Beyond plagiarism: An active learning method to analyze causes behind code-similarity

2020

csci

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.

Section: Related Workmentioning

confidence: 99%

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

2020

csci

“…A work in [33] proposed a process model for detecting plagiarism in the source code domain. A work in [34] introduced a learning method to facilitate students in understanding that source code similarity does not always lead to plagiarism.…”

Section: Literature Reviewmentioning

confidence: 99%

A Language-Independent Library for Observing Source Code Plagiarism

Franclinton

2019

JISEBI

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

“…In addition to works related to proposing source code plagiarism detection, some supportive works have also been proposed: A work in proposes an active learning method to analyze causes behind source code similarity in addition to plagiarism. A work in formulates a process model for suspecting source code plagiarism. A work in summarizes how educators inform academic integrity in programming to students. Works in capture human perspectives regarding source code plagiarism. Works in [21,23] enlist plagiarism attacks found on programming courses through some observatory studies. A work in proposes a meta‐tool that combines the results of publicly‐available source code plagiarism detection systems. Works in [20,22] develop static method linearization for supporting source code plagiarism detection in object‐oriented environment. …”

Section: Related Workmentioning

confidence: 99%

“…A work in [48] proposes an active learning method to analyze causes behind source code similarity in addition to plagiarism. A work in [25] formulates a process model for suspecting source code plagiarism.…”

Section: Related Workmentioning

confidence: 99%

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

Sulistiani

Comp Applic In Engineering

2018

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.