Minimizing software complexity is the foremost objective of each software development paradigm because it affects all other attributes of software such as maintainability, reliability, testability, reusability etc. Measuring software complexity is always essential for predicting fault proneness, computing development efforts and evaluating maintainability of software. This paper proposes a complexity model for classes in object oriented systems. The model computes Class Complexity (CC) as a sum of Method Complexity (MC) and MC is further computed as a sum of Control Flow Complexity (CFC), Total Method Call Complexity (TMCC) and Total Data Call Complexity (TDCC). CFC is computed using McCabe's cyclomatic complexity. TMCC and TDCC are computed with adherence to the principle that "The higher the number of classes involved in method/data calls and polymorphic method calls, makes the object oriented software difficult to understand and maintain". The proposed model is also compared with four Chidamber's and Kemerer's metrics-Weighted Methods per Class (WMC), Response For a Class (RFC), Depth of Inheritance Tree (DIT) and Coupling Between Objects (CBO).
Clustering is a process of grouping a set of similar data objects within the same group based on similarity criteria (i.e. based on a set of attributes). There are many clustering algorithms. The objective of this paper is to perform a comparative analysis of four clustering algorithms namely Kmeans algorithm, Hierarchical algorithm, Expectation and maximization algorithm and Density based algorithm. These algorithms are compared in terms of efficiency and accuracy, using WEKA tool. The data for clustering is used in normalized and as well as unnormalized format. In terms of efficiency and accuracy K-means produces better results as compared to other algorithms.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.