Software defect prediction based on weighted extreme learning machine

Abstract

The uncertainty of developers' activity can lead to engineering problems such as increased software defects during software development. Therefore, advanced approaches to discovering software defects are needed to improve software systems by software practitioners. This paper describes a novel fram ework named Weighted Supervised-And-Unsupervised Extreme Learning Machine (WSAU-ELM) including the construction of supervised weighted extreme learning machine for software defect prediction (WELM-SDP) and unsupervised weighted extreme learning machine with spectral clustering for software defect prediction (WELMSC-SDP) that can perform significantly better than the previous software prediction methods. The key advantages of this proposed work are: (i) both the two algorithms can reveal the better learning capability and computational efficiency; (ii) the supervised prediction algorithm is more precisely and faster to handle data sets than the common models, and save more time and resources for software companies; (iii) the unsupervised prediction algorithm can increase accuracy compared to the current method; (iv) the paper also discusses the software defect priority for the defective data, and provides the detailed priority levels that is not discussed before. Experimental results on the benchmark data sets show that the proposed framework is not only more effectively than the existing works, but also can extend the study by the priority analysis of software defects.