A study on software reliability prediction based on transduction inference

摘要：

Non-parametric statistical methods are applied to verdict that early failure behavior of the testing process may have less impact on later failure process, so it happens in software failure time prediction that one does not have enough information to estimate the software failure process well but do have enough information to estimate the failure data at given instance. The prediction accuracy of software reliability prediction models based on recurrent neural network, feed-forward neural network, relevance vector machine, support vector machine and some nonhomogeneous Poisson process models is compared. Experimental results show that software failure time prediction models based on transduction inference theory could achieve higher prediction accuracy.

关键词： Software reliability prediction Relevance vector machine Support vector machine Transduction

作者: Jungang Lou Jianhui Jiang Chunyan Shuai Ying Wu

作者单位: Department of Computer Science and Technology Tongji University Shanghai 201804, China Institute of Department of Computer Science and Technology Tongji University Shanghai 201804, China

会议类型: 国际会议

会议名称: 2010 19th IEEE Asian Test Symposium(第19届IEEE亚洲测试技术学术会议 ATS 2010)

会议地点: 上海

会议语种:英文

页码: 77-80

在线出版日期: 2010-12-01（万方平台首次上网日期，不代表论文的发表时间）