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We present a methodology for Bayesian analysis of software quality. We cast our research in the broader context of constructing a causal framework that can include process, product and other diverse sources of information regarding fault introduction during the software development process. In this paper, we discuss the aspect of relating internal product metrics to external quality metrics. Specifically, we build a Bayesian network (BN) model to relate object-oriented software metrics to software fault content and fault proneness. Assuming that the relationship can be described as a generalized linear model, we derive parametric functional forms for the target node conditional distributions, in the BN. These functional forms are shown to be able to represent linear, Poisson and binomial logistic regression. The models are empirically evaluated using a public domain data set from a software subsystem. The results show that our approach produces statistically significant estimations, and that our overall modelling method performs no worse than existing techniques.
Bayesian analysis, Bayesian networks, defects, fault proneness, metrics, object-oriented, regression, software quality

G. J. Pai and J. Bechta Dugan, "Empirical Analysis of Software Fault Content and Fault Proneness Using Bayesian Methods," in IEEE Transactions on Software Engineering, vol. 33, no. , pp. 675-686, 2007.
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