基于半监督集成学习的软件缺陷预测

Semi-supervised Ensemble Learning Based Software Defect Prediction

在软件缺陷预测中,标记样本不足与类不平衡问题会影响预测结果.为了解决这些问题,文中提出基于半监督集成学习的软件缺陷预测方法.该方法利用大量存在的未标记样本进行学习,得到较好的分类器,同时能集成一系列弱分类器,减少多数类数据对预测产生的偏倚.考虑到预测风险成本问题,文中还采用训练样本集权重向量更新策略,降低有缺陷模块预测为无缺陷模块的风险.在NASA MDP数据集上的对比实验表明,文中方法具有较好的预测效果.

The software defect prediction is usually adversely affected by the limitation of the labeled modules and the class-imbalance of software defect data. Aiming at this problem, a semi-supervised ensemble learning software defect prediction approach is proposed. High-performance classifiers can be built through semi-supervised ensemble learning by using a large amount of unlabeled modules and a better prediction capability is achieved for class-imbalanced data by using a series of weak classifiers to reduce the bias generated by the majority class. With the consideration of the cost of risk in software defect prediction, a sample weight vector updating strategy is employed to reduce the cost of risk caused by misclassifying defective modules as non-defective ones. Experimental results on NASA MDP datasets show better software defect prediction capability of the proposed approach.