Master data

Title: Fragment-based spreadsheet debugging
Subtitle:
Abstract:

Faults in spreadsheets can represent a major risk for businesses. To minimize such risks, various automated testing and debugging approaches for spreadsheets were proposed. In such approaches, often one main assumption is that the spreadsheet developer is able to indicate if the outcomes of certain calculations correspond to the intended values. This, however, might require that the user performs calculations manually, a process which can easily become tedious and error-prone for more complex spreadsheets. In this work, we propose an interactive spreadsheet algorithmic debugging method, which is based on partitioning the spreadsheet into fragments. Test cases can then be automatically or manually created for each of these smaller fragments, whose correctness or faultiness can be easier assessed by users than test cases that cover the entire spreadsheet. The annotated test cases are then fed into an algorithmic debugging technique, which returns a set of formulas that could have caused any observed failures, i.e., discrepancies between the expected and computed calculation outcomes. Simulation experiments demonstrate that the suggested decomposition approach can speed up the algorithmic debugging process and significantly reduce the number of fault candidates returned by the algorithm. An additional laboratory study shows that fragmenting a spreadsheet with our method furthermore reduces the time needed by users for creating test cases for a spreadsheet

Keywords:
Publication type: Article in journal (Authorship)
Publication date: 22.12.2018 (Online)
Published by: Automated Software Engineering
Automated Software Engineering
to publication
 ( Springer; S. Burgmaier )
Title of the series: -
Volume number: -
Issue: -
First publication: Yes
Version: -
Page: pp. 1 - 37

Versionen

Keine Version vorhanden
Publication date: 22.12.2018
ISBN (e-book): -
eISSN: -
DOI: -
Homepage: https://link.springer.com/article/10.1007%2Fs10515-018-0250-9
Open access
  • Appeared in open access journal

Assignment

Organisation Address
Fakultät für Technische Wissenschaften
 
Institut für Artificial Intelligence und Cybersecurity
Universitätsstr. 65-67
A-9020 Klagenfurt
Austria
  -993705
   aics-office@aau.at
https://www.aau.at/en/aics/
To organisation
Universitätsstr. 65-67
AT - A-9020  Klagenfurt

Categorisation

Subject areas
  • 1020 - Computer Sciences
Research Cluster No research Research Cluster selected
Citation index
  • Science Citation Index Expanded (SCI Expanded)
Information about the citation index: Master Journal List
Peer reviewed
  • Yes
Publication focus
  • Science to Science (Quality indicator: II)
Classification raster of the assigned organisational units:
working groups
  • Intelligente Systeme und Wirtschaftsinformatik

Cooperations

Organisation Address
Technische Universität Graz
Rechbauerstraße 12
8010 Graz
Austria - Styria
Rechbauerstraße 12
AT - 8010  Graz

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