Faculty Publications
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Item Reliability Analysis of Exponential Models Based on Skewness and Kurtosis(Springer India, 2015) Roopashri Tantri, B.; Murulidhar, N.N.Every field in modern era is computerized. As the requirements of software increase, competitions among the manufacturers of software also increase. Thus, there is a need for reliable software. Software reliability is defined as the probability of failure-free operation of software in a specified environment for a specific period of time. Thus, if T denotes the failure time of software, then, its reliability, denoted by R(t), is given by R(t) = P(T *gt; t). Various models of software reliability have been developed. One such model is the exponential class model. For such a model, the reliability function is given by R(t) = фe-фt, where ф is the failure rate. Various estimates of reliability have been obtained for this class of models. The most commonly used method is the method of Maximum Likelihood Estimation (MLE). But it is not as efficient as the Minimum Variance Unbiased Estimation (MVUE). In our previous work, we obtained this minimum variance unbiased estimator for the reliability function R(t) and proved its efficiency by comparing it with the Maximum likelihood estimator. We used variance as a measure of comparison. But variance is only a second order measure. In this paper, we are trying to enhance our work further by comparing higher order measures. We are also trying to analyze the same using skewness and kurtosis. © Springer India 2015.Item Machine Learning Approach for Testing the Efficiency of Software Reliability Estimators of Weibull Class Models(Springer Science and Business Media Deutschland GmbH, 2025) Murulidhar, N.N.; Tantri, B.Usage of software in every field has resulted in the concern over its quality and durability. In this regard, there is a need to have a systematic way of assessing the reliability of the software. One such assessment is the estimation of software reliability. Numerous works have been done in estimating the reliability of the software by making use of software failure times. Most of the software failure times follow Weibull distribution. Herein, Weibull models are considered. Two well-known estimators, viz, the Maximum Likelihood Estimator and the Minimum Variance Unbiased Estimator have been obtained and combined to get Improved Estimator, which satisfies maximum number of statistical properties of a good estimator. In addition, the comparison of the three estimators is carried out by means of coefficient of variation, which considers both the mean and the standard deviation. The comparison is further enhanced by applying statistical tests to these estimators. Machine learning, being the most widely used technique in recent times, herein it is intended to use the R programming language, which is considered as a powerful machine learning language, to carry out statistical tests pertaining to these estimators. Few datasets have been considered and the estimates have been tested for comparison using Modified signed-likelihood ratio test for equality of coefficients of variations. The output results have been analyzed to test the significance of the differences between the coefficients of variation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.Item Software reliability estimation of gamma failure time models(Institute of Electrical and Electronics Engineers Inc., 2017) Tantri, B.R.; Murulidhar, N.N.With the increasing role of software in every field, concern has grown over the quality of software products. One such measure of software quality is the reliability, which is the probability of failure-free operation of a computer program in a specified environment for a specified time. Prior to the release of software, failure data are obtained during testing, using which, future reliability of software can be assessed. Reliability assessment can be done using various measures like Mean Time To Failure, failure intensity function, mean value function, etc. To assess the reliability, one should have a mathematical model that describes the behavior of failure with time. Such models are called software reliability models. Several classes of software reliability models have been defined based on the failure time distribution. One such class of models is the gamma failure time models, where failure times are assumed to follow gamma distribution. In this paper, software reliability estimates of gamma failure time models have been obtained using the method of Maximum Likelihood Estimation and method of Minimum Variance Unbiased Estimation. Using these methods, reliability of the software at a future time point can be estimated. Case studies have been considered to compare the two estimates. © 2016 IEEE.