Search Results - "AI Generated Code Detection"

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  1. 1

    AIGCodeSet: A New Annotated Dataset for AI Generated Code Detection by Demirok, Basak, Kutlu, Mucahid

    Published: IEEE 25.06.2025
    “…While large language models provide significant convenience for software development, they can lead to ethical issues in job interviews and student…”
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    Conference Proceeding
  2. 2

    Assessing AI Detectors in Identifying AI-Generated Code: Implications for Education by Pan, Wei Hung, Chok, Ming Jie, Wong, Jonathan Leong Shan, Shin, Yung Xin, Poon, Yeong Shian, Yang, Zhou, Chong, Chun Yong, Lo, David, Lim, Mei Kuan

    ISSN: 2832-7578
    Published: ACM 14.04.2024
    “…Educators are increasingly concerned about the usage of Large Language Models (LLMs) such as ChatGPT in programming education, particularly regarding the…”
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    Conference Proceeding
  3. 3

    AIGCodeSet: A New Annotated Dataset for AI Generated Code Detection by Basak Demirok, Kutlu, Mucahid

    ISSN: 2331-8422
    Published: Ithaca Cornell University Library, arXiv.org 21.12.2024
    Published in arXiv.org (21.12.2024)
    “… In this study, we introduce AIGCodeSet, a dataset for AI-generated code detection tasks, specifically for the Python programming language…”
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    Paper
  4. 4

    An Empirical Study on Automatically Detecting AI-Generated Source Code: How Far are We? by Suh, Hyunjae, Tafreshipour, Mahan, Li, Jiawei, Bhattiprolu, Adithya, Ahmed, Iftekhar

    ISSN: 1558-1225
    Published: IEEE 26.04.2025
    “… Then, to improve the performance of AI-generated code detection, we propose a range of approaches, including fine-tuning the LLMs and machine learning…”
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    Conference Proceeding
  5. 5

    An Empirical Study on Automatically Detecting AI-Generated Source Code: How Far Are We? by Suh, Hyunjae, Mahan Tafreshipour, Li, Jiawei, Bhattiprolu, Adithya, Ahmed, Iftekhar

    ISSN: 2331-8422
    Published: Ithaca Cornell University Library, arXiv.org 06.11.2024
    Published in arXiv.org (06.11.2024)
    “… Then, to improve the performance of AI-generated code detection, we propose a range of approaches, including fine-tuning the LLMs and machine learning…”
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    Paper