Issues on the Effective Use of CBR Technology for Software Project Prediction

This paper explores some of the practical issues associated with the use of case-based reasoning (CBR) or estimation by analogy for software project effort prediction. Different research teams have reported varying experiences with this technology. We take the view that the problems hindering the effective use of CBR technology are twofold. First, the underlying characteristics of the datasets play a major role in determining which prediction technique is likely to be most effective. Second, when CBR is that technique, we find that configuring a CBR system can also have a significant impact upon predictive capabilities. In this paper we examine the performance of CBR when applied to various datasets using stepwise regression (SWR) as a benchmark. We also explore the impact of the choice of number of analogies and the size of the training dataset when making predictions.