Prediction of software reliability using an auto regressive process

This paper proposes a procedure for predicting software reliability, using an Auto Regressive (AR) model. The parameters of the models are selected using computationally efficient numerical methods like Singular Value Decomposition (SVD) and QR factorization. For better prediction of time between failures, the AR models have been selected using Akaike Information Criterion (AIC) and Schwarz's Information Criterion (SIC). A comparative study with the Jelinski-Moranda (JM) and Schick-Wolverton (SW) models has been performed. Some real life data has been used for illustration purposes.