Experiences Using Case-Based Reasoning to Predict Software Project Effort

This paper explores some of the practical issues associated with the use of case-based reasoning (CBR) or estimation by analogy. We note that different research teams have reported widely differing results with this technology. Whilst we accept that underlying characteristics of the datasets being used play a major role we also argue that configuring a CBR system can also have an impact. We examine the impact of the choice of number of analogies when making predictions; we also look at different adaptation strategies. Our analysis is based on a dataset of software projects collected by a Canadian software house. Our results show that choosing analogies is important but adaptation strategy appears to be less so. These findings must be tempered, however, with the finding that it was difficult to show statistical significance for smaller datasets even when the accuracy indicators differed quite substantially. For this reason we urge some degree of caution when comparing competing predicti..