An Empirical Study on Software Defect Prediction Using Over-Sampling by SMOTE

作者：Pak, Cholmyong^*; Wang, Tian Tian; Su, Xiao Hong

来源：International Journal of Software Engineering and Knowledge Engineering, 2018, 28(6): 811-830.

DOI：10.1142/S0218194018500237

• Software defect prediction
• class-imbalance
• fault prediction
• over-sampling
• SMOTE

Software defect prediction suffers from the class-imbalance. Solving the class-imbalance is more important for improving the prediction performance. SMOTE is a useful over-sampling method which solves the class-imbalance. In this paper, we study about some problems that faced in software defect prediction using SMOTE algorithm. We perform experiments for investigating how they, the percentage of appended minority class and the number of nearest neighbors, influence the prediction performance, and compare the performance of classifiers. We use paired t-test to test the statistical significance of results. Also, we introduce the effectiveness and ineffectiveness of over-sampling, and evaluation criteria for evaluating if an over-sampling is effective or not. We use those concepts to evaluate the results in accordance with the evaluation criteria for the effectiveness of over-sampling.
The results show that they, the percentage of appended minority class and the number of nearest neighbors, influence the prediction performance, and show that the over-sampling by SMOTE is effective in several classifiers.

• 出版日期2018-6
• 单位哈尔滨工业大学