mlsanityData to 14 Oct 2026

AIGCodeSet: A New Annotated Dataset for AI Generated Code Detection

arXiv 2412.16594 ↗First posted 21 Dec 2024cs.SE cs.AIcited by 5

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Abstract (arXiv)

While large language models provide significant convenience for software development, they can lead to ethical issues in job interviews and student assignments. Therefore, determining whether a piece of code is written by a human or generated by an artificial intelligence (AI) model is a critical issue. In this study, we present AIGCodeSet, which consists of 2.828 AI-generated and 4.755 human-written Python codes, created using CodeLlama 34B, Codestral 22B, and Gemini 1.5 Flash. In addition, we share the results of our experiments conducted with baseline detection methods. Our experiments show that a Bayesian classifier outperforms the other models.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 16 of its 28 references that are in the map. First posted on arXiv 21 Dec 2024.

Builds on

16 of 28 references.

References · 16

  1. 2024Gemini 1.5: Unlocking multimodal understanding across millions of tokens of contextcited by 3,224
  2. 2023Code Llama: Open Foundation Models for Codecited by 2,139
  3. 2023Large Language Models for Software Engineering: A Systematic Literature Reviewcited by 399
  4. 2021CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Taskscited by 200
  5. 2022Do Users Write More Insecure Code with AI Assistants?cited by 192
  6. 2022Programming Is Hard -- Or at Least It Used to Be: Educational Opportunities And Challenges of AI Code Generationcited by 99
  7. 2023Refining ChatGPT-Generated Code: Characterizing and Mitigating Code Quality Issuescited by 79
  8. 2023"It's Weird That it Knows What I Want": Usability and Interactions with Copilot for Novice Programmerscited by 64
  9. 2023Large Language Models and Simple, Stupid Bugscited by 45
  10. 2024Toward Secure Code Generation: Bridging Correctness and Security via Task-Adaptive Vulnerability Modeling and Execution-Based Benchmarkingcited by 27
  11. 2024Assessing AI Detectors in Identifying AI-Generated Code: Implications for Educationcited by 18
  12. 2024Whodunit: Classifying Code as Human Authored or GPT-4 Generated -- A case study on CodeChef problemscited by 13
4 more
  1. 2023Evaluating AIGC Detectors on Code Contentcited by 12
  2. 2024DeVAIC: A Tool for Security Assessment of AI-generated Codecited by 8
  3. 2024ChatGPT Code Detection: Techniques for Uncovering the Source of Codecited by 6
  4. 2024Is This You, LLM? Recognizing AI-written Programs with Multilingual Code Stylometrycited by 6

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Cited by year

Citing papers per year: 2025: 5.

5 papers.

  1. 2025CodeMirage: A Multi-Lingual Benchmark for Detecting AI-Generated and Paraphrased Source Code from Production-Level LLMscited by 6
  2. 2025MultiAIGCD: A Comprehensive dataset for AI Generated Code Detection Covering Multiple Languages, Models,Prompts, and Scenarioscited by 2
  3. 2025Zero-Shot Detection of LLM-Generated Code via Approximated Task Conditioningcited by 1
  4. 2025code_transformed: The Influence of Large Language Models on Codecited by 0
  5. 2025DuoLens: A Framework for Robust Detection of Machine-Generated Multilingual Text and Codecited by 0