Plagiarism Detection for Java Programs without Source Codes

Anjali, V S; Swapna, T.R.; Jayaraman, Bharat

doi:10.1016/j.procs.2015.02.143

Cited by 18 publications

(8 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Several special techniques address source code plagiarism. For example, [4] introduces plagiarism detection of source code by using execution traces of the final program, that is, method calls and key variables. An API-based control flow graph used in [9] to detect plagiarism represents a program as nodes (statements), with an edge between two nodes if there is a transition in the graph between the nodes.…”

Section: Related Workmentioning

confidence: 99%

Detecting Plagiarism Based on the Creation Process

Schneider

Bernstein

Damevski

et al. 2018

IEEE Trans. Learning Technol.

View full text Add to dashboard Cite

All methodologies for detecting plagiarism to date have focused on the final digital "outcome", such as a document or source code. Our novel approach takes the creation process into account using logged events collected by special software or by the macro recorders found in most office applications. We look at an author's interaction logs with the software used to create the work. Detection relies on comparing the histograms of multiple logs' command use. A work is classified as plagiarism if its log deviates too much from logs of "honestly created" works or if its log is too similar to another log. The technique supports the detection of plagiarism for digital outcomes that stem from unique tasks, such as theses and equal tasks such as assignments for which the same problem sets are solved by multiple students. Focusing on the latter case, we evaluate this approach using logs collected by an interactive development environment (IDE) from more than sixty students who completed three programming assignments.Index Terms-plagiarism detection, log analysis, distance metrics, histogram based detection, outlier detection of logs J. Schneider is with the Institute

show abstract

Section: Related Workmentioning

confidence: 99%

Detecting Plagiarism Based on the Creation Process

Schneider

Bernstein

Damevski

et al. 2018

IEEE Trans. Learning Technol.

View full text Add to dashboard Cite

show abstract

“…There exist a diverse range of techniques that can be applied to identify behavioural similarity. For example, analysing the functional equivalence of a program [14,31], the use of data at runtime [32], identifying similar interactions with the execution environment [33], or identifying similar program logic [34,35]. Such approaches are based on the assumption that the behaviour of a program is a uniquely identifying feature, and that similar behaviours indicate similar programs.…”

Section: Background a Measuring Program Similaritymentioning

confidence: 99%

“…For example prior works have analysed: input-output relations [34], executed instruction sequences (i.e. execution traces) [38], stack usage patterns [39], heap structures [40], and API method call sequences [33,41]. However, these three analysed aspects are sufficient for representing behaviour for source code plagiarism detection.…”

Section: Behaviour For Academic Source Code Plagiarism Detectionmentioning

confidence: 99%

Academic Source Code Plagiarism Detection by Measuring Program Behavioral Similarity

2021

View full text Add to dashboard Cite

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.

show abstract

“…There are a diverse range of techniques applied to identify behavioural similarity. This can be by analysing the functional equivalence of a program [14,31], the use of data at runtime [32], identifying similar interactions with the execution environment [33], or identifying similar program logic [34,35]. Such approaches are based on the assumption that the behaviour of a program is a uniquely identifying feature, and that similar behaviours indicate similar programs.…”

Section: A Measuring Program Similaritymentioning

confidence: 99%

Academic Source Code Plagiarism Detection by Measuring Program Behavioural Similarity

Cheers,

Lin,

Smith

2021

Preprint

View full text Add to dashboard Cite

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 as a result 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 represent a program in a novel graphbased format. Plagiarism is then detected by comparing these graphs and evaluating similarity scores. BPlag is evaluated for robustness, accuracy and efficiency against 5 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 compared tools.

show abstract

Plagiarism Detection for Java Programs without Source Codes

Cited by 18 publications

References 15 publications

Detecting Plagiarism Based on the Creation Process

Detecting Plagiarism Based on the Creation Process

Academic Source Code Plagiarism Detection by Measuring Program Behavioral Similarity

Academic Source Code Plagiarism Detection by Measuring Program Behavioural Similarity

Contact Info

Product

Resources

About