Implications of ceiling effects in defect predictors
Proceedings of the 4th international workshop on Predictor models in software engineering
Ensemble of software defect predictors: a case study
Proceedings of the Second ACM-IEEE international symposium on Empirical software engineering and measurement
Analysis of Naive Bayes' assumptions on software fault data: An empirical study
Data & Knowledge Engineering
Data mining source code for locating software bugs: A case study in telecommunication industry
Expert Systems with Applications: An International Journal
Misclassification cost-sensitive fault prediction models
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
Variance analysis in software fault prediction models
ISSRE'09 Proceedings of the 20th IEEE international conference on software reliability engineering
Usage of multiple prediction models based on defect categories
Proceedings of the 6th International Conference on Predictive Models in Software Engineering
Influence of confirmation biases of developers on software quality: an empirical study
Software Quality Control
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Defect prediction is important in order to reduce test times by allocating valuable test resources effectively. In this work, we propose a model using multivariate approaches in conjunction with Bayesian methods for defect predictions. The motivation behind using a multivariate approach is to overcome the independence assumption of univariate approaches about software attributes. Using Bayesian methods gives practitioners an idea about the defectiveness of software modules in a probabilistic framework rather than the hard classification methods such as decision trees. Furthermore the software attributes used in this work are chosen among the static code attributes that can easily be extracted from source code, which prevents human errors or subjectivity. These attributes are preprocessed with feature selection techniques to select the most relevant attributes for prediction. Finally we compared our proposed model with the best results reported so far on public datasets and we conclude that using multivariate approaches can perform better. Keywords: Defect prediction, Software Metrics, Naïve Bayes. Topics: Software Quality, Methods and Tools.