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For more than three decades, Box and Jenkins' Auto-Regressive Integrated Moving Average (ARIMA) technique has been one of the most widely used linear models in time series forecasting. However, it is well documented that many software failure observations are nonlinear and ARIMA is a general univariate model developed based on the assumption that the time series data being predicted are linear. Therefore,...
With recent strong emphasis on rapid development of information technology, the decisions made on the basis of early software reliability estimation can have greatest impact on schedules and cost of software projects. Software reliability prediction models is very helpful for developers and testers to know the phase in which corrective action need to be performed in order to achieve target reliability...
Support vector machine (SVM) is a new method based on statistical learning theory. It has been successfully used to solve nonlinear regression and time series problems. However, SVM has rarely been applied to software reliability prediction. In this study, an SVM-based model for software reliability forecasting is proposed. In addition, the parameters of SVM are determined by Genetic Algorithm (GA)...
In current software reliability modeling research, the main concern is how to develop general prediction models. In this paper, we propose several improvements on the conventional software reliability growth models (SRGMs) to describe actual software development process by eliminating some unrealistic assumptions. Most of these models have focused on the failure detection process and not given equal...
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