Automatic Source Code Plagiarism Detection

Kustanto, Cynthia; Liem, Inggriani

doi:10.1109/snpd.2009.62

Cited by 45 publications

(39 citation statements)

References 3 publications

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“…Plagiarism is an act for reusing other people's work without explicitly acknowledging the author beforehand [9,11,12]. In Computer Programming education, it becomes more serious problem since most source codes are written electronically and most students understand a lot about copy-and-paste technique [1].…”

Section: Related Workmentioning

confidence: 99%

“…In addition, even though plagiarism can be detected manually, it is not recommended to be conducted due to its considerable effort. Programming assignments are usually given every week and each of them consists of at least a dozens of source codes [9]. According on these reasons, automatic plagiarism detection is highly desirable to be developed on this domain.…”

Section: Related Workmentioning

confidence: 99%

“…This pattern is typically popular since it is frequently incorporated on most publicly available plagiarism detection systems. It is also incorporated on many related research works about source code plagiarism detection such as Lim et al [34], Shah et al [35], Kustanto & Liem [9], Ji et al [36], Djuric & Gasevic [6], and Pawelczak [37]. Nevertheless, not all structure-based approaches rely on standard lexical token sequence.…”

Section: Related Workmentioning

confidence: 99%

“…On the other hand, ALA is incorporated as our similarity algorithm since it outperforms Greedy String Tiling (GST) algorithm in terms of sensitivity and specificity [7]. GST algorithm is a state-of-the-art algorithm which had been frequently incorporated on various structure-based plagiarism detection approaches [8,9,10,3]. To our knowledge, there are no related works which combine both .NET CIL and adaptive local alignment for detecting source code plagiarism.…”

Section: Introductionmentioning

confidence: 99%

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Detecting Source Code Plagiarism on .NET Programming Languages using Low-level Representation and Adaptive Local Alignment

Rabbani¹,

Karnalim²

2017

JIOS

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Even though there are various source code plagiarism detection approaches, only a few works which are focused on low-level representation for deducting similarity. Most of them are only focused on lexical token sequence extracted from source code. In our point of view, low-level representation is more beneficial than lexical token since its form is more compact than the source code itself. It only considers semantic-preserving instructions and ignores many source code delimiter tokens. This paper proposes a source code plagiarism detection which rely on low-level representation. For a case study, we focus our work on .NET programming languages with Common Intermediate Language as its low-level representation. In addition, we also incorporate Adaptive Local Alignment for detecting similarity. According to Lim et al, this algorithm outperforms code similarity state-of-the-art algorithm (i.e. Greedy String Tiling) in term of effectiveness. According to our evaluation which involves various plagiarism attacks, our approach is more effective and efficient when compared with standard lexical-token approach.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Detecting Source Code Plagiarism on .NET Programming Languages using Low-level Representation and Adaptive Local Alignment

Rabbani¹,

Karnalim²

2017

JIOS

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“…Liu & Wang [9] applied multithreading on their ensemble learning in Cite this article as: O. Karnalim, "Improving Scalability of Java Archive Search Engine Through Recursion Conversion and Multithreading", CommIT (Communication & Information Technology) Journal 10(1), [15][16][17][18][19][20][21][22][23][24][25][26]2016. order to filter spam in Short Message Services (SMS).…”

Section: Introductionmentioning

confidence: 99%

Improving Scalability of Java Archive Search Engine through Recursion Conversion and Multithreading

Karnalim

2016

CommIT (Communication and Information Technology) Journal

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Abstract-Based on the fact that bytecode always exists on Java archive, a bytecode based Java archive search engine had been developed [1,2]. Although the system is quite effective, it still lacks of scalability because many modules applying recursive calls and the system only utilizes one core (single thread). In this research, Java archive search engine architecture is redesigned in order to improve its scalability. All recursions are converted into iterative forms although most of these modules are logically recursive and quite difficult to convert (e.g., Tarjan's strongly connected component algorithm). Recursion conversion can be conducted by following its respective recursive pattern. Each recursion is broken down to four parts (before and after actions of current and its children) and converted to iteration with the help of caller reference. This conversion mechanism improves scalability by avoiding stack overflow error caused by method calls. System scalability is also improved by applying multithreading mechanism which successfully cut off its processing time. Shorter processing time may enable system to handle larger data. Multithreading is applied on major parts which are indexer, vector space model (VSM) retriever, low-rank vector space model (LRVSM) retriever, and semantic relatedness calculator (semantic relatedness calculator also involves multiprocess). The correctness of both recursion conversion and multithread design are proved by the fact that all implementation yield similar result.

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ES‐Plag: Efficient and sensitive source code plagiarism detection tool for academic environment

Sulistiani

Karnalim

2018

Comp Applic In Engineering

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Source code plagiarism detection using Running‐Karp‐Rabin Greedy‐String‐Tiling (RKRGST) is a common practice in academic environment. However, such approach is time‐inefficient (due to RKRGST's cubic time complexity) and insensitive (toward token subsequence rearrangement). This paper proposes ES‐Plag, a plagiarism detection tool featured with cosine‐based filtering and penalty mechanism to handle aforementioned issues. Cosine‐based filtering mitigates time‐inefficiency by excluding non‐potential pairs from RKRGST comparison; while penalty mechanism mitigates insensitivity by reducing the number of matched tokens with the number of matched subsequences prior similarity normalization. In addition to issue‐solving features, ES‐Plag is also featured with project‐based input, colorized adjacency similarity matrix, matched token highlighting, and various similarity algorithms (e.g., Cosine Similarity and Local Alignment). Three findings can be deducted from our evaluation. First, cosine‐based filtering boosts up time efficiency with a trade‐off in effectiveness. Second, penalty mechanism enhances sensitivity even though its improvement in terms of effectiveness is quite limited. Third, ES‐Plag's features are beneficial for examiners.

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Automatic Source Code Plagiarism Detection

Cited by 45 publications

References 3 publications

Detecting Source Code Plagiarism on .NET Programming Languages using Low-level Representation and Adaptive Local Alignment

Detecting Source Code Plagiarism on .NET Programming Languages using Low-level Representation and Adaptive Local Alignment

Improving Scalability of Java Archive Search Engine through Recursion Conversion and Multithreading

ES‐Plag: Efficient and sensitive source code plagiarism detection tool for academic environment

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