Mutation-based data augmentation for software defect prediction

Software defect prediction (SDP) aims to distinguish between defective and nondefective instances, but the imbalance between these two classes often leads to reduced prediction performance. Conventional SDP approaches use oversampling techniques, such as synthetic oversampling, to tackle the problem of imbalanced data. However, these methods merely synthesize new instances based on traditional code features without considering actual defects at the code level. To address the issue of data imbalance while preserving semantic features of code samples, a mutation-based data augmentation approach in SDP is proposed. The method utilizes the mutation operator to generate mutants that mutate nondefective instances and create new defective instances. Six projects from the PROMISE dataset are used to evaluate the approach, employing four traditional and two deep classifiers. The experimental results demonstrate the effectiveness of this method in improving defect prediction performance for both traditional and deep classifiers compared with other data augmentation methods. A novel mutation-based data augmentation method is proposed, in which data are increased at the code level while preserving its semantic features. The method utilizes the mutation operator for generating mutants to mutate against nondefective instances and then generates new defective instances. Experimental results demonstrate that this approach achieves the best average overall performance.