Exploratory study of a UML metric for fault prediction

This paper describes the use of a UML metric, an approximation of the CK-RFC metric, for predicting faulty classes before their implementation. We built a code-based prediction model of faulty classes using Logistic Regression. Then, we tested it in different projects, using on the one hand their UML metrics, and on the other hand their code metrics. To decrease the difference of values between UML and code measures, we normalized them using Linear Scaling to Unit Variance. Our results indicate that the proposed UML RFC metric can predict faulty code as well as its corresponding code metric does. Moreover, the normalization procedure used was of great utility, not just for enabling our UML metric to predict faulty code, using a code-based prediction model, but also for improving the prediction results across different packages and projects, using the same model.