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

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

78 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 1st International Symposium on Empirical Software Engineering and Measurement, ESEM 2007
Pages196-204
Number of pages9
DOIs
Publication statusPublished - Dec 1 2007
Externally publishedYes
Event1st International Symposium on Empirical Software Engineering and Measurement, ESEM 2007 - Madrid, Spain
Duration: Sep 20 2007Sep 21 2007

Publication series

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

Other

Other1st International Symposium on Empirical Software Engineering and Measurement, ESEM 2007
CountrySpain
CityMadrid
Period9/20/079/21/07

ASJC Scopus subject areas

  • Computer Science(all)
  • Software

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  • Cite this

    Kamei, Y., Monden, A., Matsumoto, S., Kakimoto, T., & Matsumoto, K. I. (2007). The effects of over and under sampling on fault-prone module detection. In Proceedings - 1st International Symposium on Empirical Software Engineering and Measurement, ESEM 2007 (pp. 196-204). [4343747] (Proceedings - 1st International Symposium on Empirical Software Engineering and Measurement, ESEM 2007). https://doi.org/10.1109/ESEM.2007.80