A comparison of techniques for developing predictive models of software metrics
The use of regression analysis to derive predictive equations for software metrics has recently been complemented by increasing numbers of studies using non-traditional methods, such as neural networks, fuzzy logic models, case-based reasoning systems, and regression trees. There has also been an increasing level of sophistication in the regression-based techniques used, including robust regression methods, factor analysis, and more effective validation procedures. This paper examines the implications of using these methods and provides some recommendations as to when they may be appropriate. A comparison of the various techniques is also made in terms of their modelling capabilities with specific reference to software metrics.