Duals in spectral fault localization

Lee Naish, Hua Jie Lee

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

17 Citations (Scopus)


Numerous set similarity metrics have been used for ranking 'suspiciousness' of code in spectral fault localization, which uses execution profiles of passed and failed test cases to help locate bugs. Research in data mining has identified several forms of possibly desirable symmetry in similarity metrics. Here we define several forms of 'duals' of metrics, based on these forms of symmetries. Use of these duals, plus some other slight modifications, leads to several new similarity metrics. We show that versions of several previously proposed metrics are optimal, or nearly optimal, for locating single bugs. We also show that a form of duality exists between locating single bugs and locating 'deterministic' bugs (execution of which always results in test case failure). Duals of the various single bug optimal metrics are optimal for locating such bugs. This more theoretical work leads to a conjecture about how different metrics could be chosen for different stages of software development.

Original languageEnglish
Title of host publicationASWEC 2013
Subtitle of host publicationProceedings of the 2013 22nd Australasian Conference on Software Engineering
Place of PublicationPiscataway, NJ
Number of pages9
Publication statusPublished - 2013
Externally publishedYes
Event2013 22nd Australasian Conference on Software Engineering, ASWEC 2013 - Melbourne, VIC, Australia
Duration: 4 Jun 20137 Jun 2013


Other2013 22nd Australasian Conference on Software Engineering, ASWEC 2013
CityMelbourne, VIC


  • debugging
  • fault localization
  • program spectra
  • set similarity


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