Estimating the parameters of a non-homogeneous Poisson-process model for software reliability

A stochastic model (G-O) for the software failure phenomenon based on a nonhomogeneous Poisson process (NHPP) was suggested by Goel and Okumoto (1979). This model has been widely used but some important work remains undone on estimating the parameters. The authors present a necessary and sufficient condition for the likelihood estimates to be finite, positive, and unique. A modification of the G-O model is suggested. The performance measures and parametric inferences of the new model are discussed. The results of the new model are applied to real software failure data and compared with G-O and Jelinski-Moranda models