Classifying feature description for software defect prediction

Abstract

To overcome the limitation of numeric feature description of software modules in Software defect prediction, we propose a novel module description technology, which employs the classifying feature, rather than numerical feature to describe the software module. Firstly, we construct independent classifier on each software metric. Then the classifying results in each feature are used to represent every module. We apply two different feature classifier algorithms (based on mean criterion and minimum error rate criterion, respectively) to obtain the classifying feature description of software modules. By using the proposed description technology, the discrimination of each metric is enlarged distinctly. Also, classifying feature description is simpler compared to numeric description, which would accelerate the speed of prediction model learning and reduce the storage space of massive data sets. Experiment results on four NASA data sets (CM1, KC1, KC2 and PC1) demonstrate the effectiveness of classifying feature description, and our algorithms can significantly improve the performance of software defect prediction.