Ensemble of software defect predictors: A case study

Ayse Tosun, Burak Turhan, Ayse Bener

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

31 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationESEM'08
Subtitle of host publicationProceedings of the 2008 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement
Pages318-320
Number of pages3
DOIs
Publication statusPublished - 1 Dec 2008
Externally publishedYes
Event2nd International Symposium on Empirical Software Engineering and Measurement, ESEM 2008 - Kaiserslautern, Germany
Duration: 9 Oct 200810 Oct 2008

Conference

Conference2nd International Symposium on Empirical Software Engineering and Measurement, ESEM 2008
CountryGermany
CityKaiserslautern
Period9/10/0810/10/08

Keywords

  • Defect prediction
  • Ensemble of classifiers
  • Static code attributes

Cite this

Tosun, A., Turhan, B., & Bener, A. (2008). Ensemble of software defect predictors: A case study. In ESEM'08: Proceedings of the 2008 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement (pp. 318-320) https://doi.org/10.1145/1414004.1414066