Cross-validation-based association rule prioritization metric for software defect characterization

Takashi Watanabe, Akito Monden, Zeynep Yucel, Yasutaka Kamei, Shuji Morisaki

Research output: Contribution to journalArticlepeer-review


Association rule mining discovers relationships among variables in a data set, representing them as rules. These are expected to often have predictive abilities, that is, to be able to predict future events, but commonly used rule interestingness measures, such as support and confidence, do not directly assess their predictive power. This paper proposes a cross-validation-based metric that quantifies the predictive power of such rules for characterizing software defects. The results of evaluation this metric experimentally using four open-source data sets (Mylyn, NetBeans, Apache Ant and jEdit) show that it can improve rule prioritization performance over conventional metrics (support, confidence and odds ratio) by 72.8%for Mylyn, 15.0%for NetBeans, 10.5%for Apache Ant and 0 for jEdit in terms of SumNormPre(100) precision criterion. This suggests that the proposed metric can provide better rule prioritization performance than conventional metrics and can at least provide similar performance even in the worst case.

Original languageEnglish
Pages (from-to)2269-2278
Number of pages10
JournalIEICE Transactions on Information and Systems
Issue number9
Publication statusPublished - Sep 1 2018


  • Association rule mining
  • Cross-validation
  • Data mining
  • Defect prediction
  • Software quality

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence


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