Ensemble of software defect predictors: A case study

Ayse Tosun*, Burak Turhan, Ayse Bener

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36 Atıf (Scopus)

Özet

In this paper, we present a defect prediction model based on ensemble of classifiers, which has not been fully explored so far in this type of research. We have conducted several experiments on public datasets. Our results reveal that ensemble of classifiers considerably improve the defect detection capability compared to Naive Bayes algorithm. We also conduct a cost-benefit analysis for our ensemble, where it turns out that it is enough to inspect 32% of the code on the average, for detecting 76% of the defects.

Orijinal dilİngilizce
Ana bilgisayar yayını başlığıESEM'08
Ana bilgisayar yayını alt yazısıProceedings of the 2008 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement
Sayfalar318-320
Sayfa sayısı3
DOI'lar
Yayın durumuYayınlandı - 2008
Harici olarak yayınlandıEvet
Etkinlik2nd International Symposium on Empirical Software Engineering and Measurement, ESEM 2008 - Kaiserslautern, Germany
Süre: 9 Eki 200810 Eki 2008

Yayın serisi

AdıESEM'08: Proceedings of the 2008 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement

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???event.eventtypes.event.conference???2nd International Symposium on Empirical Software Engineering and Measurement, ESEM 2008
Ülke/BölgeGermany
ŞehirKaiserslautern
Periyot9/10/0810/10/08

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