Applying machine learning to solve an estimation problem in software inspections

Abstract

We use Bayesian neural networktec hniques to estimate the number of defects in a software document based on the outcome of an inspection of the document. Our neural networks clearly outperform standard methods from software engineering for estimating the defect content. We also show that selecting the right subset of features largely improves the predictive performance of the networks. © Springer-Verlag Berlin Heidelberg 2002.