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AIGCodeSet: A New Annotated Dataset for AI Generated Code Detection

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arxiv 2412.16594 v3 pith:XAYZRRXQ submitted 2024-12-21 cs.SE cs.AI

Basak Demirok , Mucahid Kutlu This is my paper · ORCID
classification cs.SEcs.AI
keywords aigcodesetcodedetectionexperimentsgeneratedmodelsadditionai-generated
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Zero-Shot Detection of LLM-Generated Code via Approximated Task Conditioning

    cs.CL 2025-06 conditional novelty 7.0 of 10

    ATC detects AI-generated code by asking a language model to reconstruct the programming task, then scoring token entropy under that reconstructed task, outperforming prior zero-shot detectors on Python, C++, and Java ...

  2. CodeMirage: A Multi-Lingual Benchmark for Detecting AI-Generated and Paraphrased Source Code from Production-Level LLMs

    cs.SE 2025-05 conditional novelty 6.0 of 10

    CodeMirage is a ten-language, ten-LLM benchmark with original and paraphrased AI code, and it shows current AI-generated-code detectors drop sharply under cross-model and low-false-alarm settings.

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