Software Effort Estimation Using NBC and SWR: A Comparison Based on ISBSG Projects

There are many quantitative estimation methods, e.g. linear regression, neural networks, regression trees. Compared to traditional methods, Bayesian networks are being increasingly used in software engineering because their use opens many possibilities. A main feature of Bayesian networks is their capability to combine data and expert knowledge. This paper seeks to reinforce the hypothesis that Bayesian networks are a competitive method for estimating software effort in terms of prediction accuracy. For this purpose a Naive Bayes Classifier (NBC) and a forward Stepwise Regression (SWR) models have been developed from a subset of the ISBSG dataset. Under homogeneous conditions we found similar results provided that the discretization of the continuous variables is thin enough.

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