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

70 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 Sep 52011 Sep 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
CountryHungary
CitySzeged
Period11/9/511/9/9

ASJC Scopus subject areas

  • Software

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  • Cite this

    Lee, T., Han, D. G., Kim, S., & In, H. P. (2011). Micro interaction metrics for defect prediction. In SIGSOFT/FSE'11 - Proceedings of the 19th ACM SIGSOFT Symposium on Foundations of Software Engineering (pp. 311-321). (SIGSOFT/FSE 2011 - Proceedings of the 19th ACM SIGSOFT Symposium on Foundations of Software Engineering). https://doi.org/10.1145/2025113.2025156