An investigation of machine learning based prediction systems

Traditionally, researchers have used either off-the-shelf models such as COCOMO, or developed local models using statistical techniques such as stepwise regression, to obtain software effort estimates. More recently, attention has turned to a variety of machine learning methods such as artificial neural networks (ANNs), case-based reasoning (CBR) and rule induction (RI). This paper outlines some comparative research into the use of these three machine learning methods to build software effort prediction systems. We briefly describe each method and then apply the techniques to a dataset of 81 software projects derived from a Canadian software house in the late 1980s. We compare the 1 Chris Schofield is now with Nortel ([email protected]).