Software Reliability Testing Coverage Model Using Feed-Forward Back Propagation Neural Network

The paper presents software reliability growth model (SRGM) with testing coverage which covers both detection as well as correction process under imperfect debugging. The estimation is done using feed forward back propagation artificial neural network (ANN). Many researchers have studied the importance of modelling fault detection instead of modelling fault correction. We have proposed generalised testing coverage model by adopting different testing coverage for both the processes. We have also compared proposed model with existing traditional models based on three failure datasets. Different performance criteria like goodness of fit, accuracy of the model, mean square error (MSE), and coefficient of determination (R-2) are evaluated for the datasets. The comparison results shows that the model proposed in this paper provides more efficient accuracy than the existing traditional models.