A novel approach to formulae production and overconfidence measurement to reduce risk in spreadsheet modelling

Research on formulae production in spreadsheets has established the practice as high risk yet unrecognised as such by industry. There are numerous software applications that are designed to audit formulae and find errors. However these are all post creation, designed to catch errors before the spreadsheet is deployed. As a general conclusion from EuSpRIG 2003 conference it was decided that the time has come to attempt novel solutions based on an understanding of human factors. Hence in this paper we examine one such possibility namely a novel example driven modelling approach. We discuss a control experiment that compares example driven modelling against traditional approaches over several progressively more difficult tests. The results are very interesting and certainly point to the value of further investigation of the example driven potential. Lastly we propose a method for statistically analysing the problem of overconfidence in spreadsheet modellers