Software defect prediction with imbalanced distribution by radius-synthetic minority over-sampling technique

摘要

Software defect prediction, which can identify the defect-prone modules, is an effective technology to ensure the quality of software products. Due to the importance in software maintenance, many learning-based software defect prediction models are presented in recent years. Actually, the defects usually occupy a very small proportions in software source codes; thus, the imbalanced distributions between defectprone modules and non-defect-prone modules increase the learning difficulty of the classification task. To address this issue, we present a random over-sampling mechanism used to generate minority-class samples from high-dimensional sampling space to deal with the imbalanced distributions in software defect prediction, in which two constraints are applied to provide a robust way to generate new synthetic samples, that is, scaling the random over-sampling scope to a reasonable area and distinguishing the majority-class samples in a critical region. Based on nine open datasets of software projects, we experimentally verify that our presented method is effective on predict the defect-prone modules, and the effect is superior to the traditional imbalanced processing methods.