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.

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Terminologies:Software reliability [1]: It is the probability of failure free operation of a computer program in a specified environment for a specified period of time. Software reliability models [2]:These describe the behavior of failure with time, by expressing failures as random processes in either times of failure or the number of failures, at fixed times.

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