Paper Info
Title
Software defect prediction based on enhanced metaheuristic feature selection optimization and a hybrid deep neural network
Abstract
Software defect prediction aims to identify the potential defects of new software modules in advance by constructing an effective prediction model. However, the model performance is susceptible to irrelevant and redundant features. In addition, previous studies mainly use traditional data mining or machine learning techniques for defect prediction, the prediction performance is not superior enough. For the first issue, motivated by the idea of search based software engineering, we leverage the recently proposed whale optimization algorithm (WOA) and another complementary simulated annealing (SA) to construct an enhanced metaheuristic search based feature selection algorithm named EMWS, which can effectively select fewer but closely related representative features. For the second issue, we employ a hybrid deep neural network — convolutional neural network (CNN) and kernel extreme learning machine (KELM) to construct a unified defect prediction predictor called WSHCKE, which can further integrate the selected features into the abstract deep semantic features by CNN and boost the prediction performance by taking full advantage of the strong classification capacity of KELM. We conduct extensive experiments for feature selection or extraction and defect prediction across 20 widely-studied software projects on four evaluation indicators. Experimental results demonstrate the superiority of EMWS and WSHCKE.
Year
DOI
Venue
2021
10.1016/j.jss.2021.111026
Journal of Systems and Software
Keywords
DocType
Volume
Software defect prediction,Metaheuristic feature selection,Whale optimization algorithm,Convolutional neural network,Kernel extreme learning machine
Journal
180
ISSN
Citations 
PageRank 
0164-1212
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Kun Zhu111.69
Shi Ying233431.11
Nana Zhang311.69
Dandan Zhu411.69