Premium
NHPP models for categorized software defects
Author(s) -
Liu Zhaohui,
Ravishanker Nalini,
Ray Bonnie K.
Publication year - 2005
Publication title -
applied stochastic models in business and industry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.413
H-Index - 40
eISSN - 1526-4025
pISSN - 1524-1904
DOI - 10.1002/asmb.604
Subject(s) - computer science , markov chain monte carlo , autoregressive model , software , multivariate statistics , model selection , bayesian inference , inference , bayesian probability , data mining , event (particle physics) , artificial intelligence , machine learning , statistics , mathematics , programming language , physics , quantum mechanics
We develop NHPP models to characterize categorized event data, with application to modelling the discovery process for categorized software defects. Conditioning on the total number of defects, multivariate models are proposed for modelling 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. Copyright © 2005 John Wiley & Sons, Ltd.