计算机应用研究2013，Vol.30Issue(6)：1734-1738,5.DOI:10.3969/j.issn.1001-3695.2013.06.035
软件缺陷集成预测模型研究
Software defect prediction based on classifiers ensemble
摘要
Abstract
Software defect prediction using classification algorithms was advocated by many researchers.However,several new literatures show the performance bottleneck by applying a single classifier recent years.On the other hand,classifiers ensemble can effectively improve classification performance than a single classifier.This paper conducted a comparative study of various ensemble methods with perspective of taxonomy.A series of benchmarking experiments on public-domain datasets MDP show that applying classifiers ensemble methods to predict defect could achieve better performance than using a single classifier.Specially,in all seven ensemble methods evolved by this experiments,voting and random forest have obvious performance superiority than others,and Stacking also has better generalization ability.关键词
软件缺陷预测/集成分类/投票/随机森林Key words
software defect prediction/ classifiers ensemble/ vote/ random forest分类
信息技术与安全科学引用本文复制引用
刘小花,王涛,吴振强..软件缺陷集成预测模型研究[J].计算机应用研究,2013,30(6):1734-1738,5.基金项目
国家自然科学基金面上项目(61173190) （61173190）
陕西省自然科学基础研究计划项目(2009JM8002) （2009JM8002）
中央高校基本科研业务费专项资金资助项目(GK201302055) （GK201302055）
