Clustering Software Components for Program Restructuring and Component Reuse Using Hybrid XOR Similarity Function

Component based software development has gained a lot of practical importance in the field of software engineering from several researchers and also from industry perspective. Finding components for efficient software reuse is one of the important problems aimed by researchers. Clustering reduces the search space of components by grouping similar entities together thus ensuring reduced time complexity as it reduces the search time for component retrieval. In this research, we instigate a new a generalized approach for clustering a given set of documents or text files or components by defining a new similarity function called hybrid XOR function for the purpose of finding degree of similarity between two document sets or software components. We construct a matrix called similarity matrix of the order n-1 by n for a given set of n documents or components or patterns by applying hybrid XOR function. We define and design the algorithm for component or document clustering which has the input as similarity matrix and output being set of clusters formed dynamically as compared to other clustering algorithms that predefine the count of clusters. The output is a set of highly cohesive pattern groups or documents. The approach can be justified as it carries out very simple computational logic and efficient in terms of processing with reduced search space and can be also be used in general for document clustering or software component clustering.