关键词: 软件缺陷个数预测, 数据不平衡, 回归算法

Abstract:

Focusing on the issue that the existing studies did not consider the imbalanced data distribution problem in defect datasets and employed improper performance measures for evaluating the regression models to evaluate the performance of models for predicting the number of defects, the impact on models for predicting the number of defects of different regression algorithms were explored by using Fault-Percentile-Average (FPA) as the performance measure. Experiments were conducted on six datasets from PROMISE repository to analyze the impact on the models and the difference of ten regression algorithms for predicting the number of defects. The results show that the forecast result of models for predicting the number of defects built by different regression algorithms are various, and Gradient Boosting Regression algorithm and Bayesian Ridge Regression algorithm can achieve better performance as a whole．

Key words: prediction of the number of defects, imbalanced data distribution, regression algorithm