Validation, Verification, and Testing of Computer Software
ACM Computing Surveys (CSUR)
Classification by Voting Feature Intervals
ECML '97 Proceedings of the 9th European Conference on Machine Learning
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Predicting fault-prone components in a java legacy system
Proceedings of the 2006 ACM/IEEE international symposium on Empirical software engineering
Data Mining Static Code Attributes to Learn Defect Predictors
IEEE Transactions on Software Engineering
A Multivariate Analysis of Static Code Attributes for Defect Prediction
QSIC '07 Proceedings of the Seventh International Conference on Quality Software
Validation of network measures as indicators of defective modules in software systems
PROMISE '09 Proceedings of the 5th International Conference on Predictor Models in Software Engineering
PROMISE '09 Proceedings of the 5th International Conference on Predictor Models in Software Engineering
Merits of using repository metrics in defect prediction for open source projects
FLOSS '09 Proceedings of the 2009 ICSE Workshop on Emerging Trends in Free/Libre/Open Source Software Research and Development
Reducing false alarms in software defect prediction by decision threshold optimization
ESEM '09 Proceedings of the 2009 3rd International Symposium on Empirical Software Engineering and Measurement
Information and Software Technology
An industrial case study of classifier ensembles for locating software defects
Software Quality Control
Influence of confirmation biases of developers on software quality: an empirical study
Software Quality Control
An algorithmic approach to missing data problem in modeling human aspects in software development
Proceedings of the 9th International Conference on Predictive Models in Software Engineering
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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.