# AIGCodeSet: A New Annotated Dataset for AI Generated Code Detection

arXiv: https://arxiv.org/abs/2412.16594 · HTML: https://mlsanity.com/paper/2412.16594/

## 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 (most cited first, 16 of 16)

- [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://mlsanity.com/paper/2403.05530/) (2024)
- [Code Llama: Open Foundation Models for Code](https://mlsanity.com/paper/2308.12950/) (2023)
- [Large Language Models for Software Engineering: A Systematic Literature Review](https://mlsanity.com/paper/2308.10620/) (2023)
- [CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks](https://mlsanity.com/paper/2105.12655/) (2021)
- [Do Users Write More Insecure Code with AI Assistants?](https://mlsanity.com/paper/2211.03622/) (2022)
- [Programming Is Hard -- Or at Least It Used to Be: Educational Opportunities And Challenges of AI Code Generation](https://mlsanity.com/paper/2212.01020/) (2022)
- [Refining ChatGPT-Generated Code: Characterizing and Mitigating Code Quality Issues](https://mlsanity.com/paper/2307.12596/) (2023)
- ["It's Weird That it Knows What I Want": Usability and Interactions with Copilot for Novice Programmers](https://mlsanity.com/paper/2304.02491/) (2023)
- [Large Language Models and Simple, Stupid Bugs](https://mlsanity.com/paper/2303.11455/) (2023)
- [Toward Secure Code Generation: Bridging Correctness and Security via Task-Adaptive Vulnerability Modeling and Execution-Based Benchmarking](https://mlsanity.com/paper/2407.02395/) (2024)
- [Assessing AI Detectors in Identifying AI-Generated Code: Implications for Education](https://mlsanity.com/paper/2401.03676/) (2024)
- [Evaluating AIGC Detectors on Code Content](https://mlsanity.com/paper/2304.05193/) (2023)
- [Whodunit: Classifying Code as Human Authored or GPT-4 Generated -- A case study on CodeChef problems](https://mlsanity.com/paper/2403.04013/) (2024)
- [DeVAIC: A Tool for Security Assessment of AI-generated Code](https://mlsanity.com/paper/2404.07548/) (2024)
- [ChatGPT Code Detection: Techniques for Uncovering the Source of Code](https://mlsanity.com/paper/2405.15512/) (2024)
- [Is This You, LLM? Recognizing AI-written Programs with Multilingual Code Stylometry](https://mlsanity.com/paper/2412.14611/) (2024)

## Built on by (most cited first, 5 of 5)

- [CodeMirage: A Multi-Lingual Benchmark for Detecting AI-Generated and Paraphrased Source Code from Production-Level LLMs](https://mlsanity.com/paper/2506.11059/) (2025)
- [MultiAIGCD: A Comprehensive dataset for AI Generated Code Detection Covering Multiple Languages, Models,Prompts, and Scenarios](https://mlsanity.com/paper/2507.21693/) (2025)
- [Zero-Shot Detection of LLM-Generated Code via Approximated Task Conditioning](https://mlsanity.com/paper/2506.06069/) (2025)
- [code_transformed: The Influence of Large Language Models on Code](https://mlsanity.com/paper/2506.12014/) (2025)
- [DuoLens: A Framework for Robust Detection of Machine-Generated Multilingual Text and Code](https://mlsanity.com/paper/2510.18904/) (2025)

## 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.