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E. S. Cho, C. J. Kim, D. D. Kim and S. Y. Rhew, “Static and Dynamic Metrics for Effective Object Clustering,” Proceedings of Asia Pacific International Conference on Software Engineering, IEEE Computer Society, Washington, 1998, pp. 78-85.

has been cited by the following article:

• TITLE: Improving Class Cohesion Measurement: Towards a Novel Approach Using Hierarchical Clustering

  AUTHORS: Lazhar Sadaoui, Mourad Badri, Linda Badri

  KEYWORDS: Object-Oriented; Classes; Cohesion; Similarity; Clustering; Metrics and Empirical Evaluation

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.5 No.7, June 15, 2012

  ABSTRACT: Class cohesion is considered as one of the most important object-oriented software attributes. High cohesion is, in fact, a desirable property of software. Many different metrics have been suggested in the last several years to measure the cohesion of classes in object-oriented systems. The class of structural object-oriented cohesion metrics is the most in-vestigated category of cohesion metrics. These metrics measure cohesion on structural information extracted from the source code. Empirical studies noted that these metrics fail in many situations to properly reflect cohesion of classes. This paper aims at exploring the use of hierarchical clustering techniques to improve the measurement of cohesion of classes in object-oriented systems. The proposed approach has been evaluated using three particular case studies. We also used in our study three well-known structural cohesion metrics. The achieved results show that the new approach appears to better reflect the cohesion (and structure) of classes than traditional structural cohesion metrics.