文献类型：会议论文

英文题名：Change Point Reliability Modelling for Open Source Software with Masked Data Using Expectation Maximization Algorithm

作者：Yang, Jianfeng Wang, Xibin Huo, Yujia Cai, Jing

第一作者：Yang, Jianfeng;杨剑锋

机构：[1] School of Data Science, Guizhou Institute of Technology, Guiyang, China; [2] Special Key Laboratory of Artificial Intelligence and Intelligent Control of Guizhou Province, Guiyang, China; [3] College of Data Science and Information Engineering, Guizhou Minzu University, Guiyang, China

第一机构：贵州理工学院

会议论文集：2020 Global Reliability and Prognostics and Health Management, PHM-Shanghai 2020

会议日期：October 16, 2020 - October 18, 2020

会议地点：Shanghai, China

语种：英文

外文关键词：Image segmentation - Maximum likelihood estimation - Maximum principle - Open Data - Open source software - Open systems - Reliability analysis

年份：2020

摘要：Masked data is a common missing failure data in reliability engineering. In this paper, multiple change points (CPs) software reliability growth model (SRGM) based on nonhomogeneous Poisson process (NHPP) is proposed using masked data. The C-Chart technology is used to estimate the position of the change point during the software failure process. Moreover, the maximum likelihood estimation (MLE) process of the model parameters is derived in detail, and Expectation Maximization (EM) algorithm is used to solve the likelihood function complicated problem. Finally, using the Tomcat 5 software failure data to conduct a comparative analysis of model performance, the results show that the proposed reliability model is useful and powerful. ? 2020 IEEE.