A multi-population coevolutionary approach for Software defect prediction with imbalanced data

Abstract

This paper proposes a cooperative coevolutionary approach namely COESDP to the software defect prediction (SDP) problem. The proposed method consists of three main phases. The first one conducts data preprocessing including data sampling and cleaning. The second phase utilizes a multi-population coevolutionary approach (MPCA) to find out optimal instance selection solutions. These first two phases help to deal with the imbalanced data challenge of the SDP problem. While the data sampling method aids in the creation of a more balanced data set, MPCA supports in the elimination of unnecessary data samples (or instances) and the selection of crucial instances. The output of phase 2 is a set of different optimal solutions. Each solution is a way of selecting instances from which to create a classifier (or weak learners). Phase 3 utilizes an ensemble learning method to combine these weak learners and produce the final result. The proposed algorithm is compared with conventional machine learning algorithms, ensemble learning algorithms, computational intelligence algorithms and an other multi-population algorithm on 6 standard SDP datasets. Experimental results show that the proposed method gives better and more stable results in comparison with other methods and it can tackle the challenge of imbalance in the SDP data.