TY - GEN
T1 - Ensemble of software defect predictors
T2 - 2nd International Symposium on Empirical Software Engineering and Measurement, ESEM 2008
AU - Tosun, Ayse
AU - Turhan, Burak
AU - Bener, Ayse
PY - 2008
Y1 - 2008
N2 - 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.
AB - 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.
KW - Defect prediction
KW - Ensemble of classifiers
KW - Static code attributes
UR - http://www.scopus.com/inward/record.url?scp=62949247536&partnerID=8YFLogxK
U2 - 10.1145/1414004.1414066
DO - 10.1145/1414004.1414066
M3 - Conference contribution
AN - SCOPUS:62949247536
SN - 9781595939715
T3 - ESEM'08: Proceedings of the 2008 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement
SP - 318
EP - 320
BT - ESEM'08
Y2 - 9 October 2008 through 10 October 2008
ER -