Style Analysis for Source Code Plagiarism Detection - An Analysis of a Dataset of Student Coursework

Olfat M. Mirza; Mike Joy; Georgina Cosma

doi:10.1109/ICALT.2017.117

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Conference proceeding

Style Analysis for Source Code Plagiarism Detection - An Analysis of a Dataset of Student Coursework

Olfat M. Mirza, Mike Joy and Georgina Cosma

2017 IEEE 17th International Conference on Advanced Learning Technologies (ICALT), pp.296-297

07/2017

DOI: https://doi.org/10.1109/ICALT.2017.117

Abstract

Coding Style

Complexity theory

Encoding

Feature extraction

Java

Plagiarism

Programming profession

Source Code Plagairism Detection

Style Analysis

Plagiarism has become an increasing problem in higher education in recent years. Coding style can be used to detect source code plagiarism that involves writing and deciding the structure of the code which does not affect the logic of a program, thus offering a way to differentiate between different code authors. This paper focuses to identify whether a data set consisting of student programming assignments is rich enough to apply coding style metrics to detect similarities between code sequences, and we use the BlackBox dataset as a case study.

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Details

Title: Style Analysis for Source Code Plagiarism Detection - An Analysis of a Dataset of Student Coursework
Creators - without role: Olfat M. Mirza - University of Warwick
Mike Joy - University of Warwick
Georgina Cosma - Nottingham Trent University
Publication Details: 2017 IEEE 17th International Conference on Advanced Learning Technologies (ICALT), pp.296-297
Publisher: IEEE
Identifiers: 9931225408331
Academic Unit: Umm Al Qura University
Language: English
Resource Type: Conference proceeding