Software Reliability Analysis Using Parametric and NonParametric

In this paper, we provide a detailed comparison between various models that have been provided in literature for predicting faults in the software testing process. They are commonly known as software reliability models. In this paper we discuss an experimental evaluation of software reliability analysis using parametric and non-parametric methods. The experimental data set for different, small and large projects were used. We used the normalized root mean square error (NRMSE) as evaluation criteria. The experiments show that the non-parametric models are superior when compared to the parametric models in their ability to provide an accurate estimate when historical data is missing. A comparison among the power, exponential, S-Shape parametric, regression models and the neural network and fuzzy logic models are provided. 2. Software reliability model selections Selection of a particular model is a challenging problem for software reliability prediction. There are two reasons for that. They are the selection of the release time and the value of resource allocation decision. These factors can affect the accuracy of the prediction. In the past, several solutions have been proposed to address the solution for the above-described problems. They are [6]