计算机技术与发展2017，Vol.27Issue(7)：38-42,5.DOI:10.3969/j.issn.1673-629X.2017.07.009
基于非负稀疏图的协同训练软件缺陷预测
Defect Prediction of Co-training Software with Non-negative Sparse Graph
摘要
Abstract
Software defect prediction is a system reliability assurance method which can improve the quality of software system and optimize the distribution of test resources.When the previous defect labels of modules in software history warehouse are limited,building an effective prediction classifier by using machine learning methods becomes a challenging problem.Aiming at this problem,a co-training algorithm for software defect prediction based on non-negative sparse graph is proposed,which combines with the advantages of the graph-based semi-supervised learning method and the co-training method and estimates the confidence of unlabeled data.A non-negative sparse graph has been constructed by the similarity between the software modules so that the edge of the graph reflects the similarity between samples.Then three classifiers have been employed for co-training.In order to reduce the introduction of noise data,the reliable unlabeled samples have been selected for training by the implicit selection of the three classifiers and the confidence estimation of the categories.The classifiers keep to iteratively updating until the maximum number of iterations has reached or the recognition rates of classifiers have been reduced.Experimental results on NASA MDP datasets show that the proposed method is superior to the representative semi-supervised co-training method.关键词
非负稀疏图/协同训练/半监督学习/软件缺陷预测Key words
non-negative sparse graph/co-training/semi-supervised learning/software defect prediction分类
信息技术与安全科学引用本文复制引用
张志武,荆晓远,吴飞..基于非负稀疏图的协同训练软件缺陷预测[J].计算机技术与发展,2017,27(7):38-42,5.基金项目
国家自然科学基金资助项目(61073113,61272273) （61073113,61272273）
江苏省普通高校研究生科研创新计划项目(CXZZ12_0478) （CXZZ12_0478）
