Abstract
Software reliability models (SRMs) are used to assess software reliability and to control quantitatively software testing. In this paper we consider metrics-based SRMs and tackle a statistical estimation of both software test process and product reliability simultaneously. The basic idea is to apply the Markov-dependent Poisson regression to describe the random testing environment by a discrete-time Markov chain. We formulate four Markov-dependent Poisson regression-based SRMs and develop the EM (expectation-maximization) algorithms to estimate the maximum likelihood estimates of model parameters. Numerical examples with real software project data show that our approach is useful to quantify both of software test process and software product reliability, and can answer the question why the stability of test process can lead to the improvement of software product reliability.
Published Version
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