# Whodunit: Classifying Code as Human Authored or GPT-4 Generated -- A case study on CodeChef problems

arXiv: https://arxiv.org/abs/2403.04013 · HTML: https://mlsanity.com/paper/2403.04013/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2024.

## Built on by (most cited first, 13 of 13)

- [CoDet-M4: Detecting Machine-Generated Code in Multi-Lingual, Multi-Generator and Multi-Domain Settings](https://mlsanity.com/paper/2503.13733/) (2025)
- [textttDroid: A Resource Suite for AI-Generated Code Detection](https://mlsanity.com/paper/2507.10583/) (2025)
- [CodeMirage: A Multi-Lingual Benchmark for Detecting AI-Generated and Paraphrased Source Code from Production-Level LLMs](https://mlsanity.com/paper/2506.11059/) (2025)
- [AIGCodeSet: A New Annotated Dataset for AI Generated Code Detection](https://mlsanity.com/paper/2412.16594/) (2024)
- [MultiAIGCD: A Comprehensive dataset for AI Generated Code Detection Covering Multiple Languages, Models,Prompts, and Scenarios](https://mlsanity.com/paper/2507.21693/) (2025)
- [The Hidden DNA of LLM-Generated JavaScript: Structural Patterns Enable High-Accuracy Authorship Attribution](https://mlsanity.com/paper/2510.10493/) (2025)
- [AICD Bench: A Challenging Benchmark for AI-Generated Code Detection](https://mlsanity.com/paper/2602.02079/) (2026)
- [Automatic Detection of LLM-Generated Code: A Comparative Case Study of Contemporary Models Across Function and Class Granularities](https://mlsanity.com/paper/2409.01382/) (2024)
- [On Developers' Self-Declaration of AI-Generated Code: An Analysis of Practices](https://mlsanity.com/paper/2504.16485/) (2025)
- [Do AI Agents Really Improve Code Readability?](https://mlsanity.com/paper/2603.13723/) (2026)
- [LLMSniffer: Detecting LLM-Generated Code via GraphCodeBERT and Supervised Contrastive Learning](https://mlsanity.com/paper/2604.16058/) (2026)
- [HybridCodeAuthorship: A Benchmark Dataset for Line-Level Code Authorship Detection](https://mlsanity.com/paper/2606.12620/) (2026)
- [Synthetic Semantic Supervision for Contrastive Code Representation Learning in Small Transformers: An Empirical Study](https://mlsanity.com/paper/2609.03702/) (2026)

## Abstract (arXiv)

Artificial intelligence (AI) assistants such as GitHub Copilot and ChatGPT, built on large language models like GPT-4, are revolutionizing how programming tasks are performed, raising questions about whether code is authored by generative AI models. Such questions are of particular interest to educators, who worry that these tools enable a new form of academic dishonesty, in which students submit AI generated code as their own work. Our research explores the viability of using code stylometry and machine learning to distinguish between GPT-4 generated and human-authored code. Our dataset comprises human-authored solutions from CodeChef and AI-authored solutions generated by GPT-4. Our classifier outperforms baselines, with an F1-score and AUC-ROC score of 0.91. A variant of our classifier that excludes gameable features (e.g., empty lines, whitespace) still performs well with an F1-score and AUC-ROC score of 0.89. We also evaluated our classifier with respect to the difficulty of the programming problem and found that there was almost no difference between easier and intermediate problems, and the classifier performed only slightly worse on harder problems. Our study shows that code stylometry is a promising approach for distinguishing between GPT-4 generated code and human-authored code.