An Empirical Study of Software Metrics
IEEE Transactions on Software Engineering
Measuring software design quality
Measuring software design quality
C4.5: programs for machine learning
C4.5: programs for machine learning
Object-oriented metrics: measures of complexity
Object-oriented metrics: measures of complexity
Software metrics (2nd ed.): a rigorous and practical approach
Software metrics (2nd ed.): a rigorous and practical approach
Practical Software Maintenance: Best Practices for Managing Your Software Investment
Practical Software Maintenance: Best Practices for Managing Your Software Investment
Object-Relational DBMSs: Tracking the Next Great Wave
Object-Relational DBMSs: Tracking the Next Great Wave
Software Measurement: A Necessary Scientific Basis
IEEE Transactions on Software Engineering
Validation of Metrics for Object-Relational Databases
Proceedings of the Workshop on Object-Oriented Technology
Intelligent data analysis
IEEE Transactions on Software Engineering
An Empirical Study with Metrics for Object-Relational Databases
ECSQ '02 Proceedings of the 7th International Conference on Software Quality
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New Object-Relational Database Management Systems (ORDBMSs) are replacing existing relational ones. In spite of the high expressiveness, application systems built upon ORDBMS are more complex and difficult to maintain due to the mixing of two paradigms, the relational and the object-oriented. This paper describes a suite of metrics for measuring different aspects of an object-relational database. An empirical validation of the usefulness of the proposed metrics in estimating the understandability of an object-relational schema is given. The analysis procedure comprises the use of two techniques: C4.5, a machine learning algorithm, and RoC, a robust Bayesian classifier. The results demonstrate that a subset of the proposed measures is relevant for the estimation of the understandability.