Reducing Error in Spreadsheets: Example Driven Modeling Versus Traditional Programming

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