HYBRID BINARY WHALE OPTIMIZATION ALGORITHM BASED ON TAPER SHAPED TRANSFER FUNCTION FOR SOFTWARE DEFECT PREDICTION

Reliability is one of the key factors used to gauge software quality. Software defect prediction (SDP) is one of the most important factors which affects measuring software's reliability. Additionally, the high dimensionality of the features has a direct effect on the accuracy of SDP models. The objective of this paper is to propose a hybrid binary whale optimization algorithm (BWOA) based on taper-shape transfer functions for solving feature selection problems and dimension reduction with a KNN classifier as a new software defect prediction method. In this paper, the values of a real vector that represents the individual encoding have been converted to binary vector by using the four types of Taper-shaped transfer functions to enhance the performance of BWOA to reduce the dimension of the search space. The performance of the suggested method (T-BWOA-KNN) was evaluated using eleven standard software defect prediction datasets from the PROMISE and NASA repositories depending on the K-Nearest Neighbor (KNN) classifier. Seven evaluation metrics have been used to assess the effectiveness of the suggested method. The experimental results have shown that the performance of T-BWOA-KNN produced promising results compared to other methods including ten methods from the literature, four types of T-BWOA with the KNN classifier. In addition, the obtained results are compared and analyzed with other methods from the literature in terms of the average number of selected features (SF) and accuracy rate (ACC) using the Kendall W test. In this paper, a new hybrid software defect prediction method called T-BWOA-KNN has been proposed which is concerned with the feature selection problem. The experimental results have proved that T-BWOA-KNN produced promising performance compared with other methods for most datasets.