Benchmarks for software clone detection: A ten-year retrospective

Roy, Chanchal K.; Cordy, James R.

doi:10.1109/saner.2018.8330194

Cited by 50 publications

(41 citation statements)

References 73 publications

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“…• CCD benchmarks: Benchmarks are considered as means to advance the state of the art in software engineering [29]. The idea of using curated data sets and benchmarks in CCD research for comparing and evaluating the tools has been explored to some extent by various researchers as well [30]. Several benchmarks exist in the literature, ranging from one by Bellon et al [31] to Big-CloneBench [32] from Roy et al The latter contains more than eight million manually validated clones within over 25,000 open-source Java systems.…”

Section: B Other Initiativesmentioning

confidence: 99%

MoCoP: Towards a Model Clone Portal

Babur

Stephan

2019

2019 IEEE/ACM 11th International Workshop on Modelling in Software Engineering (MiSE)

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DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal. If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User Agreement:

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Section: B Other Initiativesmentioning

confidence: 99%

MoCoP: Towards a Model Clone Portal

Babur

Stephan

2019

2019 IEEE/ACM 11th International Workshop on Modelling in Software Engineering (MiSE)

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“…• We devise a c-like IR based on LLVM and run NiCad [21] on it (LLNiCad). • We compare the performance of LLNiCad, NiCad, and MeCC in terms of precision and recall using the clone oracle created by Krutz et al [13].…”

Section: Introductionmentioning

confidence: 99%

Improving Syntactical Clone Detection Methods through the Use of an Intermediate Representation

Caldeira

Sakamoto

Washizaki

et al. 2020

2020 IEEE 14th International Workshop on Software Clones (IWSC)

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Detection of type-3 and type-4 clones remains a difficult task. Current methods are complex, both on a conceptual and computational level. Similarly, their usage requires substantial implementation efforts. Instead of creating yet another method, it might be more productive to combine the simplicity of syntactic approaches with the abstractions granted by intermediate representations (IR). To this end, we devised a c-like IR based on LLVM and ran NiCad on it (LLNiCad). To establish whether the clone detection capabilities of syntactic approaches can be improved through an IR, we compared NiCad and LLNiCad on three open source projects taken from Krutz's benchmark and a subset of Google code jam solutions. In our results, the f1score of LLNiCad consistently outperforms NiCad. Indeed, for all clone types in Krutz's benchmark, LLNiCad has a f1-score that is 37% higher than NiCad; with both better precision and recall. For type-4 clones in our GCJ benchmark, the f1-score of LLNiCad also outperforms CCCD (a semantic clone detector) by 44%. These findings suggest that IRs are beneficial for improving clone detection and that they have a larger impact on type-3 and type-4 clones.

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“…Clones can be further grouped into two main groups based on their similarity to the source as [3]: clones of textual similarity which includes type-I, type-II and type-III Or functional similarity which applies to type-IV.…”

Section: Introductionmentioning

confidence: 99%

“…Clone detection accuracy is measured using standard information retrieval metrics specifically precision and recall [3]. Precision measures how well the tool can detect an actual clone.…”

Section: Introductionmentioning

confidence: 99%

Clone Detection Techniques for JavaScript and Language Independence: Review

Alfageh¹,

Alhakami²,

Baz³

et al. 2020

IJACSA

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Code clone detection is an active field of study in computer science. Despite its rich history, it lacks focus on web scripting languages. Due to the expansion of web applications and web development amongst developers of varying education and experience levels, they inevitably resort to cloning through out the web. The spread of code clones is further increased by websites like StackOverflow and GitHub. In this paper, we will be focusing on clone detection research done to target clones in JavaScript code and discuss its areas of concern. Also, we will summarize language independent research done and possibility of its application on JavaScript and web applications.

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Benchmarks for software clone detection: A ten-year retrospective

Cited by 50 publications

References 73 publications

MoCoP: Towards a Model Clone Portal

MoCoP: Towards a Model Clone Portal

Improving Syntactical Clone Detection Methods through the Use of an Intermediate Representation

Clone Detection Techniques for JavaScript and Language Independence: Review

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