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View all- AbuHassan AAlshayeb MGhouti L(2020)Software smell detection techniques: A systematic literature reviewJournal of Software: Evolution and Process10.1002/smr.2320Online publication date: 15-Oct-2020
An Empirical Study on Retrieving Structural Clones Using Sequence Pattern Mining Algorithms
Pages 270 - 276
Abstract
Many clone detection techniques focus on fragments of duplicated code, i.e., simple clones. Structural clones are simple clones within a syntactic boundary that are good candidates for refactoring. In this paper, a new approach for detection of structural clones in source code is presented. The proposed approach is parse-tree-based and is enhanced by frequent subsequence mining. It comprises three stages: preprocessing, mining frequent statement sequences, and fine-matching for structural clones using a modified longest common subsequence (LCS) algorithm. The lengths of control statements in a programming language and method identifiers differ; thus, a conventional LCS algorithm does not return the expected length of matched identifiers. We propose an encoding algorithm for control statements and method identifiers. Retrieval experiments were conducted using the Java SWING source code. The results show that the proposed data mining algorithm detects clones comprising 51 extracted statements. Our modified LCS algorithm retrieves a number of structural clones with arbitrary statement gaps.
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Index Terms
- An Empirical Study on Retrieving Structural Clones Using Sequence Pattern Mining Algorithms
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Published In
December 2014
587 pages
ISBN:9781450330015
DOI:10.1145/2684200
Copyright © 2014 ACM.
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- @WAS: International Organization of Information Integration and Web-based Applications and Services
- Johannes Kepler Univ Linz: Johannes Kepler Universität Linz
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Association for Computing Machinery
New York, NY, United States
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Published: 04 December 2014
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iiWAS '14
iiWAS '14: The 16th International Conference on Information Integration and Web-based Applications & Services
December 4 - 6, 2014
Hanoi, Viet Nam
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View all- AbuHassan AAlshayeb MGhouti L(2020)Software smell detection techniques: A systematic literature reviewJournal of Software: Evolution and Process10.1002/smr.2320Online publication date: 15-Oct-2020
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