Mutation-aware fault prediction

David Bowes, Tracy Hall, Mark Harman, Yue Jia, Federica Sarro, Fan Wu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

31 Citations (Scopus)


We introduce mutation-aware fault prediction, which leverages additional guidance from metrics constructed in terms of mutants and the test cases that cover and detect them. We report the results of 12 sets of experiments, applying 4 Different predictive modelling techniques to 3 large real-world systems (both open and closed source). The results show that our proposal can significantly (p ≤ 0:05) improve fault prediction performance. Moreover, mutation-based metrics lie in the top 5% most frequently relied upon fault predictors in 10 of the 12 sets of experiments, and provide the majority of the top ten fault predictors in 9 of the 12 sets of experiments.
Original languageEnglish
Title of host publicationISSTA 2016 - Proceedings of the 25th International Symposium on Software Testing and Analysis
EditorsAndreas Zeller, Abhik Roychoudhury
Place of PublicationSaarbrucken
PublisherACM Press
Number of pages12
ISBN (Electronic)978-145034390-9
Publication statusPublished - 18 Jul 2016
EventISSTA 2016: 25th International Symposium on Software Testing and Analysis - Saarbrucken, Germany
Duration: 18 Jul 201620 Jul 2016


ConferenceISSTA 2016


  • Empirical study
  • Mutation testing
  • Software defect prediction
  • Software fault prediction
  • Software metrics


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