Retrieving and classifying instances of source code plagiarism

Ganguly, Debasis; Jones, Gareth J. F.; Ramírez-de-la-Cruz, Aarón; Ramírez-de-la-Rosa, Gabriela; Villatoro-Tello, Esaú

doi:10.1007/s10791-017-9313-y

Cited by 23 publications

(43 citation statements)

References 20 publications

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“…It has been argued that the source code token sequence may not be comprehensive enough for representing the code files, and hence more-advanced representations have been introduced. Other techniques which have been proposed include: the use of abstract syntax trees containing source code tokens in their syntactic structure (Fu et al, 2017;Ganguly et al, 2018); program dependency graphs which describe how each instruction relies on other instructions (Liu et al, 2006); and low-level token sequences extracted from the binary files of compiled source code (Karnalim, 2017(Karnalim, , 2019Rabbani & Karnalim, 2017).…”

Section: A Review Of Automated Source Code Plagiarism Detectionmentioning

confidence: 99%

Source Code Plagiarism Detection in Academia with Information Retrieval: Dataset and the Observation

Karnalim¹,

Budi²,

Toba³

et al. 2019

Informatics in Education

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Source code plagiarism is an emerging issue in computer science education. As a result, a number of techniques have been proposed to handle this issue. However, comparing these techniques may be challenging, since they are evaluated with their own private dataset(s). This paper contributes in providing a public dataset for comparing these techniques. Specifically, the dataset is designed for evaluation with an Information Retrieval (IR) perspective. The dataset consists of 467 source code files, covering seven introductory programming assessment tasks. Unique to this dataset, both intention to plagiarise and advanced plagiarism attacks are considered in its construction. The dataset's characteristics were observed by comparing three IR-based detection techniques, and it is clear that most IR-based techniques are less effective than a baseline technique which relies on Running-Karp-Rabin Greedy-String-Tiling, even though some of them are far more time-efficient.

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Section: A Review Of Automated Source Code Plagiarism Detectionmentioning

confidence: 99%

Source Code Plagiarism Detection in Academia with Information Retrieval: Dataset and the Observation

Karnalim¹,

Budi²,

Toba³

et al. 2019

Informatics in Education

View full text Add to dashboard Cite

show abstract

“…Source code token fragments can be compared by adopting techniques from other domains. Works in [11][12][13], for instances, rely on Information Retrieval techniques such as Latent Semantic Indexing [14]. Other examples are works in [15,16] that rely on a clustering technique (where the fragments act as vectors) and a work in [17] that relies on a technique from Information Theory.…”

Section: Related Workmentioning

confidence: 99%

Automated Hints Generation for Investigating Source Code Plagiarism and Identifying The Culprits on In-Class Individual Programming Assessment

Budiman

Karnalim

2019

Computers

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Most source code plagiarism detection tools only rely on source code similarity to indicate plagiarism. This can be an issue since not all source code pairs with high similarity are plagiarism. Moreover, the culprits (i.e., the ones who plagiarise) cannot be differentiated from the victims even though they need to be educated further on different ways. This paper proposes a mechanism to generate hints for investigating source code plagiarism and identifying the culprits on in-class individual programming assessment. The hints are collected from the culprits’ copying behaviour during the assessment. According to our evaluation, the hints from source code creation process and seating position are 76.88% and at least 80.87% accurate for indicating plagiarism. Further, the hints from source code creation process can be helpful for indicating the culprits as the culprits’ codes have at least one of our predefined conditions for the copying behaviour.

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“…Attribute-based tool considers two source codes as similar to each other when they share similar attributes (e.g., source code characteristics or fragments). The similarity can be measured either by standard occurrence counting [7], [8], Information Retrieval measurement [9]- [12], or clustering technique [13], [14].…”

Section: Literature Reviewmentioning

confidence: 99%

A Language-Independent Library for Observing Source Code Plagiarism

Franclinton

Karnalim

2019

JISEBI

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Background: Most source code plagiarism detection tools are not modifiable. Consequently, when a modification is required to be applied, a new detection tool should be created along with it. This could be a problem as creating the tool from scratch is time-inefficient while most of the features are similar across source code plagiarism detection tools.Objective: To alleviate researchers' effort, this paper proposes a library for observing two plagiarism-suspected codes (a feature which is similar across most source code plagiarism detection tools).Methods: Unique to this library, it is not constrained by the selected programming language for development. It is executed from command line, which is supported by most programming languages.Results: According to our evaluation, the library is integrable and functional. Moreover, the library can enhance teaching assistants' accuracy and reduce the tasks' completion time.Conclusion: The library can be beneficial for the development of source code plagiarism detection tools since it is integrable, functional, and helpful for teaching assistants.Keywords:Language independency, Plagiarism detection, Reusable library, Source code, Tool development

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Retrieving and classifying instances of source code plagiarism

Cited by 23 publications

References 20 publications

Source Code Plagiarism Detection in Academia with Information Retrieval: Dataset and the Observation

Source Code Plagiarism Detection in Academia with Information Retrieval: Dataset and the Observation

Automated Hints Generation for Investigating Source Code Plagiarism and Identifying The Culprits on In-Class Individual Programming Assessment

A Language-Independent Library for Observing Source Code Plagiarism

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