The effects of over and under sampling on fault-prone module detection

Yasutaka Kamei, Akito Monden, Shinsuke Matsumoto, Takeshi Kakimoto, Ken Ichi Matsumoto

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

86 被引用数 (Scopus)

抄録

The goal of this paper is to improve the prediction performance of fault-prone module prediction models (fault-proneness models) by employing over/under sampling methods, which are preprocessing procedures for a fit dataset. The sampling methods are expected to improve prediction performance when the fit dataset is imbalanced, i.e. there exists a large difference between the number of fault-prone modules and not-fault-prone modules. So far, there has been no research reporting the effects of applying sampling methods to fault-proneness models. In this paper, we experimentally evaluated the effects of four sampling methods (random over sampling, synthetic minority over sampling, random under sampling and one-sided selection) applied to four fault-proneness models (linear discriminant analysis, logistic regression analysis, neural network and classification tree) by using two module sets of industry legacy software. All four sampling methods improved the prediction performance of the linear and logistic models, while neural network and classification tree models did not benefit from the sampling methods. The improvements of F1-values in linear and logistic models were 0.078 at minimum, 0.224 at maximum and 0.121 at the mean.

本文言語英語
ホスト出版物のタイトルProceedings - 1st International Symposium on Empirical Software Engineering and Measurement, ESEM 2007
ページ196-204
ページ数9
DOI
出版ステータス出版済み - 12 1 2007
外部発表はい
イベント1st International Symposium on Empirical Software Engineering and Measurement, ESEM 2007 - Madrid, スペイン
継続期間: 9 20 20079 21 2007

出版物シリーズ

名前Proceedings - 1st International Symposium on Empirical Software Engineering and Measurement, ESEM 2007

その他

その他1st International Symposium on Empirical Software Engineering and Measurement, ESEM 2007
Countryスペイン
CityMadrid
Period9/20/079/21/07

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Software

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