Detecting Pervasive Source Code Plagiarism through Dynamic Program Behaviours

Cheers, Hayden; Lin, Yuqing; Smith, Shamus P.

doi:10.1145/3373165.3373168

Cited by 14 publications

(19 citation statements)

References 26 publications

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“…Hence, it could not be included in this evaluation. However, in a prior study [12], MOSS performed with poorer results than JPlag, but greater results than Sim; hence similar results can be assumed in its absence.…”

Section: ) Compared Scpdtsmentioning

confidence: 68%

“…It is conceivable that such a student has the skills to pervasively transform source code such that it no longer warrants suspicion of plagiarism. Cheers, Lin and Smith [12] noted that with the use of modern integrated development environments, many automated source code transformations can be applied to a misappropriated assignment in order to transform it such that currently available SCPDTs would not measure a high enough similarity to warrant suspicion. Such transformations took approximately 1-2 hours to apply, being much less time than a typical major programming assignment.…”

Section: A Behaviour and Its Robustness To Plagiarism-hiding Transfomentioning

confidence: 99%

“…Ideally, the approach would be evaluated against as many SCPDTs as possible to ensure a thorough comparison. However, as identified in prior works [9,12], not all SCPDTs are made available by their authors after publication. For this evaluation, six SCPDTs were identified as accessible for reuse: MOSS [18], JPlag [11], Plaggie [19], Sim [20], Sherlock-Warwick [7], Sherlock-Sydney [15].…”

Section: ) Compared Scpdtsmentioning

confidence: 99%

“…Although existing SCPDTs are largely robust and accurate in the presence of plagiarism-hiding transformations, they are vulnerable to pervasive plagiarism-hiding transformations [12]. Pervasive plagiarism-hiding transformations represent extreme cases of plagiarism-hiding transformations, where a plagiariser has applied source code transformations entirely throughout a body of source code, transforming it such that it bares little cosmetic or structural similarity.…”

Section: Introductionmentioning

confidence: 99%

“…Such transformations can greatly impact upon the ability of SCPDTs to measure similarity. Subsequently, when a plagiarised work is pervasively transformed, SCPDTs can report a low enough similarity that it does not raise suspicion of plagiarism [12].…”

Section: Introductionmentioning

confidence: 99%

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Academic Source Code Plagiarism Detection by Measuring Program Behavioral Similarity

2021

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Source code plagiarism is a long-standing issue in tertiary computer science education. Many source code plagiarism detection tools have been proposed to aid in the detection of source code plagiarism. However, existing detection tools are not robust to pervasive plagiarism-hiding transformations and can be inaccurate in the detection of plagiarised source code. This article presents BPlag, a behavioural approach to source code plagiarism detection. BPlag is designed to be both robust to pervasive plagiarism-hiding transformations and accurate in the detection of plagiarised source code. Greater robustness and accuracy is afforded by analysing the behaviour of a program, as behaviour is perceived to be the least susceptible aspect of a program impacted upon by plagiarism-hiding transformations. BPlag applies symbolic execution to analyse execution behaviour and represents a program in a novel graph-based format. Plagiarism is then detected by comparing these graphs and evaluating similarity scores. BPlag is evaluated for robustness, accuracy and efficiency against five commonly used source code plagiarism detection tools. It is then shown that BPlag is more robust to plagiarism-hiding transformations and more accurate in the detection of plagiarised source code, but is less efficient than the compared tools.INDEX TERMS source code plagiarism detection, behavioural similarity, source code similarity.

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Section: ) Compared Scpdtsmentioning

confidence: 68%

Section: A Behaviour and Its Robustness To Plagiarism-hiding Transfomentioning

confidence: 99%