Kitsune: Structurally Aware and Adaptable Plagiarism Detection

Zachary Monroe, Ajay Bansal

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Plagiarism is a huge problem in a learning environment. In programming classes especially, plagiarism can be hard to detect as source codes' appearance can be easily modified without changing the intent through simple formatting changes or refactoring. Many source code plagiarism tools do not support a high number of languages because doing so requires maintaining too large of a codebase. It is also difficult to add support for new languages because each language can be vastly different syntactically. Tools that are more extensible often do so by reducing the features of a language that are encoded and end up closer to text comparison tools than structurally aware program analysis tools [27]. This paper introduces a new tool called Kitsune, a plagiarism detection tool, focused on syntactically and structurally aware yet adaptable plagiarism detection. Kitsune has been evaluated for 10 of the languages in the Antlr4 grammar repository with success and could easily be extended to support all the grammars currently developed by Antlr4 or future grammars which are developed as new languages are written.

Original languageEnglish (US)
Title of host publicationProceedings - 2021 IEEE Frontiers in Education Conference, FIE 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665438513
StatePublished - 2021
Event2021 IEEE Frontiers in Education Conference, FIE 2021 - Lincoln, United States
Duration: Oct 13 2021Oct 16 2021

Publication series

NameProceedings - Frontiers in Education Conference, FIE
ISSN (Print)1539-4565


Conference2021 IEEE Frontiers in Education Conference, FIE 2021
Country/TerritoryUnited States


  • adaptable
  • plagiarism detection tools
  • structurally aware

ASJC Scopus subject areas

  • Software
  • Education
  • Computer Science Applications


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