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General Information
    • ISSN: 2301-3559
    • Frequency: Quarterly
    • DOI: 10.18178/LNSE
    • Editor-in-Chief: Prof. Jemal Antidze
    • Executive Editor: Ms. Nina Lee
    • Abstracting/ Indexing: EI (INSPEC, IET), DOAJ, Electronic Journals Library, Engineering & Technology Digital Library, Ulrich's Periodicals Directory, International Computer Science Digital Library (ICSDL), ProQuest and Google Scholar.
    • E-mail: lnse@ejournal.net
Editor-in-chief
Prof. Jemal Antidze
I. Vekua Scientific Institute of Applied Mathematics
Tbilisi State University, Georgia
I'm happy to take on the position of editor in chief of LNSE. We encourage authors to submit papers concerning any branch of Software Engineering.

LNSE 2014 Vol.2(3): 201-204 ISSN: 2301-3559
DOI: 10.7763/LNSE.2014.V2.123

An Efficient Estimator of Reliability for Exponential Class Software Reliability Models

B. Roopashri Tantri and Murulidhar N. N.
Abstract—Software quality has become a major concern of all software manufacturers. One such measure of software quality is the reliability, which is the probability of failure-free operation of a software in a specified environment for a specified time. If T denotes the time to failure of any software, then, the reliability of this software, denoted by R(t), is given by R(t)=P(T>t). The reliability of any software can be estimated using various methods of estimation. The simplest among these methods, is the method of maximum likelihood estimation. Even though it satisfies most of the desirable properties of a good estimator, it is still not as efficient as the minimum variance unbiased estimator. In this paper, the minimum variance unbiased estimator of R(t) for exponential class software reliability models, is obtained using a procedure called blackwellization. The estimator so obtained using this method always has minimum variance. The same is verified for a model belonging to the exponential class, viz, the Jelinski - Moranda model.

Index Terms—Exponential class models, maximum likelihood estimator, minimum variance unbiased estimator, software reliability, software reliability models, variance.

B. Roopashri Tantri is with Nagarjuna College of Engineering and Technology, Bangalore, India (e-mail: roopa_tan@hotmail.com).
Murulidhar N. N. is with National Institute of Technology Karnataka, Surathkal, India (e-mail: murulidharnn@gmail.com).

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Cite: B. Roopashri Tantri and Murulidhar N. N., "An Efficient Estimator of Reliability for Exponential Class Software Reliability Models," Lecture Notes on Software Engineering vol. 2, no. 3, pp. 201-204, 2014.

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