A language independent approach for detecting duplicated code

Ducasse, Sté́phane; Rieger, Matthias; Demeyer, Serge

doi:10.1109/icsm.1999.792593

Cited by 412 publications

(304 citation statements)

References 9 publications

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“…Data from empirical studies show that typically between 8% and 12% of industrial software consists of duplicated code [DRD99]. Although this may not seem like much, in fact it is difficult to achieve very high rates of duplication.…”

Section: Detecting Duplicated Codementioning

confidence: 99%

“…The Dotplot tool [Hel95] has been used to detect similarities in manual pages, literary texts and names from file systems. In the FAMOOS project, the pattern has been applied to build Duploc, a tool for identifying duplication in software source code [DRD99]. The Dup tool [Bak92] has been used to investigated the source code of the X-Window system and uses a dotplot matrix graphical representation.…”

Section: Known Usesmentioning

confidence: 99%

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Object-Oriented Reengineering Patterns

Nierstrasz¹,

Demeyer

2003

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Section: Detecting Duplicated Codementioning

confidence: 99%

Section: Known Usesmentioning

confidence: 99%

Object-Oriented Reengineering Patterns

Nierstrasz¹,

Demeyer

2003

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“…One of the widely-used string similarity methods is to find a longest common subsequence (LCS) which is adopted by the NiCad clone detector (Roy and Cordy 2008), Plague (Whale 1990), the first version of YAP (Wise 1992), and CoP (Luo et al 2014). Other text-based tools with string matching algorithms other than LCS include, but not limited to, Duploc (Ducasse et al 1999), Simian (Harris 2015, and PMD's Copy/Paste Detector (CPD) (Dangel A and Pelisse R 2011).…”

Section: Code Similarity Measurementmentioning

confidence: 99%

A comparison of code similarity analysers

2017

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Copying and pasting of source code is a common activity in software engineering. Often, the code is not copied as it is and it may be modified for various purposes; e.g. refactoring, bug fixing, or even software plagiarism. These code modifications could affect the performance of code similarity analysers including code clone and plagiarism detectors to some certain degree. We are interested in two types of code modification in this study: pervasive modifications, i.e. transformations that may have a global effect, and local modifications, i.e. code changes that are contained in a single method or code block. We evaluate 30 code similarity detection techniques and tools using five experimental scenarios for Java source code. These are (1) pervasively modified code, created with tools for source code and bytecode obfuscation, and boiler-plate code, (2) source code normalisation through compilation and decompilation using different decompilers, (3) reuse of optimal configurations over different data sets, (4) tool evaluation using ranked-based measures, and (5) local + global code modifications. Our experimental results show that in the presence of pervasive modifications, some of the general textual similarity measures can offer similar performance to specialised code similarity tools, whilst in the presence of boiler-plate code, highly specialised source code similarity detection techniques and tools outperform textual similarity measures. Our study strongly validates the use of compilation/decompilation as a normalisation technique. Its use reduced false classifications to zero for three of the tools. Moreover, we demonstrate that optimal configurations are very sensitive to a specific data set. After directly applying optimal configurations derived from one data set to another, the tools perform poorly on the new data set. The code similarity analysers are thoroughly evaluated not only based on several well-known pair-based and query-based error measures but also on each specific type of pervasive code modification. This broad, thorough study is the largest in existence and potentially an invaluable guide for future users of similarity detection in source code.

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“…There are a lot of researches on clone detection and many tools have been developed [21][22][23][24][25][26][27].We could have used those tools instead of CCFinder.…”

Section: Smatmentioning

confidence: 99%

Measuring Similarity of Large Software Systems Based on Source Code Correspondence

Yamamoto

Matsushita

Kobayashi

et al. 2005

Product Focused Software Process Improvement

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Abstract. It is an important and intriguing issue to know the quantitative similarity of large software systems. In this paper, a similarity metric between two sets of source code files based on the correspondence of overall source code lines is proposed. A Software similarity MeAsurement Tool SMAT was developed and applied to various versions of an operating system(BSD UNIX). The resulting similarity valuations clearly revealed the evolutionary history characteristics of the BSD UNIX Operating System.

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A language independent approach for detecting duplicated code

Cited by 412 publications

References 9 publications

Object-Oriented Reengineering Patterns

Object-Oriented Reengineering Patterns

A comparison of code similarity analysers

Measuring Similarity of Large Software Systems Based on Source Code Correspondence

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