A Partitional Clustering Algorithm for Crosscutting Concerns Identification
نویسندگان
چکیده
Identifying crosscutting concerns is an important issue in the maintenance of software systems. It aims at refactoring the existing systems to use aspect oriented programming, in order to make them easier to maintain and to evolve. In this paper we present a new partitional clustering algorithm for identifying crosscutting concerns in existing software systems. We experimentally evaluate our algorithm using the open source case study JHotDraw, providing a comparison of the proposed approach with similar existing approaches. Key–Words: Aspect mining, crosscutting concerns, clustering
منابع مشابه
Comparing Clustering Algorithms for the Identification of Similar Pages in Web Applications
In this paper, we analyze some widely employed clustering algorithms to identify duplicated or cloned pages in web applications. Indeed, we consider an agglomerative hierarchical clustering algorithm, a divisive clustering algorithm, k-means partitional clustering algorithm, and a partitional competitive clustering algorithm, namely Winner Takes All (WTA). All the clustering algorithms take as ...
متن کاملHierarchical Clustering for Identifying Crosscutting Concerns in Object Oriented Software Systems
Crosscutting concerns are parts of a program that affect or crosscut other concerns. Usually these concerns cannot be cleanly decomposed from the rest of the system, and they are mixed with many core concerns from the system leading to code scattering and code tangling, and, also, to systems that are hard to explore and understand. Identifying crosscutting concerns automatically improves both t...
متن کاملComparison of Agglomerative and Partitional Document Clustering Algorithms
Fast and high-quality document clustering algorithms play an important role in providing intuitive navigation and browsing mechanisms by organizing large amounts of information into a small number of meaningful clusters, and in greatly improving the retrieval performance either via cluster-driven dimensionality reduction, term-weighting, or query expansion. This ever-increasing importance of do...
متن کاملخوشهبندی خودکار داده‌ها با بهره‌گیری از الگوریتم رقابت استعماری بهبودیافته
Imperialist Competitive Algorithm (ICA) is considered as a prime meta-heuristic algorithm to find the general optimal solution in optimization problems. This paper presents a use of ICA for automatic clustering of huge unlabeled data sets. By using proper structure for each of the chromosomes and the ICA, at run time, the suggested method (ACICA) finds the optimum number of clusters while optim...
متن کاملAn Incremental DC Algorithm for the Minimum Sum-of-Squares Clustering
Clustering is an unsupervised technique dealing with problems of organizing a collection of patterns into clusters based on similarity. Most clustering algorithms are based on hierarchical and partitional approaches. Algorithms based on an hierarchical approach generate a dendrogram representing the nested grouping of patterns and similarity levels at which groupings change [19]. Partitional cl...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2009