Developer Micro Interaction Metrics for Software Defect Prediction

Taek Lee, Jaechang Nam, Donggyun Han, Sunghun Kim, Hoh In

Research output: Contribution to journalArticle

22 Citations (Scopus)

Abstract

To facilitate software quality assurance, defect prediction metrics, such as source code metrics, change churns, and the number of previous defects, have been actively studied. Despite the common understanding that developer behavioral interaction patterns can affect software quality, these widely used defect prediction metrics do not consider developer behavior. We therefore propose micro interaction metrics (MIMs), which are metrics that leverage developer interaction information. The developer interactions, such as file editing and browsing events in task sessions, are captured and stored as information by Mylyn, an Eclipse plug-in. Our experimental evaluation demonstrates that MIMs significantly improve overall defect prediction accuracy when combined with existing software measures, perform well in a cost-effective manner, and provide intuitive feedback that enables developers to recognize their own inefficient behaviors during software development.

Original languageEnglish
Article number7447797
Pages (from-to)1015-1035
Number of pages21
JournalIEEE Transactions on Software Engineering
Volume42
Issue number11
DOIs
Publication statusPublished - 2016 Nov 1

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Defects
Quality assurance
Software engineering
Feedback
Costs

Keywords

  • Defect prediction
  • developer interaction
  • Mylyn
  • software metrics
  • software quality

ASJC Scopus subject areas

  • Software

Cite this

Developer Micro Interaction Metrics for Software Defect Prediction. / Lee, Taek; Nam, Jaechang; Han, Donggyun; Kim, Sunghun; In, Hoh.

In: IEEE Transactions on Software Engineering, Vol. 42, No. 11, 7447797, 01.11.2016, p. 1015-1035.

Research output: Contribution to journalArticle

Lee, Taek ; Nam, Jaechang ; Han, Donggyun ; Kim, Sunghun ; In, Hoh. / Developer Micro Interaction Metrics for Software Defect Prediction. In: IEEE Transactions on Software Engineering. 2016 ; Vol. 42, No. 11. pp. 1015-1035.
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