Thomas Schmitz

Patrick Koch, Konstantin Schekotihin, Dietmar Jannach et al.

Spreadsheets are commonly used in organizations as a programming tool for business-related calculations and decision making. Since faults in spreadsheets can have severe business impacts, a number of approaches from general software engineering have been applied to spreadsheets in recent years, among them the concept of code smells. Smells can in particular be used for the task of fault prediction. An analysis of existing spreadsheet smells, however, revealed that the predictive power of individual smells can be limited. In this work we therefore propose a machine learning based approach which combines the predictions of individual smells by using an AdaBoost ensemble classifier. Experiments on two public datasets containing real-world spreadsheet faults show significant improvements in terms of fault prediction accuracy.

Dietmar Jannach, Thomas Schmitz

A number of automated techniques and tools were proposed in the research literature over the years which aim to support the spreadsheet developer in the process of testing and debugging a faulty spreadsheet. One underlying assumption of many of these approaches is that the spreadsheet developer is capable of providing test cases or is at least reliably able to determine whether a calculated value in a certain cell is correct given the current set of inputs. Since real-world spreadsheets can be complex, we argue that these assumptions might be too strong in some situations. We therefore propose to support the user during testing and debugging by automatically computing spreadsheet fragments of manageable size. The spreadsheet developer can then verify the correctness of a smaller set of formulas for which the calculated output can be more easily validated.