Patrick Koch

Patrick Koch

Spreadsheets are the go-to tool for computerized calculation and modelling, but are hard to comprehend and adapt after reaching a certain complexity. In general, cognition of complex systems is facilitated by having a higher order mental model of the system in question to work with. We therefore present a concept for structure-aware understanding of and interaction with spreadsheets that extends previous work on structure inference in the domain. Following this concept, structural information is used to enrich visualizations, reactively enhance traditional user actions, and provide tools to proactively alter the overall spreadsheet makeup instead of individual cells The intended systems should, in first approximation, not replace common spreadsheet tools, but provide an additional layer of functionality alongside the established interface. In ongoing work, we therefore implemented a tool for structure inference and visualization along the common spreadsheet layout. Based on this framework, we plan to introduce the envisioned proactive and reactive interaction mechanics, and finally provide structure-aware unctionality as an add-in for common spreadsheet processors. We believe that providing the tools for thinking about and interacting with spreadsheets in this manner will benefit users both in terms of productivity and overall spreadsheet quality.

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.