A random approximate reduct-based ensemble learning approach and its application in software defect prediction

Abstract

Software defect prediction (SDP) is an important research topic in software engineering. It can optimize the allocation of testing resources by indicating the defect-prone software modules. In recent years, ensemble learning has been frequently used in SDP. Ensemble learning can effectively improve the generalization ability of individual learners, but how to increase the diversity of individual learners is still an open issue. In this paper, we propose a random approximate reduct-based ensemble algorithm called ERAR. To increase the diversity of learners generated by ERAR, a novel technique, called random approximate reduct (RAR), is proposed to perturb the attribute space, and the resampling technique is used to perturb the instance space. RAR is derived from the random subspace method and the granular decision entropy-based reduct in rough set theory. We then investigate the application of ERAR in SDP. In order to alleviate the class imbalance issue in SDP, we further design a hybrid mechanism by combining ERAR with the Synthetic Minority Over-sampling Technique (SMOTE). Experimental results on 20 benchmark data sets show that the hybrid mechanism can provide competitive solutions for SDP.