The impact of using regression models to build defect classifiers

Gopi Krishnan Rajbahadur, Shaowei Wang, Yasutaka Kamei, Ahmed E. Hassan

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

22 被引用数 (Scopus)

抄録

It is common practice to discretize continuous defect counts into defective and non-defective classes and use them as a target variable when building defect classifiers (discretized classifiers). However, this discretization of continuous defect counts leads to information loss that might affect the performance and interpretation of defect classifiers. Another possible approach to build defect classifiers is through the use of regression models then discretizing the predicted defect counts into defective and non-defective classes (regression-based classifiers). In this paper, we compare the performance and interpretation of defect classifiers that are built using both approaches (i.e., discretized classifiers and regression-based classifiers) across six commonly used machine learning classifiers (i.e., linear/logistic regression, random forest, KNN, SVM, CART, and neural networks) and 17 datasets. We find that: i) Random forest based classifiers outperform other classifiers (best AUC) for both classifier building approaches, ii) In contrast to common practice, building a defect classifier using discretized defect counts (i.e., discretized classifiers) does not always lead to better performance. Hence we suggest that future defect classification studies should consider building regression-based classifiers (in particular when the defective ratio of the modeled dataset is low). Moreover, we suggest that both approaches for building defect classifiers should be explored, so the best-performing classifier can be used when determining the most influential features.

本文言語英語
ホスト出版物のタイトルProceedings - 2017 IEEE/ACM 14th International Conference on Mining Software Repositories, MSR 2017
出版社IEEE Computer Society
ページ135-145
ページ数11
ISBN(電子版)9781538615447
DOI
出版ステータス出版済み - 6 29 2017
イベント14th IEEE/ACM International Conference on Mining Software Repositories, MSR 2017 - Buenos Aires, アルゼンチン
継続期間: 5 20 20175 21 2017

出版物シリーズ

名前IEEE International Working Conference on Mining Software Repositories
ISSN(印刷版)2160-1852
ISSN(電子版)2160-1860

その他

その他14th IEEE/ACM International Conference on Mining Software Repositories, MSR 2017
Countryアルゼンチン
CityBuenos Aires
Period5/20/175/21/17

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Software

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