Abstract
The advent of new static analysis tools has automated the searching for code clones, which are duplicated or similar code fragments in a program. However, clone detection tools can report many clones if the source code that is being searched is large. Programmers may have difficulty comprehending the extensive results from the detection tool, which may inhibit the ability to maintain the identified clones. Latent Semantic Indexing (LSI) is an information retrieval technique that attempts to find relationships in a corpus based on the analysis of the documents in the corpus and the terms in the documents. In this paper, LSI is used to cluster clone classes that have been identified initially by a clone detection tool. The goal of this paper is to detect trends and associations among the clustered clone classes and determine if they provide further comprehension to assist in the maintenance of clones. Experimental evaluation of the approach is reported from a sequence of tools that are chained together to perform an analysis of clones detected in the Microsoft Windows NT kernel source code.
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Notes
Clone Detection Literature web site, http://www.cis.uab.edu/tairasr/clones/literature
Windows Research Kernel, http://www.microsoft.com/resources/sharedsource/Licensing/researchkernel.mspx
CCFinder, http://www.ccfinder.net
Project web site, http://www.cis.uab.edu/tairasr/ir4pc
Apache Derby, http://db.apache.org/derby
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We thank the anonymous reviewers who provided many helpful suggestions that assisted in improving the content and presentation of this paper.
This project is supported by National Science Foundation grant CPA-0702764.
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Editors: Tim Menzies and Letha Etzkorn
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Tairas, R., Gray, J. An information retrieval process to aid in the analysis of code clones. Empir Software Eng 14, 33–56 (2009). https://doi.org/10.1007/s10664-008-9089-1
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DOI: https://doi.org/10.1007/s10664-008-9089-1