An Investigation of Design Level Class Cohesion Metrics
Design level class cohesion metrics are based on the assumption that if all the methods of a class have access to similar parameter types, then they all process closely related information. A class with a large number of parameter types common in its methods is more cohesive than a class with less number of parameter types common in its methods. In this paper, the authors review the design level class cohesion metrics with a special focus on metrics which use similarity of parameter types of methods of a class as the basis of its cohesiveness. Basically, three metrics fall in this category: Cohesion Among Methods of a Class (CAMC), Normalized Hamming Distance (NHD), and Scaled NHD (SNHD).