Reducing Error in Spreadsheets: Example Driven Modeling Versus Traditional Programming

In this paper we present experimental data supporting an alternative approach to developing decision support spreadsheets using a Programming by Demonstration (PbD) paradigm. This technique is coined “Example Driven Modelling” and uses example data (attribute classifications) in combination with inductive machine learning to create decision support models as an alternative to spreadsheet programming. In this experiment we examine whether participants can define attribute classifications (“example-giving”) satisfactorily and describe benefits and limitations this method offers through statistical analysis of the experimental results. We then consider the wider implications of this research in traditional programming.