An Analysis of the New Reliability Model Based on Bathtub-Shaped Failure Rate Distribution with Application to Failure Data

Abstract

The reliability of software has a tremendous influence on the reliability of systems. Software dependability models are frequently utilized to statistically analyze the reliability of software. Numerous reliability models are based on the nonhomogeneous Poisson method (NHPP). In this respect, in the current study, a novel NHPP model established on the basis of the new power function distribution is suggested. The mathematical formulas for its reliability measurements were found and are visually illustrated. The parameters of the suggested model are assessed utilizing the weighted nonlinear least-squares, maximum-likelihood, and nonlinear least-squares estimation techniques. The model is subsequently verified using a variety of reliability datasets. Four separate criteria were used to assess and compare the estimating techniques. Additionally, the effectiveness of the novel model is assessed and evaluated with two foundation models both objectively and subjectively. The implementation results reveal that our novel model performed well in the failure data that we examined.

An updated software reliability model using the shanker model and failure data

“…It is usually calculated as mean of prediction errors (PEs) and is an important measure to evaluate the accuracy and precision of a predictive model. [45][46][47] The prediction error is calculated as the sum of the differences between the predicted and actual values. The evaluation of a predictive model's accuracy and precision often relies on the calculation of bias and its corresponding standard deviation, which are statistical measures used to determine the variability and reliability of its forecasts.…”

“…Bias is the statistical term for the difference between the expected value of a prediction model's outputs and the true values that the model is attempting to predict. It is usually calculated as mean of prediction errors (PEs) and is an important measure to evaluate the accuracy and precision of a predictive model 45–47 . The prediction error is calculated as the sum of the differences between the predicted and actual values.…”