文献类型：期刊文献

英文题名：ELS algorithm for estimating open source software reliability with masked data considering both fault detection and correction processes

作者：Yang, Jianfeng Zhao, Ming Chen, Jing

第一作者：杨剑锋

通信作者：Yang, JF[1]

机构：[1]Guizhou Inst Technol, Sch Data Sci, Guiyang 550003, Peoples R China;[2]Univ Gavle, Fac Technol & Sustainable Dev, Gavle, Sweden;[3]Guizhou Univ Tradit Chinese Med, Coll Informat Engn, Guiyang, Peoples R China

第一机构：贵州理工学院

通信机构：corresponding author), Guizhou Inst Technol, Sch Data Sci, Guiyang 550003, Peoples R China.|贵州理工学院;

年份：0

外文期刊名：COMMUNICATIONS IN STATISTICS-THEORY AND METHODS

收录：;EI(收录号：20210509836706);Scopus(收录号：2-s2.0-85099837488);WOS:【SCI-EXPANDED(收录号:WOS:000611605200001)】；

基金：This work was supported by National Natural Science Foundation of China (No. 71901078) and Science and Technology Foundation of Guizhou, China (No. Qian KeHeJZi[2015]2064).

语种：英文

外文关键词：Masked data; fault detection process; fault correction process; open source software reliability; expectation least squares

摘要：Masked data are the system failure data when the exact cause of the failures might be unknown. That is, the cause of the system failures may be any one of the components. Additionally, to incorporate more information and provide more accurate analysis, modeling software fault detection and correction processes have attracted widespread research attention recently. However, stochastic fault correction time and masked data brings more difficulties in parameter estimation. In this paper, a framework of open source software growth reliability model with masked data considering both fault detection and correction processes is proposed. Furthermore, a novel Expectation Least Squares (ELS) method, an EM-like (Expectation Maximization) algorithm, is used to solve the problem of parameter estimation, because of its mathematical convenience and computational efficiency. It is note that the ELS procedure is easy to use and useful for practical applications, and it just needs more relaxed hidden assumptions. Finally, three data sets from real open source software project are applied to the proposed framework, and the results show that the proposed reliability model is useful and powerful.