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Nonhomogeneous Poisson process (NHPP) and software reliability growth models (SRGM) are a popular approach
to estimate useful metrics such as the number of faults remaining,
failure rate, and reliability, which is defined as the probability of
failure free operation in a specified environment for a specified
period of time. We propose performance-optimized expectation
conditional maximization (ECM) algorithms for NHPP SRGM.
In contrast to the expectation maximization (EM) algorithm, the
ECM algorithm reduces the maximum-likelihood estimation process to multiple simpler conditional maximization (CM)-steps. The
advantage of these CM-steps is that they only need to consider one
variable at a time, enabling implicit solutions to update rules when
a closed form equation is not available for a model parameter. We
compare the performance of our ECM algorithms to previous EM
and ECM algorithms on many datasets from the research literature. Our results indicate that our ECM algorithms achieve two
orders of magnitude speed up over the EM and ECM algorithms
of [1] when their experimental methodology is considered and three
orders of magnitude when knowledge of the maximum-likelihood
estimation is removed, whereas our approach is as much as 60 times
faster than the EM algorithms of [2]. We subsequently propose a
two-stage algorithm to further accelerate performance.
