On Bayesian Inference of Software Reliability Measurement

被引：0
|
作者：
Inoue, Shinji [1 ]
Yamada, Shigeru [2 ]
机构：
[1] Kansai Univ, Fac Informat, 2-1-1 Ryozenji, Takatsuki, Osaka 5691095, Japan
[2] Tottori Univ, Grad Sch Engn, 4-101 Koyama Minami, Tottori, Tottori 6808552, Japan
来源：
2017 INTERNATIONAL CONFERENCE ON INFOCOM TECHNOLOGIES AND UNMANNED SYSTEMS (TRENDS AND FUTURE DIRECTIONS) (ICTUS) | 2017年
关键词：
Software reliability assessment; Bayesian interval estimation; MCMC method; TIME;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
We discuss an interval estimation approach for parameters and software reliability assessment measures, which are derived from a discretized software reliability model. In our approach, we apply the Markov chain Monte Carlo (MCMC) method for conducing Bayesian interval estimations in software reliability assessment. Further, we shows numerical examples of our approach by using actual fault count data.
引用
收藏
页码：116 / 119
页数：4
相关论文
共 50 条
  • [1] Measurement of Software Reliability Using Sequential Bayesian Technique
    Prasad, Lalji
    Gupta, Ankur
    Badoria, Sarita
    [J]. WCECS 2009: WORLD CONGRESS ON ENGINEERING AND COMPUTER SCIENCE, VOLS I AND II, 2009, : 242 - +
  • [2] Software reliability prediction: A machine learning and approximation Bayesian inference approach
    Oveisi, Shahrzad
    Moeini, Ali
    Mirzaei, Sayeh
    Farsi, Mohammad Ali
    [J]. QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, 2024,
  • [3] A Bayesian Inference Tool for NHPP-Based Software Reliability Assessment
    Hirata, Takumi
    Okamura, Hiroyuki
    Dohi, Tadashi
    [J]. FUTURE GENERATION INFORMATION TECHNOLOGY, PROCEEDINGS, 2009, 5899 : 225 - 236
  • [4] Comparative analysis of Bayesian and classical approaches for software reliability measurement
    Wandji, Ketchiozo T.
    Sarkani, Shahryar
    Eveleigh, Timothy
    Holzer, Thomas H.
    Keiller, Peter. A.
    [J]. 2013 IEEE INTERNATIONAL SYMPOSIUM ON SOFTWARE RELIABILITY ENGINEERING WORKSHOPS (ISSREW), 2013, : 9 - +
  • [5] Using the BACC Software for Bayesian Inference
    William J. McCausland
    [J]. Computational Economics, 2004, 23 (3) : 201 - 218
  • [6] Bayesian inference for nonhomogeneous Poisson processes in software reliability models assuming nonmonotonic intensity functions
    Cid, JER
    Achcar, JA
    [J]. COMPUTATIONAL STATISTICS & DATA ANALYSIS, 1999, 32 (02) : 147 - 159
  • [7] SOFTWARE FAILURE PROBABILITY ASSESSMENT BY BAYESIAN INFERENCE
    Park, Gee-Yong
    Eom, Heung-Seop
    Jang, Seung Cheol
    Kang, Hyun Gook
    [J]. NUCLEAR TECHNOLOGY, 2013, 183 (01) : 107 - 118
  • [8] Past, Present and Future of Software for Bayesian Inference
    Strumbelj, Erik
    Bouchard-Cote, Alexandre
    Corander, Jukka
    Gelman, Andrew
    Rue, Havard
    Murray, Lawrence
    Pesonen, Henri
    Plummer, Martyn
    Vehtari, Aki
    [J]. STATISTICAL SCIENCE, 2024, 39 (01) : 46 - 61
  • [9] Comparison of software packages for performing Bayesian inference
    Koprivica M.
    [J]. Neural Network World, 2020, 30 (05): : 283 - 294
  • [10] Bayesian inference from measurement information
    Lira, I
    Kyriazis, G
    [J]. METROLOGIA, 1999, 36 (03) : 163 - 169
12345