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Assessing AI Detectors in Identifying AI-Generated Code: Implications for Education

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arxiv 2401.03676 v1 pith:HV754WXG submitted 2024-01-08 cs.SE cs.AI

Wei Hung Pan , Ming Jie Chok , Jonathan Leong Shan Wong , Yung Xin Shin , Yeong Shian Poon , Zhou Yang , Chun Yong Chong , David Lo
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Mei Kuan Lim
This is my paper
classification cs.SEcs.AI
keywords codedetectorsaigcai-generatedchatgptdataseteducationhuman-written
verification ladder T0 review T1 audit T2 compute T3 formal
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Educators are increasingly concerned about the usage of Large Language Models (LLMs) such as ChatGPT in programming education, particularly regarding the potential exploitation of imperfections in Artificial Intelligence Generated Content (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. This is achieved by generating code in response to a given question using different variants. We collected a dataset comprising 5,069 samples, with each sample consisting of a textual description of a coding problem and its corresponding human-written Python solution codes. These samples were obtained from various sources, including 80 from Quescol, 3,264 from Kaggle, and 1,725 from LeetCode. From the dataset, we created 13 sets of code problem variant prompts, which were used to instruct ChatGPT to generate the outputs. Subsequently, we assessed the performance of five AIGC detectors. Our results demonstrate that existing AIGC Detectors perform poorly in distinguishing between human-written code and AI-generated code.

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Cited by 2 Pith papers

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  1. Detecting LLM-generated Code with Subtle Modification by Adversarial Training

    cs.SE 2025-07 conditional novelty 6.0 of 10

    CodeGPTSensor+, trained with adversarial samples that combine identifier renaming and structure transformation, is substantially more robust to subtle modifications of LLM-generated code than the original CodeGPTSensor.

  2. I Know Which LLM Wrote Your Code Last Summer: LLM generated Code Stylometry for Authorship Attribution

    cs.LG 2025-06 conditional novelty 6.0 of 10

    A fine-tuned encoder-only CodeT5 model attributes LLM-generated C code to its source model with up to 97.56% binary and 95.40% five-class accuracy on a new 32,000-program benchmark.

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