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Maximum likelihood estimation for software reliability with masked failure data
College of Computer Science & Information, Guizhou University, Guiyang 550025, China.
University of Gävle, Faculty of Engineering and Sustainable Development, Department of Industrial Development, IT and Land Management, Industrial economics. University of Gävle, Center for Logistics and Innovative Production.
2013 (Chinese)In: Systems engineering and electronics, ISSN 1001-506X, Vol. 35, no 12, p. 2665-2669Article in journal (Refereed) Published
Abstract [en]

The masked data are the system failure data when the exact cause of the failures might be unknown. That is, it is the subset of components known to contain the component causing system failures. In general, the maximum likelihood estimation (MLE) of parameters are difficult to find when there exist masked data, because superposition non-homogenous Poisson process (NHPP) software reliability model cannot be decomposed into the independent NHPP models. In this paper, the MLE of software reliability from masked data is studied based on superposition NHPP models. Finally, numerical example based on simulation data is given to illustrate a good performance of MLE.

Place, publisher, year, edition, pages
2013. Vol. 35, no 12, p. 2665-2669
Keywords [en]
masked data, non-homogeneous Poisson process (NHPP), software reliability, maximum likelihood estimation
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:hig:diva-15489DOI: 10.3969/j.issn.1001-506X.2013.12.34Scopus ID: 2-s2.0-84891954316OAI: oai:DiVA.org:hig-15489DiVA, id: diva2:654684
Available from: 2013-10-08 Created: 2013-10-08 Last updated: 2018-03-13Bibliographically approved

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Zhao, Ming

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  • apa
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  • nn-NB
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