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SimilaR: R Code Clone and Plagiarism Detection

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journal contribution
posted on 2020-01-01, 00:00 authored by Maciej Bartoszuk, Marek Gagolewski
Third-party software for assuring source code quality is becoming increasingly popular. Tools that evaluate the coverage of unit tests, perform static code analysis, or inspect run-time memory use are crucial in the software development life cycle. More sophisticated methods allow for performing meta-analyses of large software repositories, e.g., to discover abstract topics they relate to or common design patterns applied by their developers. They may be useful in gaining a better understanding of the component interdependencies, avoiding cloned code as well as detecting plagiarism in programming classes. A meaningful measure of similarity of computer programs often forms the basis of such tools. While there are a few noteworthy instruments for similarity assessment, none of them turns out particularly suitable for analysing R code chunks. Existing solutions rely on rather simple techniques and heuristics and fail to provide a user with the kind of sensitivity and specificity required for working with R scripts. In order to fill this gap, we propose a new algorithm based on a Program Dependence Graph, implemented in the SimilaR package. It can serve as a tool not only for improving R code quality but also for detecting plagiarism, even when it has been masked by applying some obfuscation techniques or imputing dead code. We demonstrate its accuracy and efficiency in a real-world case study.

History

Journal

R Journal

Volume

12

Issue

1

Pagination

367 - 385

Publisher

R Foundation

Location

[Unknown]

ISSN

2073-4859

Language

English

Publication classification

C1 Refereed article in a scholarly journal