Trimming test suites with coincidentally correct test cases for enhancing fault localizations

Xiaozhen Xue, Yulei Pang, Akbar Siami Namin

Research output: Contribution to journalConference article

22 Scopus citations

Abstract

Although empirical studies have demonstrated the usefulness of statistical fault localizations based on code coverage, the effectiveness of these techniques may be deteriorated due to the presence of some undesired circumstances such as the existence of coincidental correctness where one or more passing test cases exercise a faulty statement and thus causing some confusion to decide whether the underlying exercised statement is faulty or not. Fault localizations based on coverage can be improved if all possible instances of coincidental correctness are identified and proper strategies are employed to deal with these troublesome test cases. We introduce a technique to effectively identify coincidentally correct test cases. The proposed technique combines support vector machines and ensemble learning to detect mislabeled test cases, i.e. Coincidentally correct test cases. The ensemble-based support vector machine then can be used to trim a test suite or flip the test status of the coincidental correctness test cases and thus improving the effectiveness of fault localizations.

Original languageEnglish
Article number6899222
Pages (from-to)239-244
Number of pages6
JournalProceedings - International Computer Software and Applications Conference
DOIs
StatePublished - Sep 15 2014
Event38th Annual IEEE Computer Software and Applications Conference, COMPSAC 2014 - Vasteras, Sweden
Duration: Jul 21 2014Jul 25 2014

Keywords

  • coincidentally correct
  • coverage-based faults localization
  • ensemble learning
  • support vector machine

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