Dealing with noise in defect prediction
Proceedings of the 33rd International Conference on Software Engineering
Micro interaction metrics for defect prediction
Proceedings of the 19th ACM SIGSOFT symposium and the 13th European conference on Foundations of software engineering
Proceedings of the 2013 International Conference on Software Engineering
Obtaining the threat model for e-mail phishing
Applied Soft Computing
Software defect prediction using Bayesian networks
Empirical Software Engineering
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Recently, machine learning classifiers have emerged as a way to predict the existence of a bug in a change made to a source code file. The classifier is first trained on software history data, and then used to predict bugs. Two drawbacks of existing classifier-based bug prediction are potentially insufficient accuracy for practical use, and use of a large number of features. These large numbers of features adversely impact scalability and accuracy of the approach. This paper proposes a feature selection technique applicable to classification-based bug prediction. This technique is applied to predict bugs in software changes, and performance of Naive Bayes and Support Vector Machine (SVM) classifiers is characterized.