Finding errors in the Enron spreadsheet corpus

Spreadsheet environments like MS Excel are the most widespread type of end-user software development tools and spreadsheet-based applications can be found almost everywhere in organizations. Since spreadsheets are prone to error, several approaches were proposed in the research literature to help users locate formula errors. However, the proposed methods were often designed based on assumptions about the nature of errors and were evaluated with mutations of correct spreadsheets. In this work we propose a method and tool to identify realworld formula errors within the Enron spreadsheet corpus. Our approach is based on heuristics that help us identify versions of the same spreadsheet and our software helps the user identify spreadsheets of which we assume that they contain error corrections. An initial manual inspection of a subset of such candidates led to the identification of more than two dozen formula errors. We publicly share the new collection of real-world spreadsheet errors.