Detecting source code plagiarism on introductory programming course assignments using a bytecode approach

Karnalim, Oscar

doi:10.1109/icts.2016.7910274

Cited by 35 publications

(53 citation statements)

References 21 publications

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“…In general, there are four works which incorporates low-level code for detecting source code plagiarism: the work of Ji et al [36], Karnalim [3], Juričić [41], and Juričić et al [10]. The first two works are focused on Java programming language whereas the other two are focused on .NET programming language.…”

Section: Related Workmentioning

confidence: 99%

“…Even though plagiarism can be conducted easily, detecting this illegal behavior is not a trivial task. Plagiarism can be conducted by every student, even the smartest one who knows programming concept well [2,3]. Thus, plagiarism attacks incorporated on student assignments may be varied.…”

Section: Introductionmentioning

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%

“…It may be the simplest one such as verbatim copy (conducted by weak student) or the most complicated one which UDC 004.415. 3 Original Scientific Paper incorporates various logic and structure changes (conducted by smart students). Detecting these plagiarism variants may require considerable effort and time since lecturer should enlist all possible plagiarism attacks and check each possible source code pair.…”

Section: Introductionmentioning

confidence: 99%

“…Beside proposing a source code plagiarism detection approach, we also evaluate the impact of our approach when compared with standard token-based approach in terms of effectiveness and efficiency. This evaluation is conducted based on various plagiarism attacks from Karnalim's dataset [3] and evaluated based on both single and multiple attack schemas.…”

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

J. inf. organ. sci. (Online)

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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: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

J. inf. organ. sci. (Online)

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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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Layered similarity detection for programming plagiarism and collusion on weekly assessments

Karnalim

Simón²,

Chivers

2022

Comp Applic In Engineering

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When weekly programming assessments are used, it is often the case that some of them are either trivial or strongly directed. Common code similarity detectors are not particularly helpful with such assessments: some potential instances of misconduct are not selected for manual investigation as all submissions are expected to be similar and it is not feasible to check them all. Several dedicated similarity detectors have been developed to work with such assessments, but the experience is required to determine when to use them. This paper presents a similarity detector that works on many kinds of weekly assessments. It combines three‐layered types of similarity so that even within a set of highly similar submissions, program pairs are still sorted according to their levels of similarity. Our similarity detector is more effective than JPlag in distinguishing similar programs and helping to identify plagiarism and collusion. The similarity detector is slower than JPlag, but the longer execution time is partly offset by some optimization that has no negative impact on the effectiveness. As weekly assessments seldom entail large submissions, the execution time does not appear to be a barrier to use.

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Detecting source code plagiarism on introductory programming course assignments using a bytecode approach

Cited by 35 publications

References 21 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

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

Layered similarity detection for programming plagiarism and collusion on weekly assessments

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