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ince its introduction in 1977, the expectation maximization (EM) algorithm has been one of the most important and
widely used estimation method in estimating parameters of distributions in the presence of incomplete information. In this paper,
a variant of the EM algorithm, the expectation conditional maximization (ECM) algorithm, is introduced for the first time and
it provides a promising alternative in estimating the parameters
of nonhomogeneous poisson (NHPP) software reliability growth
models (SRGM). This algorithm circumvents the difficult M-step
of the EM algorithm by replacing it by a series of conditional maximization steps. The utility of the ECM approach is demonstrated in
the estimation of parameters of several well-known models for both
time domain and time interval software failure data. Numerical examples with real-data indicate that the ECM algorithm performs
well in estimating parameters ofNHPP SRGM with complex mean
value functions and can produce a faster rate of convergence.
