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Proceedings ArticleDOI

Automatic Generation of Plagiarism Detection Among Student Programs

TLDR
Initial qualitative and quantitative evaluations illustrate a flexible, convenient and cost-effective tool for building plagiarism detectors for effective detection of programs in various imperative and procedural programming languages.
Abstract
A system for the automatic generation of plagiarism detectors that find similar programs in a set of student programs is presented. Existing plagiarism detectors are either applied to a programming language or a pre-defined set of programming languages. The general purpose one usually employs string matching to perform similarity measures that are based on plagiarism detection among documents in general, and not in programs in particular, thus, losing much of the structure and logic of programs in the process. On the other hand, plagiarism detectors for specific languages only cater to that particular set of languages. This study provides a means for the user to specify the programming language of the student programs to be analyzed. Moreover, an automatic plagiarism detector system must be immune to the transformations that students perform on copied programs. These transformations are usually dependent on several factors namely: the type of programming problems and correspondingly, the complexity of the project to be implemented by the students, and also the programming language paradigm of the programs. Thus, the similarity measures employed by the system should be determined by these factors and can be specified by the professor. He/she has the option to specify how the similarities among the student programs will be captured. The system provides an interface for the specification of the particular programming language in which the student programs are implemented, and a knowledgebase of similarity measures that the user would like to include in the analysis of the student programs. Hence, the system provides flexibility in the programming language of the student programs to be analyzed and the similarity measures that the professor wishes to employ. Initial qualitative and quantitative evaluations illustrate a flexible, convenient and cost-effective tool for building plagiarism detectors for effective detection of programs in various imperative and procedural programming languages. The approach also addresses some of the changes that students perform on copied programs which JPlag fails to handle, thus, allowing for improved accuracy in terms of the reduction of false-positives, increasing the chance of catching plagiarized programs. These changes include modification of control structures, use of temporary variables and subexpressions, in-lining and re-factoring of methods, and redundancy (variables or methods that were not used). Comprehensive tests on other programming languages under various programming language paradigms such as object-oriented, logic and functional languages, considering the different changes that the students employ to copied programs (such as the tests done in JPlag) are also recommended for empirical evaluation

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Citations
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Journal ArticleDOI

Source-code Similarity Detection and Detection Tools Used in Academia: A Systematic Review

TL;DR: This review gives an overview of definitions of plagiarism, plagiarism detection tools, comparison metrics, obfuscation methods, datasets used for comparison, and algorithm types and identifies interesting insights about metrics and datasets for quantitative tool comparison and categorisation of detection algorithms.

Plagiarism Detection based on studying correlation between Author, Title and Content

TL;DR: This research intends to present an alternative to plagiarism detection tools by automating the traditional free search process on search engines to detect plagiarism by intelligently extracting selective parts of text from the file subject to check and pass them to search engine in different forms and processing results in order to come up with a decision of committing plagiarism.
Journal ArticleDOI

Layered similarity detection for programming plagiarism and collusion on weekly assessments

TL;DR: In this article , the authors present a similarity detector that works on many kinds of weekly programming assessments, combining three-layered types of similarity so that even within a set of highly similar submissions, program pairs are still sorted according to their levels of similarity.
Journal ArticleDOI

Research of anti-plagiarism monitoring system model

TL;DR: A flexible anti-plagiarism system model based on user-defined plagiarism standards that can be used in digital library, E-learning and other fields and also proposed PlagLazy and format-legacy phenomena that plagiarist will remain format-Legacy such as soft-enter symbol in his DOC document after plagiarizing material from web.

Sistem Otomatisasi Pengelolaan Laboratorium untuk Penilaian Praktikum Pemrograman Dasar dengan Deteksi Plagiarisme

TL;DR: Banyaknya mahasiswa yang mengikuti kelas pemrograman membuat pengelolaannya menjadi lebih berat karena dosen harus memeriksa satu per satu source code ying dikumpulkan.
References
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Proceedings ArticleDOI

Winnowing: local algorithms for document fingerprinting

TL;DR: The class of local document fingerprinting algorithms is introduced, which seems to capture an essential property of any finger-printing technique guaranteed to detect copies, and a novel lower bound on the performance of any local algorithm is proved.
Journal Article

Finding Plagiarisms among a Set of Programs with JPlag

TL;DR: JPlag is a web service that finds pairs of similar programs among a given set of programs and its architecture and its comparsion algorithm is described, which is based on a known one called Greedy String Tiling.
Journal ArticleDOI

An algorithmic approach to the detection and prevention of plagiarism

TL;DR: This paper discuses one possible quantification which works well when applied to student computer pro grams and shows how this problem can be reduced by quantifyin g papers in such a way that equivalent papers are given equal values.
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