Faculty Publications
Permanent URI for this communityhttps://idr.nitk.ac.in/handle/123456789/18736
Publications by NITK Faculty
Browse
4 results
Search Results
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.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 TechnologiesItem 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.Item Accuracy Comparison of Logistic Regression and Decision Tree Prediction Models Using Machine Learning Technique(Springer Science and Business Media Deutschland GmbH, 2025) Tantri, B.R.; Bhat, S.With the advancements in data science and machine learning, it has become beneficial for scientists, technologists, social scientists, and businessmen to adopt the latest developments in machine learning into their domains to make important decisions about their problems of interest. The biggest advantage of machine learning algorithms in such fields is their prediction capability. Statistical tools in powerful machine-learning languages like R have led to simpler solutions to more complex problems. Various models are in use in the process of making decisions and predictions. The most commonly used model in many situations is the regression model. Herein, it is intended to use the logistic regression model and the decision tree model in the prediction of binary categorical variables. R programming is used in the development of these prediction models. It is intended to compare the accuracy of the two models by using the confusion matrices. Two different datasets have been used for the prediction using these models and their comparisons. It has been observed that prediction using a decision tree model has a better accuracy as compared to that of a logistic regression model. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.