An Investigation of the Effect of Discretization on Defect Prediction Using Static Measures

Software repositories with defect logs are main resource for defect prediction. In recent years, researchers have used the vast amount of data that is contained by software repositories to predict the location of defect in the code that caused problems. In this paper we evaluate the effectiveness of software fault prediction with Naive-Bayes classifiers and J48 classifier by integrating with supervised discretization algorithm developed by Fayyad and Irani. Public datasets from the promise repository have been explored for this purpose. The repository contains software metric data and error data at the function/method level. Our experiment shows that integration of discretization method improves the software fault prediction accuracy when integrated with Naive-Bayes and J48 classifiers.