基于邻域三支决策粗糙集模型的软件缺陷预测方法

摘要

Based on existing software defect data,it is possible to improve the efficiency of software testing and reduce the test cost by establishing the classification model to predict the software modules.Most machine learning based defect prediction researches are based on two-way decision method.Since software defect prediction can be seen as a kind of cost-sensitive learning problem,and the software data has continuous values,this paper proposes a classification method based on neighborhood three-way decision-theoretic rough set model.For ambiguous testing modules,compared with two-way decision methods,this method makes a deferment decision to reduce the misclassification rate.Experimental results on NASA software datasets show that the proposed method can get a higher classification accuracy and a lower misclassification cost.%基于已有软件缺陷数据,建立分类模型对待测软件模块进行预测,能够提高测试效率和降低测试成本.现有基于机器学习方法对软件缺陷预测的研究大部分基于二支决策方式,存在误分率较高等问题.本文针对软件缺陷数据具有代价敏感特性且软件度量取值为连续值等特性,提出了一种基于邻域三支决策粗糙集模型的软件缺陷预测方法,该方法对易分错的待测软件模块作出延迟决策,和二支决策方法相比,降低了误分类率.在NASA软件数据集上的实验表明所提方法能够提高分类正确率并减小误分类代价.