An Improved SDA Based Defect Prediction Framework for Both Within-Project and Cross-Project Class-Imbalance Problems

Abstract

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Background.</i> Solving the class-imbalance problem of within-project software defect prediction (SDP) is an important research topic. Although some class-imbalance learning methods have been presented, there exists room for improvement. For cross-project SDP, we found that the class-imbalanced source usually leads to misclassification of defective instances. However, only one work has paid attention to this cross-project class-imbalance problem. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Objective.</i> We aim to provide effective solutions for both within-project and cross-project class-imbalance problems. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Method.</i> Subclass discriminant analysis (SDA), an effective feature learning method, is introduced to solve the problems. It can learn features with more powerful classification ability from original metrics. For within-project prediction, we improve SDA for achieving balanced subclasses and propose the improved SDA (ISDA) approach. For cross-project prediction, we employ the semi-supervised transfer component analysis (SSTCA) method to make the distributions of source and target data consistent, and propose the SSTCA+ISDA prediction approach. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Results</i> . Extensive experiments on four widely used datasets indicate that: 1) ISDA-based solution performs better than other state-of-the-art methods for within-project class-imbalance problem; 2) SSTCA+ISDA proposed for cross-project class-imbalance problem significantly outperforms related methods. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> Conclusion</i> . Within-project and cross-project class-imbalance problems greatly affect prediction performance, and we provide a unified and effective prediction framework for both problems.