Micro interaction metrics for defect prediction

Taek Lee, Dong Gyun Han, Sunghun Kim, Hoh Peter In

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

    114 Citations (Scopus)

    Abstract

    There is a common belief that developers'behavioral interaction patterns may affect software quality. However, widely used defect prediction metrics such as source code metrics, change churns, and the number of previous defects do not capture developers'direct interactions. We propose 56 novel micro interaction metrics (MIMs) that leverage developers'interaction information stored in the Mylyn data. Mylyn is an Eclipse plug-in, which captures developers'interactions such as file editing and selection events with time spent. To evaluate the performance of MIMs in defect prediction, we build defect prediction (classification and regression) models using MIMs, traditional metrics, and their combinations. Our experimental results show that MIMs significantly improve defect classification and regression accuracy.

    Original languageEnglish
    Title of host publicationSIGSOFT/FSE'11 - Proceedings of the 19th ACM SIGSOFT Symposium on Foundations of Software Engineering
    Pages311-321
    Number of pages11
    DOIs
    Publication statusPublished - 2011
    Event19th ACM SIGSOFT Symposium on Foundations of Software Engineering, SIGSOFT/FSE'11 - Szeged, Hungary
    Duration: 2011 Sept 52011 Sept 9

    Publication series

    NameSIGSOFT/FSE 2011 - Proceedings of the 19th ACM SIGSOFT Symposium on Foundations of Software Engineering

    Other

    Other19th ACM SIGSOFT Symposium on Foundations of Software Engineering, SIGSOFT/FSE'11
    Country/TerritoryHungary
    CitySzeged
    Period11/9/511/9/9

    ASJC Scopus subject areas

    • Software

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