Software clone detection: A systematic review

Rattan, Dhavleesh; Bhatia, Rajesh; Singh, Maninder

doi:10.1016/j.infsof.2013.01.008

Cited by 309 publications

(168 citation statements)

References 204 publications

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“…Rattan et al [22] found at least 70 clone detectors in the literature, almost double the 40 found by Roy et al [27] in 2009. However, very few tools target scalability to very large repositories.…”

Section: Related Workmentioning

confidence: 83%

SourcererCC

Sajnani

Saini

Svajlenko

et al. 2016

Proceedings of the 38th International Conference on Software Engineering

339

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Despite a decade of active research, there is a marked lack in clone detectors that scale to very large repositories of source code, in particular for detecting near-miss clones where significant editing activities may take place in the cloned code. We present SourcererCC, a token-based clone detector that targets three clone types, and exploits an index to achieve scalability to large inter-project repositories using a standard workstation. SourcererCC uses an optimized invertedindex to quickly query the potential clones of a given code block. Filtering heuristics based on token ordering are used to significantly reduce the size of the index, the number of code-block comparisons needed to detect the clones, as well as the number of required token-comparisons needed to judge a potential clone.We evaluate the scalability, execution time, recall and precision of SourcererCC, and compare it to four publicly available and state-of-the-art tools. To measure recall, we use two recent benchmarks, (1) a large benchmark of real clones, BigCloneBench, and (2) a Mutation/Injection-based framework of thousands of fine-grained artificial clones. We find SourcererCC has both high recall and precision, and is able to scale to a large inter-project repository (250MLOC) using a standard workstation.

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Section: Related Workmentioning

confidence: 83%

SourcererCC

Sajnani

Saini

Svajlenko

et al. 2016

Proceedings of the 38th International Conference on Software Engineering

339

View full text Add to dashboard Cite

show abstract

“…The latter has been of increasing interest in the software engineering research community under the term ''cloning.'' In particular, clones that resulted from copy&paste can now be detected reliably (Rattan, Bhatia & Singh, 2013). In our own research with companies, we often found rates of redundant code caused by copy&paste in the range of 20%-30% (Wagner, 2013).…”

Section: Introductionmentioning

confidence: 90%

“…Even clones with additional changes (inconsistent, near-miss or type-3 clones) can be detected by several detection approaches and tools (Kamiya, Kusumoto & Inoue, 2002;Deissenboeck et al, 2008;Jiang et al, 2007a). There are also two surveys (Koschke, 2007;Roy & Cordy, 2007) and a systematic literature review (Rattan, Bhatia & Singh, 2013) on this topic. Tiarks, Koschke & Falke (2011) investigated in particular type-3 clones and also their differences.…”

Section: Related Workmentioning

confidence: 99%

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How are functionally similar code clones syntactically different? An empirical study and a benchmark

Wagner

Abdulkhaleq

Bogicevic

et al. 2016

PeerJ Computer Science

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Background. Today, redundancy in source code, so-called ''clones'' caused by copy &paste can be found reliably using clone detection tools. Redundancy can arise also independently, however, not caused by copy&paste. At present, it is not clear how only functionally similar clones (FSC) differ from clones created by copy&paste. Our aim is to understand and categorise the syntactical differences in FSCs that distinguish them from copy&paste clones in a way that helps clone detection research. Methods. We conducted an experiment using known functionally similar programs in Java and C from coding contests. We analysed syntactic similarity with traditional detection tools and explored whether concolic clone detection can go beyond syntax. We ran all tools on 2,800 programs and manually categorised the differences in a random sample of 70 program pairs. Results. We found no FSCs where complete files were syntactically similar. We could detect a syntactic similarity in a part of the files in <16% of the program pairs. Concolic detection found 1 of the FSCs. The differences between program pairs were in the categories algorithm, data structure, OO design, I/O and libraries. We selected 58 pairs for an openly accessible benchmark representing these categories. Discussion. The majority of differences between functionally similar clones are beyond the capabilities of current clone detection approaches. Yet, our benchmark can help to drive further clone detection research.

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“…A program dependency graph (PDG) represents control and data flow dependencies of a function of source code (Rattan et al, 2013). In other words, it considers the semantic information encoded in the dependency graph.…”

Section: Graph Basedmentioning

confidence: 99%

A novel approach in detecting code clones in Java using DFS

Choudhary¹,

Gupta²

2017

Int. j. adv. appl. sci

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Code is the rudimentary element of any software. Code clones may be defined as the segments of the program which are akin to one another. The similarity may be either syntactic or semantic. Cloning is easy to implement but hard to detect. Many researches have been carried out in order to find the methods for detecting these clones of code as problems are encountered at the time of maintenance due to these clones in codes. This further increases the cost of maintenance. The objective of our work is to precisely detect the code clones. Here, an approach is proposed based on the Abstract Syntax Tree method. The purpose for adopting AST is that it gives better detection results as compared to other techniques and is considered to be the best approach for detecting type 3 code clones. Furthermore, AST offers syntactic knowledge which can be leveraged to filter certain types of clones. The results obtained clearly shows that the technique adopted is able to precisely detect the near-miss clones as compared to the tools namely NICAD and CLAN.

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Software clone detection: A systematic review

Cited by 309 publications

References 204 publications

SourcererCC

SourcererCC

How are functionally similar code clones syntactically different? An empirical study and a benchmark

A novel approach in detecting code clones in Java using DFS

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