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Spreadsheet users are often unaware of the risks imposed by poorly designedspreadsheets. One way to assess spreadsheet quality is to detect smells whichattempt to identify parts of spreadsheets that are hard to comprehend ormaintain and which are more likely to be the root source of bugs.Unfortunately, current spreadsheet smell detection techniques suffer from anumber of drawbacks that lead to incorrect or redundant smell reports. Forexample, the same quality issue is often reported for every copy of a cell,which may overwhelm users. To deal with these issues, we propose to refinespreadsheet smells by exploiting inferred structural information for smelldetection. We therefore first provide a detailed description of our staticanalysis approach to infer clusters and blocks of related cells. We thenelaborate on how to improve existing smells by providing three examplerefinements of existing smells that incorporate information about cell groupsand computation blocks. Furthermore, we propose three novel smell detectiontechniques that make use of the inferred spreadsheet structures. Empiricalevaluation of the proposed techniques suggests that the refinementssuccessfully reduce the number of incorrectly and redundantly reported smells,and novel deficits are revealed by the newly introduced smells.

On the Refinement of Spreadsheet Smells by means of Structure Information