A neural network approach for software reliability growth modeling in the presence of code churn

One of the key assumptions made in most of the time-domain based software reliability growth models is that the complete code for the system is available before testing starts and that the code remains frozen during testing. However, this assumption is often violated in large software projects. Thus, the existing models may not be able to provide an accurate description of the failure process in the presence of code churn. Daal and McIntosh (1992) developed an extended stochastic model by incorporating continuous code churn into a standard Poisson process model and observed an improvement in the model's estimation accuracy. This paper demonstrates the applicability of the neural network approach to the problem of developing an extended software reliability growth model in the face of continuous code churn. In this preliminary study, a comparison is made between two neural network models, one with the code churn information and the other without the code churn information, for the accuracy of fit and the predictive quality using a data set from a large telecommunication system. The preliminary results suggest that the neural network model that incorporates the code churn information is capable of providing a more accurate prediction than the network without the code churn information.