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abstract

An Automated Management System for References in Programming Code

Published:26 June 2021Publication History

ABSTRACT

One way to offset source code plagiarism is by teaching students the importance of referencing externally sourced code. Our work proposes a semi-automated code comment generation system which utilises code similarity detection. This approach is envisioned to ease the code referencing process for students and help establish a proposed referencing standard.

References

  1. Mohammad Masudur Rahman, Chanchal K Roy, and Iman Keivanloo. 2015. Recommending insightful comments for source code using crowdsourced knowledge. In 15th International Working Conference on Source Code Analysis and Manipulation (SCAM). IEEE, 81--90.Google ScholarGoogle ScholarCross RefCross Ref
  2. Simon, Judy Sheard, Michael Morgan, Andrew Petersen, Amber Settle, and Jane Sinclair. 2018. Informing students about academic integrity in programming. In 20th Australasian Computing Education Conference (ACE 2018). 113--122. https://doi.org/10.1145/3160489.3160502Google ScholarGoogle Scholar

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  1. An Automated Management System for References in Programming Code

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      • Published in

        cover image ACM Conferences
        ITiCSE '21: Proceedings of the 26th ACM Conference on Innovation and Technology in Computer Science Education V. 2
        June 2021
        109 pages
        ISBN:9781450383974
        DOI:10.1145/3456565

        Copyright © 2021 Owner/Author

        Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 26 June 2021

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        Overall Acceptance Rate552of1,613submissions,34%

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        ITiCSE 2024
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