Options
A unified approach to automate the usage of plagiarism detection tools in programming courses
Date Issued
2017-08-25
Date Available
2017-11-06T10:53:35Z
Abstract
Plagiarism in programming assignments is an extremely common problem in universities. While there are many tools that automate the detection of plagiarism in source code, users still need to inspect the results and decide whether there is plagiarism or not. Moreover, users often rely on a single tool (using it as "gold standard" for all cases), which can be ineffective and risky. Hence, it is desirable to make use of several tools to complement their results. However, various limitations exist in these tools that make their usage a very time-consuming task, such as the need of manually analyzing and correlating their multiple outputs. In this paper, we propose an automated system that addresses the common usage limitations of plagiarism detection tools. The system automatically manages the execution of different plagiarism tools and generates a consolidated comparative visualization of their results. Consequently, the user can make better-informed decisions about potential plagiarisms. Our experimental results show that the effort and expertise required to use plagiarism detection tools is significantly reduced, while the probability of detecting plagiarism is increased. Results also show that our system is lightweight (in terms of computational resources), proving it is practical for real-world usage.
Sponsorship
Science Foundation Ireland
Other Sponsorship
13/RC/2094
Type of Material
Conference Publication
Publisher
IEEE
Copyright (Published Version)
2017 IEEE
Language
English
Status of Item
Peer reviewed
Conference Details
12th International Conference on Computer Science and Education (ICCSE), Houston, TX, USA
This item is made available under a Creative Commons License
File(s)
Owning collection
Scopus© citations
3
Acquisition Date
Mar 19, 2024
Mar 19, 2024
Views
1193
Acquisition Date
Mar 19, 2024
Mar 19, 2024
Downloads
473
Last Week
4
4
Last Month
8
8
Acquisition Date
Mar 19, 2024
Mar 19, 2024