Prediction of Code Fault Using Naive Bayes and SVM Classifiers
K. Jaya Sankar, Sathya Kannan, Percival Jennifer · 2014
Machine learning classifiers have emerged as a way to predict the existence of fault in the software code. The classifier is first trained on software history data and then used to predict fault. The proposed system over comes the problem of potential insufficiency in accuracy for practical use and use of a large number of features. These large numbers of features adversely impact the accuracy of the approach. This paper proposes a feature selection technique applicable to classification-based fault prediction. This technique is applied to predict faults in software codes and performance of Naive Bayes and Support Vector Machine (SVM) classifiers is characterized. The F-Measure metric is used to compute the accuracy of the fault prediction.