A hybrid faulty module prediction using association rule mining and logistic regression analysis

Yasutaka Kamei, Akito Monden, Shuji Morisaki, Ken Ichi Matsumoto

研究成果: 書籍/レポート タイプへの寄稿会議への寄与

23 被引用数 (Scopus)

抄録

This paper proposes a fault-prone module prediction method that combines association rule mining with logistic regression analysis. In the proposed method, we focus on three key measures of interestingness of an association rule (support, confidence and lift) to select useful rules for the prediction. If a module satisfies the premise (i.e. the condition in the antecedent part) of one of the selected rules, the module is classified by the rule as either fault-prone or not. Otherwise, the module is classified by the logistic model. We experimentally evaluated the prediction performance of the proposed method with different thresholds of each rule interestingness measure (support, confidence and lift) using a module set in the Eclipse project, and compared it with three well-known fault-proneness models (logistic regression model, linear discriminant model and classification tree). The result showed that the improvement of the Fl-value of the proposed method was 0.163 at maximum compared to conventional models.

本文言語英語
ホスト出版物のタイトルESEM'08: Proceedings of the 2008 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement
ページ279-281
ページ数3
DOI
出版ステータス出版済み - 2008
外部発表はい
イベント2nd International Symposium on Empirical Software Engineering and Measurement, ESEM 2008 - Kaiserslautern, ドイツ
継続期間: 10月 9 200810月 10 2008

その他

その他2nd International Symposium on Empirical Software Engineering and Measurement, ESEM 2008
国/地域ドイツ
CityKaiserslautern
Period10/9/0810/10/08

!!!All Science Journal Classification (ASJC) codes

  • コンピュータ サイエンスの応用
  • ソフトウェア
  • 電子工学および電気工学

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