A typical model audit approach
Integrity and internal control in information systems V
A graphical approach for reducing spreadsheet linking errors
Advanced topics in end user computing
Advanced topics in end user computing
UCheck: A spreadsheet type checker for end users
Journal of Visual Languages and Computing
A critical review of the literature on spreadsheet errors
Decision Support Systems
A visualization-based approach for improving spreadsheet quality
Proceedings of the Warm Up Workshop for ACM/IEEE ICSE 2010
A dynamic graph-based visualization for spreadsheets
HCI '08 Proceedings of the Third IASTED International Conference on Human Computer Interaction
A bidirectional model-driven spreadsheet environment
Proceedings of the 34th International Conference on Software Engineering
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The widespread presence of errors in spreadsheets is now well-established. Quite a few methodological and software approaches have been suggested as ways to reduce spreadsheet errors. However, these approaches are always tailored to particular types of errors. Are such errors, in fact, widespread? A tool that focuses on rare errors is not very appealing. In other fields of error analysis, especially linguistics, it has proven useful to collect corpuses (systematic samples) of errors. This paper presents two corpuses of errors seen in spreadsheet experiments. Hopefully, these corpuses will help us assess the claims of spreadsheet reduction approaches and should guide theory creation and testing.