Spreadsheets are considered to be the most widely used programming language in the world, and reports have shown that 90% of real-world spreadsheets contain errors. In this work, we try to identify spreadsheet smells, a concept adapted from software, which consists of a surface indication that usually corresponds to a deeper problem. Our smells have been integrated in a tool, and were computed for a large spreadsheet repository. Finally, the analysis of the results we obtained led to the refinement of our initial catalog.
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