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

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

研究成果: ジャーナルへの寄稿学術誌査読

1 被引用数 (Scopus)


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.

ジャーナルIEICE Transactions on Information and Systems
出版ステータス出版済み - 9月 2018

!!!All Science Journal Classification (ASJC) codes

  • ソフトウェア
  • ハードウェアとアーキテクチャ
  • コンピュータ ビジョンおよびパターン認識
  • 電子工学および電気工学
  • 人工知能


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