Combining Models for Improved Fault Localization in Spreadsheets

Birgit Hofer, Andrea Höfler, Franz Wotawa

Research output: Contribution to journalArticlepeer-review


Spreadsheets are the most prominent example of end-user programming, but
they unfortunately are often erroneous and thus they compute wrong values. Localizing the true cause of such an observed misbehavior can be cumbersome and frustrating especially for large spreadsheets. Therefore, supporting techniques and tools for fault localization are highly required. Model-based software debugging (MBSD) is a well-known technique for fault localization in software written in imperative and object-oriented programming languages like C, C++ and Java. In this paper, we explain how to use MBSD for fault localization in spreadsheets and compare three types of models for MBSD, namely the value-based model, the dependency-based model and an improved version of the dependency-based model. Whereas the value-based model computes the lowest number of diagnoses, both dependency-based models convince by their low computational complexity. Hence, a combination of these two types of models is desired, and we present a solution that combines value-based and dependency-based models in this paper. Moreover, we discuss a detailed evaluation of the models and the combined approach, which indicates that the combined approach computes the same number of diagnoses like the value-based models while requiring less computation time. Hence, the proposed approach is more appropriate to be used in tools for fault localization in spreadsheets.
Original languageEnglish
Pages (from-to)38-53
Number of pages16
JournalIEEE Transactions on Reliability
Issue number1
Publication statusPublished - 2017


  • Cognition
  • Computational modeling
  • Debugging
  • Java
  • Object oriented modeling
  • Software debugging
  • Fault diagnosis
  • fault location
  • model-based diagnosis (MBD)
  • reasoning about programs
  • spreadsheet programs

Fields of Expertise

  • Information, Communication & Computing


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