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.