Identifying Clusters of Concepts in a Low Cohesive Class for Extract Class Refactoring Using Metrics Supplemented Agglomerative Clustering Technique
This work addresses software maintenance issues for developers by providing a method to refactor low cohesive classes, but it appears incremental as it builds on existing clustering and metrics techniques.
The paper tackles the problem of low cohesive classes in object-oriented software, which increase maintenance costs, by proposing a metrics supplemented agglomerative clustering technique to identify such classes and clusters of concepts for extract class refactoring, successfully applying it to two examples and a case study.
Object oriented software with low cohesive classes can increase maintenance cost. Low cohesive classes are likely to be introduced into the software during initial design due to deviation from design principles and during evolution due to software deterioration. Low cohesive class performs operations that should be done by two or more classes. The low cohesive classes need to be identified and refactored using extract class refactoring to improve the cohesion. In this regard, two aspects are involved; the first one is to identify the low cohesive classes and the second one is to identify the clusters of concepts in the low cohesive classes for extract class refactoring. In this paper, we propose metrics supplemented agglomerative clustering technique for covering the above two aspects. The proposed metrics are validated using Weyuker's properties. The approach is applied successfully on two examples and on a case study.