Predicting Software Quality using Bayesian Belief Networks

In the absence of an agreed measure of software quality the density of defects has been a very commonly used surrogate measure. As a result there have been numerous attempts to build models for predicting the number of residual software defects. Typically, the key variables in these models are either size and complexity metrics or measures arising from testing information. There are, however, serious statistical and theoretical difficulties with these approaches. Using Bayesian Belief Networks we can overcome some of the more serious problems by taking account of all the diverse factors implicit in defect prevention, detection and complexity.