A Validation of Object-Oriented Design Metrics as Quality Indicators
IEEE Transactions on Software Engineering
Refactoring: improving the design of existing code
Refactoring: improving the design of existing code
A Metrics Suite for Object Oriented Design
IEEE Transactions on Software Engineering
Using Coupling Measurement for Impact Analysis in Object-Oriented Systems
ICSM '99 Proceedings of the IEEE International Conference on Software Maintenance
A Metric-Based Approach to Enhance Design Quality through Meta-pattern Transformations
CSMR '03 Proceedings of the Seventh European Conference on Software Maintenance and Reengineering
A Survey of Software Refactoring
IEEE Transactions on Software Engineering
Evaluating Object-Oriented Designs with Link Analysis
Proceedings of the 26th International Conference on Software Engineering
Detection Strategies: Metrics-Based Rules for Detecting Design Flaws
ICSM '04 Proceedings of the 20th IEEE International Conference on Software Maintenance
Measurement of Intra-Class & Inter-Class Weakness for Object-Oriented Software
ITNG '06 Proceedings of the Third International Conference on Information Technology: New Generations
CIMMACS'11/ISP'11 Proceedings of the 10th WSEAS international conference on Computational Intelligence, Man-Machine Systems and Cybernetics, and proceedings of the 10th WSEAS international conference on Information Security and Privacy
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Even though object oriented software development has gained popularity due to its inherent features, it also throws challenges in early detection of defects during design phase. Detection of design defects helps in performing appropriate refactorings in improving the quality of design. Literature indicates that active research is going on in detecting design defects using metrics. The present paper introduces a set of metrics for detecting defects in object oriented designs caused by the presence of shotgun surgery and divergent change bad smells. These metrics are, dependency oriented complexity metric for structure (DOCMS(R)), dependen-cy oriented complexity metric for an artifact causing ripples (DOCMA(CR)), and dependency oriented complexity metric for an artifact affected by ripples (DOCMA(AR)). The proposed me-trics have been computed for four cases. These metrics are used successfully in detecting design defects and complexity. In the present study DOCMA(CR) metric value indicated the presence of shotgun surgery bad smell, whereas DOCMA(AR) metric value indicated the presence of divergent change bad smell. DOCMS(R) metric value indicated the increase in complexity of structure (ar-chitecture) when the design defects are present. Detecting bad smells helps in performing appropriate refactorings to make the software maintainable and to improve the quality of software.