Boosting Spectrum-Based Fault Localization for Spreadsheets with Product Metrics in a Learning Approach

Adil Mukhtar, Birgit Gertraud Hofer, Dietmar Jannach, Franz Wotawa, Konstantin Schekotihin

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review


Faults in spreadsheets are not uncommon and they can have significant negative consequences in practice. Various approaches for fault localization were proposed in recent years, among them techniques that transferred ideas from spectrum-based fault localization (SFL) to the spreadsheet domain. Applying SFL to spreadsheets proved to be effective, but has certain limitations. Specifically, the constrained computational structures of spreadsheets may lead to large sets of cells that have the same assumed fault probability according to SFL and thus have to be inspected manually. In this work, we propose to combine SFL with a fault prediction method based on spreadsheet metrics in a machine learning (ML) approach. In particular, we train supervised ML models using two orthogonal types of features: (i) variables that are used to compute similarity coefficients in SFL and (ii) spreadsheet metrics that have shown to be good predictors for faulty formulas in previous work. Experiments with a widely-used corpus of faulty spreadsheets indicate that the combined model helps to significantly improve fault localization performance in terms of wasted effort and accuracy.
Original languageEnglish
Title of host publicationASE '22: Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering
Subtitle of host publicationiWOAR 2022 - 7th International Workshop on Sensor-Based Activity Recognition and Artificial Intelligence
PublisherAssociation of Computing Machinery
Number of pages5
ISBN (Electronic)978-1-4503-9475-8
Publication statusPublished - 19 Sept 2022
Event37th IEEE/ACM International Conference on Automated Software Engineering: ASE '22 - Rochester, United States
Duration: 10 Oct 202214 Oct 2022

Publication series

NameACM International Conference Proceeding Series


Conference37th IEEE/ACM International Conference on Automated Software Engineering
Abbreviated titleASE '22
Country/TerritoryUnited States


  • Spreadsheets
  • Spectrum-based Fault Localization
  • Artificial Intelligence
  • machine learning
  • Machine Learning

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

Fields of Expertise

  • Information, Communication & Computing


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