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

Budiman, Ariel Elbert; Karnalim, Oscar

doi:10.3390/computers8010011

Cited by 10 publications

(11 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our technique is also more practical to be used compared to other techniques that are able to distinguish coincidental and non-coincidental similarity. It requires no specific assessment constraints [37] and IDE [38].…”

Section: Methodsmentioning

confidence: 99%

“…We believe few detection techniques can mitigate such an effort for distinguishing even though it was not one of their primary aims. A technique in [37] for instance, can reduce the number of pairs with coincidental similarity during an in-class offline assessment as it limits the suspicion on source code files which authors are adjacently seated. Another example is a work proposed by [38] which timestamps each save actions and embeds it on the code.…”

Section: Related Workmentioning

confidence: 99%

“…Disauthorship probability (a part of dishonesty probability) can also be used to distinguish the culprits from the victims if high content similarity is on board. Such distinction can be used to educate them separately [37] or setting the appropriate penalty (if not all dishonesty acts are considered equal). Let us assume we have a code pair with high content similarity in which A and B are the students.…”

Section: Pro21mentioning

confidence: 99%

See 2 more Smart Citations

Programming Style on Source Code Plagiarism and Collusion Detection

Karnalim¹,

Kurniawati²

2020

IJC

Self Cite

View full text Add to dashboard Cite

This paper utilises programming style on a source code plagiarism and collusion detection to both capture obvious attempts of such academic dishonesty (which characteristics are ignored on most detection techniques) and prioritise non-coincidental similarity to the coincidental one (as only the former can raise suspicion). The technique relies on pairwise programming style similarity to deal with the former and dishonesty probability (how significant is the programming style change between the author’s current submission and previous submissions) to deal with the latter. According to our evaluation, programming style similarity can increase precision since when a code is copied, the programming style can be unconsciously shared (especially for novice students). Dishonesty probability increases not only precision but also recall, f-score, and the resulted similarity degree of suspected pairs; the copied code commonly has different programming style in comparison with the student’s usual style (captured from previous submissions). Our detection technique is comparable to a common technique in academia except that it takes longer processing time as more hints are generated and considered.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Pro21mentioning

confidence: 99%

See 1 more Smart Citation

Programming Style on Source Code Plagiarism and Collusion Detection

Karnalim¹,

Kurniawati²

2020

IJC

Self Cite

View full text Add to dashboard Cite

show abstract

“…These techniques rely on various structures for comparison. Some of the structures are source code token strings [8,29,41], abstract syntax trees [20,30,53], parse trees [48], program dependency graphs [32], and low-level token strings [25,42].…”

Section: Related Workmentioning

confidence: 99%

TF-IDF Inspired Detection for Cross-Language Source Code Plagiarism and Collusion

Karnalim

2020

csci

Self Cite

View full text Add to dashboard Cite

Several computing courses allow students to choose which programming language they want to use for completing a programming task. This can lead to cross-language code plagiarism and collusion, in which the copied code file is rewritten in another programming language. In response to that, this paper proposes a detection technique which is able to accurately compare code files written in various programming languages, but with limited effort in accommodating such languages at development stage. The only language-dependent feature used in the technique is source code tokeniser and no code conversion is applied. The impact of coincidental similarity is reduced by applying a TF-IDF inspired weighting, in which rare matches are prioritised. Our evaluation shows that the technique outperforms common techniques in academia for handling language conversion disguises. Further, it is comparable to those techniques when dealing with conventional disguises.

show abstract

“…This could lead to several problems, especially those which are related to authorship and creative works. Hence, several automated plagiarism detection tools dedicated to code domain (e.g., JPlag [2], ES-Plag [3], and IC-Plag [4]) have been proposed. Using those tools, such an illegal behaviour can be detected with limited human effort.…”

Section: Introductionmentioning

confidence: 99%

A Language-Independent Library for Observing Source Code Plagiarism

Franclinton

Karnalim

2019

JISEBI

Self Cite

View full text Add to dashboard Cite

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

show abstract

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

Cited by 10 publications

References 32 publications

Programming Style on Source Code Plagiarism and Collusion Detection

Programming Style on Source Code Plagiarism and Collusion Detection

TF-IDF Inspired Detection for Cross-Language Source Code Plagiarism and Collusion

A Language-Independent Library for Observing Source Code Plagiarism

Contact Info

Product

Resources

About