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ASE 2019

SGUARD: A Feature-Based Clustering Tool for Effective Spreadsheet Defect Detection

Da Li, Huiyan Wang, Chang Xu, Ruiqing Zhang, Shing-Chi Cheung, Xiaoxing Ma

DOI 10.1109/ase.2019.00122dblp

Abstract

Spreadsheets are widely used but subject to various defects. In this paper, we present SGuard to effectively detect spreadsheet defects. SGuard learns spreadsheet features to cluster cells with similar computational semantics, and then refines these clusters to recognize anomalous cells as defects. SGuard well balances the trade-off between the precision (87.8%) and recall rate (71.9%) in the defect detection, and achieves an F-measure of 0.79, exceeding existing spreadsheet defect detection techniques. We introduce the SGuard implementation and its usage by a video presentation (https://youtu.be/gNPmMvQVf5Q), and provide its public download repository (https://github.com/sheetguard/sguard).

BibTeX
@inproceedings{Li-al:ASE19,
  author    = {Da Li and
               Huiyan Wang and
               Chang Xu and
               Ruiqing Zhang and
               Shing{-}Chi Cheung and
               Xiaoxing Ma},
  title     = {{SGUARD:} A {Feature-Based} Clustering Tool for Effective Spreadsheet Defect Detection},
  booktitle = {ASE},
  pages     = {1142--1145},
  publisher = {{IEEE}},
  year      = {2019},
}

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