Application of neural networks for software quality prediction using object-oriented metrics
Journal of Systems and Software
Empirical Validation of Object-Oriented Metrics on Open Source Software for Fault Prediction
IEEE Transactions on Software Engineering
Object-oriented software fault prediction using neural networks
Information and Software Technology
Journal of Systems and Software
On The Detection of Test Smells: A Metrics-Based Approach for General Fixture and Eager Test
IEEE Transactions on Software Engineering
Improving fault detection in modified code: a study from the telecommunication industry
Journal of Computer Science and Technology
Journal of Software Maintenance and Evolution: Research and Practice
Anomaly-based fault detection in pervasive computing system
Proceedings of the 5th international conference on Pervasive services
Analyzing clusters of class characteristics in OO applications
Journal of Systems and Software
Fault detection and prediction in an open-source software project
PROMISE '09 Proceedings of the 5th International Conference on Predictor Models in Software Engineering
The Journal of Supercomputing
Maintainability prediction of object-oriented software system by multilayer perceptron model
ACM SIGSOFT Software Engineering Notes
An in-depth study of the potentially confounding effect of class size in fault prediction
ACM Transactions on Software Engineering and Methodology (TOSEM)
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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.