Software defect prediction with imbalanced distribution by radius‐synthetic minority over‐sampling technique. Issue 7 (5th June 2021)

• Record Type: Journal Article
• Authors: Guo, Shikai; Dong, Jian; Li, Hui; Wang, Jiahui
• Is Part Of: Journal of software. Volume 33:Issue 7(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-06-05
• Subjects: imbalanced learning -- software defect prediction -- software quality; Software engineering -- Periodicals; Computer software -- Development -- Periodicals; Software maintenance -- Periodicals; 005.1
• DOI: 10.1002/smr.2362
• Languages: English
• ISSNs: 2047-7473

Abstract

Abstract: 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 defect‐prone 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.