Assessing UML design metrics for predicting fault-prone classes in a Java system

Identifying and fixing software problems before implementation are believed to be much cheaper than after implementation. Hence, it follows that predicting fault-proneness of software modules based on early software artifacts like software design is beneficial as it allows software engineers to perform early predictions to anticipate and avoid faults early enough. Taking this motivation into consideration, in this paper we evaluate the usefulness of UML design metrics to predict fault-proneness of Java classes. We use historical data of a significant industrial Java system to build and validate a UML-based prediction model. Based on the case study we have found that level of detail of messages and import coupling-both measured from sequence diagrams, are significant predictors of class fault-proneness. We also learn that the prediction model built exclusively using the UML design metrics demonstrates a better accuracy than the one built exclusively using code metrics.