A Study on Fault-Proneness Detection of Object-Oriented Systems

Fault proneness detection in object-oriented systems is an interesting area for software companies and researchers. Several hundreds of metrics have been defined with the aim of measuring the different aspects of object-oriented systems. Only a few of them have been validated for fault detection, several interesting works with this view have been considered. This paper reports a research study started from the analysis of more than 200 different object-oriented metrics extracted from the literature with the aim of identifying suitable models for the detection of fault-proneness of classes. Such a large number of metrics allows extracting a subset of them in order to obtain models that can be adopted for fault proneness detection. To this end, the whole set of metrics has been classified on the basis of the measured aspect in order to reduce their number to a manageable one; then statistical techniques have been employed to produce a hybrid model comprised of 12 metrics. The work has been focussed on identifying models that can detect as many faulty classes as possible and, at the same time, models that are based on a manageable small set of metrics. A compromise between these aspects and the classification correctness of faulty and non-faulty classes was the main challenge of the research. As a result, two models for fault-proneness classes detection have been obtained and validated.