We develop NHPP models to characterize categorized event data, with application to modeling the discovery process for categorized software defects. Conditioning on the total number of defects, multivariate models are proposed for modeling the defects by type. A latent vector autoregressive structure is used to characterize dependencies among the different types. We show how Bayesian inference can be achieved via MCMC procedures, with a posterior prediction-based L-measure used for model selection. The results are illustrated for defects of different types found during the System Test phase of a large operating system software development project.
By: Zhaohui Liu; Nalini Ravishanker; Bonnie K. Ray
Published in: RC23521 in 2005
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