Cost-sensitive Dictionary Learning for Software Defect Prediction

In recent years, software defect prediction has been recognized as a cost-sensitive learning problem. To deal with the unequal misclassification losses resulted by different classification errors, some cost-sensitive dictionary learning methods have been proposed recently. Generally speaking, these methods usually define the misclassification costs to measure the unequal losses and then propose to minimize the cost-sensitive reconstruction loss by embedding the cost information into the reconstruction function of dictionary learning. Although promising performance has been achieved, their cost-sensitive reconstruction functions are not well-designed. In addition, no sufficient attentions are paid to the coding coefficients which can also be helpful to reduce the reconstruction loss. To address these issues, this paper proposes a new cost-sensitive reconstruction loss function and introduces an additional cost-sensitive discrimination regularization for the coding coefficients. Both the two terms are jointly optimized in a unified cost-sensitive dictionary learning framework. By doing so, we can achieve the minimum reconstruction loss and thus obtain a more cost-sensitive dictionary for feature encoding of test data. In the experimental part, we have conducted extensive experiments ontwenty-fivesoftware projects from four benchmark datasets of NASA, AEEEM, ReLink and Jureczko. The results, in comparison withtenstate-of-the-art software defect prediction methods, demonstrate the effectiveness of learned cost-sensitive dictionary for software defect prediction.