On the Refinement of Spreadsheet Smells by means of Structure Information

TL;DR

This paper improves spreadsheet smell detection by leveraging structural information to reduce false positives and introduce new, more accurate smell detection techniques, enhancing spreadsheet quality assessment.

Contribution

It introduces a static analysis method to infer spreadsheet structures and refines existing smell detection techniques while proposing new ones based on these structures.

Findings

Refinements reduce incorrect and redundant smell reports.

New smell detection techniques reveal previously unnoticed issues.

Structural analysis improves overall smell detection accuracy.

Abstract

Spreadsheet users are often unaware of the risks imposed by poorly designed spreadsheets. One way to assess spreadsheet quality is to detect smells which attempt to identify parts of spreadsheets that are hard to comprehend or maintain and which are more likely to be the root source of bugs. Unfortunately, current spreadsheet smell detection techniques suffer from a number of drawbacks that lead to incorrect or redundant smell reports. For example, 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 refine spreadsheet smells by exploiting inferred structural information for smell detection. We therefore first provide a detailed description of our static analysis approach to infer clusters and blocks of related cells. We then elaborate on how to improve existing smells by providing three example refinements of existing smells that incorporate information about cell groups and computation blocks. Furthermore, we propose three novel smell detection techniques that make use of the inferred spreadsheet structures. Empirical evaluation of the proposed techniques suggests that the refinements successfully reduce the number of incorrectly and redundantly reported smells, and novel deficits are revealed by the newly introduced smells.