The prediction of faulty classes using object-oriented design metrics

The study used data collected from one version of a commercial Java application for constructing a prediction model and found that an export coupling metric had the strongest association with fault-proneness, indicating a structural feature that may be symptomatic of a class with a high probability of latent faults.

Abstract

Contemporary evidence suggests that most field faults in software applications are found in a small percentage of the software's components. This means that if these faulty software components can be detected early in the development project's life cycle, mitigating actions can be taken, such as a redesign. For object-oriented applications, prediction models using design metrics can be used to identify faulty classes early on. In this paper we report on a study that used object-oriented design metrics to construct such prediction models. The study used data collected from one version of a commercial Java application for constructing a prediction model. The model was then validated on a subsequent release of the same application. Our results indicate that the prediction model has a high accuracy. Furthermore, we found that an export coupling (EC) metric had the strongest association with fault-proneness, indicating a structural feature that may be symptomatic of a class with a high probability of latent faults.