methods, as well as enhanced the overall performance of the machine learning classifier. INDEX TERMS Software fault prediction, feature selection, binary whale optimization algorithm, adaptive synthetic sampling, classification. , teaching courses in software engineering, internet programming, and programming languages. He has an academic administration experience, as he served as the Department Chair and the Director of the Computing Master Program. He has a profound experience in human-computer interaction. He designed a collaboration framework and groupware tool to enable Requirements Engineering Team collaboration. He participated in several EU funded projects. His research interests include software architecture, virtual software engineering teams, Software risk assessment and metrics, and mining software repositories. HAMZA TURABIEH received the B.A. and M.Sc. degrees in computer science from Balqa Applied University, Jordan, in 2004 and 2006, respectively, and the Ph.D. degree from National University of Malaysia (UKM), in 2010. He is currently an Associate Professor with the Department of Computer Science, Faculty of Science and Information Technology, Taif University. His research interests include interface of computer science and operational research, intelligent decision support systems, search and optimization (combinatorial optimization, constraint optimization, multi-modal optimization, and multi-objective optimization) using heuristics, local search, meta-heuristics (in particular memetic algorithms, particle swarm optimization), and hybrid approaches and their theoretical foundations.
doi:10.1109/access.2021.3052149 fatcat:6qhesbgztnahfmotntsw2fteni