Predicting fault-proneness using OO metrics. An industrial case study

Software quality is an important external software attribute that is difficult to measure objectively. In this case study, we empirically validate a set of object-oriented metrics in terms of their usefulness in predicting fault-proneness, an important software quality indicator We use a set of ten software product metrics that relate to the following software attributes: the size of the software, coupling, cohesion, inheritance, and reuse. Eight hypotheses on the correlations of the metrics with fault-proneness are given. These hypotheses are empirically tested in a case study, in which the client side of a large network service management system is studied. The subject system is written in Java and it consists of 123 classes. The validation is carried out using two data analysis techniques: regression analysis and discriminant analysis