基于采样的半监督支持向量机软件缺陷预测方法  

Software defect prediction using semi-supervised support vector machine with sampling

作　　者：廖胜平[1] 徐玲[1] 鄢萌[1] LIAO Shengping;XU Ling;YAN Meng(School of Software Engineering, Chongqing University, Chongqing 401331, China)

机构地区：[1]重庆大学软件学院,重庆401331

出　　处：《计算机工程与应用》2017年第14期161-166,共6页Computer Engineering and Applications

基　　金：国家自然科学重点基金(No.91118005);重庆市研究生科研创新项目(No.CYS14008)

摘　　要：软件缺陷预测有助于提高软件开发质量,保证测试资源有效分配。针对软件缺陷预测研究中类标签数据难以获取和类不平衡分布问题,提出基于采样的半监督支持向量机预测模型。该模型采用无监督的采样技术,确保带标签样本数据中缺陷样本数量不会过低,使用半监督支持向量机方法,在少量带标签样本数据基础上利用无标签数据信息构建预测模型;使用公开的NASA软件缺陷预测数据集进行仿真实验。实验结果表明提出的方法与现有半监督方法相比,在综合评价指标F值和召回率上均优于现有方法;与有监督方法相比,能在学习样本较少的情况下取得相当的预测性能。Software defect prediction is helpful to improve the quality of software and effectively allocate test resources.To tackle two practical yet important issues in software defect prediction:labeled data is hard to be collected and classimbalance,a sample based semi-supervised support vector machine method is proposed.This method uses an unsupervisedsample approach to sample a small percentage of modules to be tested and labeled,and this sample method canensure that the defect instances in training sets are not too few.Semi-supervised support vector machine algorithm usesfew labeled data combined with unlabeled to build predictor so that the model can exploit the information of unlabeleddata.In the evaluation on four NASA projects,the experimental results show that the proposed approach achieves comparableperformance compared with supervised learning models,but uses little defect information.Moreover,proposedmethod’s performance is better than other semi-supervised learning methods in terms of recall and F-measure.

关 键 词：软件缺陷预测 半监督 SAFE 半监督支持向量机(S4VM) 类不平衡 采样 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]