Faculty Publications

Permanent URI for this communityhttps://idr.nitk.ac.in/handle/123456789/18736

Publications by NITK Faculty

Browse

Author: search.filters.author.Murulidhar, N.N.

Search Results

Now showing 1 - 7 of 7
  • No Thumbnail Available
    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.
  • No Thumbnail Available
    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.
  • No Thumbnail Available
    Item
    Analysis of systems subject to on - Line preventive maintenance
    (International Society of Science and Applied Technologies issatconferences.org, 2014) Murulidhar, N.N.
    This paper deals with the analysis of systems subject to on-line preventive maintenance. An expression for the system reliability of a one unit system subject to on-line PM action is obtained by using regeneration point technique.
  • No Thumbnail Available
    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.
  • No Thumbnail Available
    Item
    Novel Software Reliability Estimate for Exponential Class Models
    (International Society of Science and Applied Technologies, 2022) Murulidhar, N.N.; Tantri, B.R.
    Increasing usage of software in every domain has raised concern over its quality and durability. Many indicators for measuring the quality and durability of the software exist. One such indicator is the software reliability, which is a measure of the life time of the software. Estimation of software reliability enables the users of the software to decide whether or not to accept the software. Knowing the probability distribution of the failure times of the software, the reliability of the software can be estimated. Herein, software reliability models having exponential failure times have been considered. The reliability has been estimated by considering the methods of Maximum Likelihood Estimation (MLE) and Minimum Variance Unbiased Estimation (MVUE). The two estimators are combined to obtain the Improved Estimator (IM). Few data sets have been considered and the estimates have been obtained using the said three methods. The three estimators are then compared using the coefficient of variation. It is observed that the Improved Estimator possesses the least value of coefficient of variation, thus indicating that the Improved Estimator is better as compared to the other two estimators and hence provides more accurate estimate of reliability. © 2022 International Society of Science and Applied Technologies
  • No Thumbnail Available
    Item
    Improved Estimator of Software Reliability for Weibull Class Models
    (International Society of Science and Applied Technologies, 2023) Murulidhar, N.N.; Tantri, B.R.
    Increase in the usage of software in every field has resulted in having concern over its quality and durability. Research in this area is still of importance and many researchers are still working towards the improvement in the reliability of the software products. Measures of quality in terms of reliability are vast and obtaining the estimate of reliability would provide more insight into the durability and hence in assessing the performance of the software. Software reliability models are widely used in this estimation process. Most of the failure data models fall into Weibull class models, in which, the failures times are assumed to be distributed as Weibull. Herein, such Weibull class software reliability models are considered. It is intended to combine two well-known estimators, viz, the Maximum Likelihood Estimator and the Minimum Variance Unbiased Estimator. Both estimators have their own pros and cons, in terms of the properties satisfied by them. Herein, it is intended to preserve the statistical properties satisfied by both the estimators by combining them to get an 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 considering the quartile coefficient of dispersion of the three estimators. Some bench mark failure data are considered to establish the efficiency of the improved estimator. © RQD 2023. All rights reserved.All right reserved.
  • No Thumbnail Available
    Item
    This paper deals with the cost analysis of a two-unit repairable system subject to on-line preventive maintenance (on-line PM) and/or repair. The policy adopted here is that the on-line PM work of the operating unit is undertaken first on its completion, the repair work of the failed unit, if any, is subsequently carried out. All the random variables that arise in the analysis are assumed to be independently and arbitrarily distributed. An expression for the expected total cost incurred by the system in a specified time interval is obtained by considering the expected busy period of the server spent on various actions. The analysis is carried out using the regeneration point technique. © 1992.
    (Cost analysis of a two-unit repairable system subject to on-line preventive maintenance and/or repair) Gopalan, M.N.; Bhanu, K.S.; Murulidhar, N.N.
    1992