Ensemble of Software Defect Predictors: A Case Study
A. Tosun, B. Turhan, and A. Bener. Proceedings of the Second ACM-IEEE International Symposium on Empirical Software Engineering and Measurement, page 318--320. New York, NY, USA, ACM, (2008)
DOI: 10.1145/1414004.1414066
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.
%0 Conference Paper
%1 Tosun:2008:ESD:1414004.1414066
%A Tosun, Ayse
%A Turhan, Burak
%A Bener, Ayse
%B Proceedings of the Second ACM-IEEE International Symposium on Empirical Software Engineering and Measurement
%C New York, NY, USA
%D 2008
%I ACM
%K myown
%P 318--320
%R 10.1145/1414004.1414066
%T Ensemble of Software Defect Predictors: A Case Study
%U http://doi.acm.org/10.1145/1414004.1414066
%X 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.
%@ 978-1-59593-971-5
@inproceedings{Tosun:2008:ESD:1414004.1414066,
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.},
acmid = {1414066},
added-at = {2015-09-17T22:24:54.000+0200},
address = {New York, NY, USA},
author = {Tosun, Ayse and Turhan, Burak and Bener, Ayse},
biburl = {https://www.bibsonomy.org/bibtex/2ba7dc0a9ac1ecb9d1bf164f00f018ff1/burak.turhan},
booktitle = {Proceedings of the Second ACM-IEEE International Symposium on Empirical Software Engineering and Measurement},
description = {Ensemble of software defect predictors},
doi = {10.1145/1414004.1414066},
interhash = {9caaae7c8a1db77be1e9e489e8f9f04c},
intrahash = {ba7dc0a9ac1ecb9d1bf164f00f018ff1},
isbn = {978-1-59593-971-5},
keywords = {myown},
location = {Kaiserslautern, Germany},
numpages = {3},
pages = {318--320},
publisher = {ACM},
series = {ESEM '08},
timestamp = {2015-09-17T22:24:54.000+0200},
title = {Ensemble of Software Defect Predictors: A Case Study},
url = {http://doi.acm.org/10.1145/1414004.1414066},
year = 2008
}