Abstract: “A bug in a software application may be a requirement bug, development bug, testing bug or security bug, etc. To predict the bug numbers accurately is a challenging task. Both end users and software developers get benefit by predicting the number of bugs in a new version of software application in advance. The choice of predicting models becomes an important factor for improving the prediction accuracy. This paper provides a combination methodology that combines ARIMA and ANN models for predicting the bug numbers in advance. This method is examined using bug number data for Debian which is publicly available. This paper also gives a comparative analysis of forecasting performance of hybrid Nonlinear ARIMA, ARIMA and ANN models. Empirical results indicate that an Nonlinear ARIMA model can improve the prediction accuracy.”

Keywords: Debian, Bug, Bug Pattern, Artificial Neural Network, ARIMA, Hybrid Model