Abstract
Whilst some software measurement research has been unquestionably successful, other research has struggled to enable expected advances in project and process management. Contributing to this lack of advancement has been the incidence of inappropriate or non-optimal application of various model-building procedures. This obviously raises questions over the validity and reliability of any results obtained as well as the conclusions that may have been drawn regarding the appropriateness of the techniques in question. In this paper we investigate the influence of various data set characteristics and the purpose of analysis on the effectiveness of four model-building techniques---three statistical methods and one neural network method. In order to illustrate the impact of data set characteristics, three separate data sets, drawn from the literature, are used in this analysis. In terms of predictive accuracy, it is shown that no one modeling method is best in every case. Some consideration of the characteristics of data sets should therefore occur before analysis begins, so that the most appropriate modeling method is then used. Moreover, issues other than predictive accuracy may have a significant influence on the selection of model-building methods. These issues are also addressed here and a series of guidelines for selecting among and implementing these and other modeling techniques is discussed.