Applied multivariate techniques
Applied multivariate techniques
A Validation of Object-Oriented Design Metrics as Quality Indicators
IEEE Transactions on Software Engineering
Exploring the relationship between design measures and software quality in object-oriented systems
Journal of Systems and Software
Feature normalization and likelihood-based similarity measures for image retrieval
Pattern Recognition Letters - Special issue on image/video indexing and retrieval
Designing Concurrent, Distributed, and Real-Time Applications with Uml
Designing Concurrent, Distributed, and Real-Time Applications with Uml
Measuring OO Design Metrics from UML
UML '02 Proceedings of the 5th International Conference on The Unified Modeling Language
Object oriented software quality prediction using general regression neural networks
ACM SIGSOFT Software Engineering Notes
Early estimation of software quality using in-process testing metrics: a controlled case study
3-WoSQ Proceedings of the third workshop on Software quality
Towards logistic regression models for predicting fault-prone code across software projects
ESEM '09 Proceedings of the 2009 3rd International Symposium on Empirical Software Engineering and Measurement
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This paper describes the use of a UML metric, an approximation of the CK-RFC metric, for predicting faulty classes before their implementation. We built a code-based prediction model of faulty classes using Logistic Regression. Then, we tested it in different projects, using on the one hand their UML metrics, and on the other hand their code metrics. To decrease the difference of values between UML and code measures, we normalized them using Linear Scaling to Unit Variance. Our results indicate that the proposed UML RFC metric can predict faulty code as well as its corresponding code metric does. Moreover, the normalization procedure used was of great utility, not just for enabling our UML metric to predict faulty code, using a code-based prediction model, but also for improving the prediction results across different packages and projects, using the same model.