An iterative scheme for maximum likelihood estimation in software reliability modeling

This paper focuses on an estimation problem of model parameters in software reliability modeling. We introduce the EM (expectation-maximization) algorithms for software reliability models and compare them with the classical parameter estimation methods. Especially, we extensively develop the EM algorithms for two cases; (i) the time interval data of software fault detection are available, (ii) additive software reliability models based on non-homogeneous Poisson processes are used. In numerical examples, we compare the iterative schemes based on the EM algorithms with classical methods such as the Newton's method and the Fisher's scoring method and show that the EM algorithms are attractive in terms of convergence property.