Search Results - AI-Generated Code Detection*

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  1. 1

    Using pseudo-AI submissions for detecting AI-generated code by Bashir, Shariq

    ISSN: 2624-9898, 2624-9898
    Published: Frontiers Media S.A 23.05.2025
    Published in Frontiers in computer science (Lausanne) (23.05.2025)
    “… Students may use these tools inappropriately for their programming assignments, and there currently are not reliable methods to detect AI-generated code…”
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    Journal Article
  2. 2

    CDUP: Detecting AI Generated Code Using Abstract Syntax Tree Analyst by Păuleț, Ștefan, Moruz, Alex

    ISSN: 1877-0509, 1877-0509
    Published: Elsevier B.V 2025
    Published in Procedia computer science (2025)
    “… Most work in the field is aimed at the larger problem of detecting AI generated text, leaving an important gap as far as specialized tools for code detection are concerned…”
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    Journal Article
  3. 3

    AI Generated Code Plagiarism Detection in Computer Science Courses: A Literature Mapping by Simmons, Archer, Holanda, Maristela, Chamon, Christiana, Da Silva, Dilma

    ISSN: 2377-634X
    Published: IEEE 13.10.2024
    “…, methods of plagiarism have evolved, however methods of detection may not be capable of accurately differentiating between code generated by human and artificial intelligence (AI…”
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    Conference Proceeding
  4. 4

    Exploring the Boundaries Between LLM Code Clone Detection and Code Similarity Assessment on Human and AI-Generated Code by Zhang, Zixian, Saber, Takfarinas

    ISSN: 2504-2289, 2504-2289
    Published: Basel MDPI AG 01.02.2025
    Published in Big data and cognitive computing (01.02.2025)
    “…As Large Language Models (LLMs) continue to advance, their capabilities in code clone detection have garnered significant attention…”
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    Journal Article
  5. 5

    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
    “… This study first presents an empirical analysis to investigate the effectiveness of the existing AI detection tools in detecting AI-generated code…”
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    Conference Proceeding
  6. 6

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

    Published: IEEE 25.06.2025
    “…) model is a critical issue. In this study, we present AIGCodeSet, which consists of 2.828 AI-generated…”
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    Conference Proceeding
  7. 7

    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
    “… (AIGC) Detectors for academic misconduct. In this paper, we present an empirical study where the LLM is examined for its attempts to bypass detection by AIGC Detectors…”
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    Conference Proceeding
  8. 8

    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
  9. 9

    Detecting Adversarial Prompted AI-Generated Code on Stack Overflow: A Benchmark Dataset and an Enhanced Detection Approach by Swaraj, Aman, Agarwal, Krishna, Joshi, Atharv, Kumar, Sandeep

    ISSN: 2576-3148
    Published: IEEE 07.09.2025
    “… While recent studies have focused on detecting AI-generated code, they have mostly worked with long code samples from repositories and assignments…”
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    Conference Proceeding
  10. 10

    Detecting AI-Generated Source Code in Student Assignments Using Steganographic Watermarks by Horvath, Marek, Bubenkova, Lenka, Pietrikova, Emilia, Corba, Matej

    Published: IEEE 13.11.2025
    “…This paper presents the use of steganography to ensure the integrity and uniqueness of student programming assignments by embedding hidden student identifiers…”
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    Conference Proceeding
  11. 11

    DeVAIC: A tool for security assessment of AI-generated code by Cotroneo, Domenico, De Luca, Roberta, Liguori, Pietro

    ISSN: 0950-5849
    Published: Elsevier B.V 01.01.2025
    Published in Information and software technology (01.01.2025)
    “… This research work introduces DeVAIC (Detection of Vulnerabilities in AI-generated Code), a tool to evaluate the security of AI-generated Python code, which overcomes the challenge of examining incomplete code…”
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    Journal Article
  12. 12

    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)
    “… This study first presents an empirical analysis to investigate the effectiveness of the existing AI detection tools in detecting AI-generated code…”
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    Paper
  13. 13

    Security Analysis of Automated Code Generation: Structural Vulnerabilities in AI-Generated Code by Yoo, Sang Hyun, Kim, Hyun Jung

    ISSN: 1846-6168, 1848-5588
    Published: Sveučilište Sjever 2025
    Published in Tehnički glasnik (2025)
    “… This study examines the susceptibilities inherent in AI-generated code through a hybrid methodology that combines Ghidra for static analysis with Valgrind and Frida for dynamic evaluation to identify…”
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    Paper Journal Article
  14. 14

    Artifact feature purification for cross-domain detection of AI-generated images by Meng, Zheling, Peng, Bo, Dong, Jing, Tan, Tieniu, Cheng, Haonan

    ISSN: 1077-3142
    Published: Elsevier Inc 01.10.2024
    Published in Computer vision and image understanding (01.10.2024)
    “… Existing generated image detection methods suffer from performance drops when faced with out-of-domain generators and image scenes…”
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    Journal Article
  15. 15
  16. 16

    Evaluating ChatGPT-3’s efficacy in solving coding tasks: implications for academic integrity in English language assessments by Elhambakhsh, Seyedeh Elham

    ISSN: 2229-0443, 2229-0443
    Published: Cham Springer International Publishing 01.12.2025
    Published in Language Testing in Asia (01.12.2025)
    “… The researcher explored potential implications for academic integrity and the challenges associated with AI-generated solutions…”
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    Journal Article
  17. 17

    Security Analysis of Automated Code Generation: Structural Vulnerabilities in AI-Generated by Kim, Hyun Jung, Yoo, Sang Hyun

    ISSN: 1846-6168, 1848-5588
    Published: 15.09.2025
    Published in Tehnički glasnik (15.09.2025)
    “… This study examines the susceptibilities inherent in AI-generated code through a hybrid methodology that combines Ghidra for static analysis with Valgrind and Frida for dynamic evaluation to identify…”
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    Journal Article
  18. 18

    Is This You, LLM? Recognizing AI-written Programs with Multilingual Code Stylometry by Gurioli, Andrea, Gabbrielli, Maurizio, Zacchiroli, Stefano

    ISSN: 2640-7574
    Published: IEEE 04.03.2025
    “…With the increasing popularity of LLM-based code completers, like GitHub Copilot, the interest in automatically detecting AI-generated code is also increasing-in particular in contexts where the use…”
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    Conference Proceeding
  19. 19

    GPTSniffer: A CodeBERT-based classifier to detect source code written by ChatGPT by Nguyen, Phuong T., Di Rocco, Juri, Di Sipio, Claudio, Rubei, Riccardo, Di Ruscio, Davide, Di Penta, Massimiliano

    ISSN: 0164-1212, 1873-1228
    Published: Elsevier Inc 01.08.2024
    Published in The Journal of systems and software (01.08.2024)
    “…, in software education) rather than as a replacement for humans. Thus, detecting automatically generated source code by ChatGPT is necessary, and tools for identifying AI-generated content need to be adapted to work effectively with code…”
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    Journal Article
  20. 20

    Generative AI for thematic analysis in a maternal health study: coding semistructured interviews using large language models by Qiao, Shan, Fang, Xingyu, Wang, Junbo, Zhang, Ran, Li, Xiaoming, Kang, Yuhao

    ISSN: 1758-0846, 1758-0854, 1758-0854
    Published: England Wiley Subscription Services, Inc 01.06.2025
    Published in Applied psychology : health and well-being (01.06.2025)
    “…: Using transcripts from interviews conducted with maternity care providers in South Carolina, we leveraged ChatGPT for inductive coding to generate codes from interview transcripts without a predetermined coding scheme…”
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    Journal Article