关键词: 软件缺陷预测, 代价敏感分类, 最优代价因子, 决策树, 集成算法

Abstract: Software defects prediction is considered as an effective means for the optimization of quality assurance activities. Taking into account the different misclassification cost for unknown software modules using the software defects prediction models, this paper proposes the cost-sensitive classification method for constructing software defects prediction models. Firstly, for the code attribute metric data, decision tree algorithm is selected to construct base-
classifiers using cost-sensitive classification method by sampling with replacement of Bagging. Then, the defects prediction model is constructed based on majority rule, and the approach to obtain the approximate optimal cost-factor value is researched. The experimental results on the NASA software defects prediction datasets show that the proposed method is averagely superior to the conventional methods with lower probability of false alarm and higher comprehensive evaluation values.

Key words: software defects prediction, cost-sensitive classification, optimal cost-factor, decision tree, ensemble algorithm