期刊名称：International Journal of Computer Science and Information Technologies
电子版ISSN：0975-9646
出版年度：2014
卷号：5
期号：4
页码：5793-5796
出版社：TechScience Publications
摘要：Code clones are similar program structures recurring in variant forms in software system(s). Several techniques have been proposed to discover simple clones i.e., method level clones. But identifying structural clones has been a difficult task because this requires am iterative scan of the database. Structural clones show a bigger picture of simple clones. Hence identification of the structural level clones improves the performance of the system under development by enhancing the properties like reusability, maintainability and re-engineering. So to identify the structural clones a number of approaches have been developed but the efficiency of those algorithms are less. Hence in this paper we would like to propose two different techniques one for association mining and one for clustering to identify the structural clones. The proposed technique would not only scan sparse data but also dense data to identify the clones. We also try to detect exact and near miss clones. The techniques used would be mining frequent patterns using prefix trees and an efficient density based clustering algorithm. At last we make a comparison between the existing method and the one proposed in this paper.
