A Comparison of Source Code Plagiarism Detection Engines

Lancaster, Thomas; Culwin, Fintan

doi:10.1080/08993400412331363843

Cited by 96 publications

(38 citation statements)

References 7 publications

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“…A number of tools have been developed specifically for computer science courses by addressing the unique challenges of examining code, see e.g. Lancaster and Culwin (2004), Cosma and Joy (2012), or Durić and Gašević (2013). Occasionally students raise concerns regarding the privacy and security of their intellectual property with these kinds of tools, or with the efficacy of the tool itself (Jones & Moore, 2010).…”

Section: Detection and Disciplinementioning

confidence: 99%

An Analysis of the Effect of Mobile Learning on Lebanese Higher Education

Jabbour¹

2014

Informatics in Education

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We present an overview of the nature of academic dishonesty with respect to computer science coursework. We discuss the efficacy of various policies for collaboration with regard to student education, and we consider a number of strategies for mitigating dishonest behaviour on computer science coursework by addressing some common causes. Computer science coursework is somewhat unique, in that there often exist ideal solutions for problems, and work may be shared and copied with very little effort. We discuss the idiosyncratic nature of how collaboration, collusion and plagiarism are defined and perceived by students, instructors and administration. After considering some of the common reasons for dishonest behaviour among students, we look at some methods that have been suggested for mitigating them. Finally, we propose several ideas for improving computer science courses in this context. We suggest emphasizing the intended learning outcomes of each assignment, providing tutorial sessions to facilitate acceptable collaboration, delivering quizzes related to assignment content after each assignment is submitted, and clarifying the boundary between collaboration and collusion in the context of each course. While this discussion is directed at the computer science community, much may apply to other disciplines as well, particularly those with a similar nature such as engineering, other sciences, or mathematics.

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Section: Detection and Disciplinementioning

confidence: 99%

An Analysis of the Effect of Mobile Learning on Lebanese Higher Education

Jabbour¹

2014

Informatics in Education

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“…in (Lancaster and Culwin, 2004). In the following paragraphs, selected systems which enable users to compare (at least partly or roughly) source codes written in the PHP language are briefly described.…”

Section: Theoretical Frameworkmentioning

confidence: 99%

“…It uses structure comparison, it is offered for free (until recent time as a free web service), and is licensed under GNU General Public License (jPlag, 2017;Prechelt et al, 2000). Its main advantages are reliability and support of the main programming languages, which make it one of the most popular tools both among teachers of programming at MENDELU and among experts (Lancaster and Culwin, 2004).…”

Section: Theoretical Frameworkmentioning

confidence: 99%

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Source Code Plagiarism Detection for PHP Language

Všianský¹,

Dlabolová²,

Foltýnek³

2017

EJOBSAT

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This paper introduces a system for detection of plagiarism in source codes written in the PHP computer language, part of the plagiarism detection tool Anton. We used the greedy string tiling algorithm together with tokenization and hash calculation. The efficiency of the system was tested on both an artificial dataset and on real data coming from a course taught at our university. Our results are compared with other similar systems and solutions, concluding that Anton can detect all examined types of plagiarism with higher accuracy than other systems.

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“…Within the Computing discipline, two freely available tools are often used to find collusion within a batch of source code [12,13,14]. Best known are Measure Of Software Similarity (MOSS) [15] and JPlag [16].…”

Section: The Existing Research Base On Plagiarism and Contract Chmentioning

confidence: 99%

An Observational Analysis of the Range and Extent of Contract Cheating from Online Courses Found on Agency Websites

Lancaster

Clarke

2014

2014 Eighth International Conference on Complex, Intelligent and Software Intensive Systems

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-Although online courses can provide access to higher education through e-learning systems which would not otherwise be available for students, they also pose challenges for academic integrity. Paramount to this is contract cheating, where students have been observed paying other people to complete work for them to complete their online courses. This paper analyses attempts by students at contract cheating using Transtutors.com, which is a billed as a site for homework support. A sample of 174 online assignments found on Transtutors.com are analysed and traced back to 17 online universities. Assignments from online institutions are demonstrated to be a particular problem for contract cheating detectives, since notifying staff at those institutions of attempts by their students to cheat has proved to be difficult or impossible. The paper concludes by looking at the wider issues posed by online contract cheating and the opportunities for automated detection within this field.

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A Comparison of Source Code Plagiarism Detection Engines

Cited by 96 publications

References 7 publications

An Analysis of the Effect of Mobile Learning on Lebanese Higher Education

An Analysis of the Effect of Mobile Learning on Lebanese Higher Education

Source Code Plagiarism Detection for PHP Language

An Observational Analysis of the Range and Extent of Contract Cheating from Online Courses Found on Agency Websites

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