Empirical analysis of change metrics for software fault prediction

Garvit Rajesh Choudhary, Sandeep Kumar*, Kuldeep Kumar, Alok Mishra, Cagatay Catal

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

19 Citations (Scopus)


A quality assurance activity, known as software fault prediction, can reduce development costs and improve software quality. The objective of this study is to investigate change metrics in conjunction with code metrics to improve the performance of fault prediction models. Experimental studies are performed on different versions of Eclipse projects and change metrics are extracted from the GIT repositories. In addition to the existing change metrics, several new change metrics are defined and collected from the Eclipse project repository. Machine learning algorithms are applied in conjunction with the change and source code metrics to build fault prediction models. The classification model with new change metrics performs better than the models using existing change metrics. In this work, the experimental results demonstrate that change metrics have a positive impact on the performance of fault prediction models, and high-performance models can be built with several change metrics.
Original languageEnglish
Pages (from-to)15-24
JournalComputers and Electrical Engineering
Publication statusPublished - 1 Apr 2018


  • Change log
  • Defect prediction
  • Eclipse
  • Metrics
  • Software fault prediction
  • Software quality

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