AIGCodeSet: A New Annotated Dataset for AI Generated Code Detection

• URL: http://arxiv.org/abs/2412.16594v2
• Date: Sun, 09 Mar 2025 10:31:29 GMT
• Title: AIGCodeSet: A New Annotated Dataset for AI Generated Code Detection
• Authors: Basak Demirok, Mucahid Kutlu,
• Abstract summary: We present AIGCodeSet, which consists of 2.828 AI-generated and 4.755 human-written Python codes.<n>Our experiments show that a Bayesian classifier outperforms the other models.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• KodCode: A Diverse, Challenging, and Verifiable Synthetic Dataset for Coding [49.56049319037421]
  KodCode is a synthetic dataset that addresses the persistent challenge of acquiring high-quality, verifiable training data. It comprises question-solution-test triplets that are systematically validated via a self-verification procedure. This pipeline yields a large-scale, robust and diverse coding dataset.
  arXiv  Detail & Related papers  (2025-03-04T19:17:36Z)
• ChatGPT vs. DeepSeek: A Comparative Study on AI-Based Code Generation [0.0]
  This research compares ChatGPT and DeepSeek for Python code generation using online judge coding challenges. It evaluates correctness (online judge verdicts, up to three attempts), code quality (Pylint/Flake8), and efficiency (execution time/memory usage) DeepSeek demonstrated higher correctness, particularly on algorithmic tasks, often achieving 'Accepted' on the first attempt.
  arXiv  Detail & Related papers  (2025-01-30T16:14:48Z)
• An Empirical Study on Automatically Detecting AI-Generated Source Code: How Far Are We? [8.0988059417354]
  We propose a range of approaches to improve the performance of AI-generated code detection. Our best model outperforms state-of-the-art AI-generated code detector (GPTSniffer) and achieves an F1 score of 82.55.
  arXiv  Detail & Related papers  (2024-11-06T22:48:18Z)
• VersiCode: Towards Version-controllable Code Generation [58.82709231906735]
  Large Language Models (LLMs) have made tremendous strides in code generation, but existing research fails to account for the dynamic nature of software development. We propose two novel tasks aimed at bridging this gap: version-specific code completion (VSCC) and version-aware code migration (VACM) We conduct an extensive evaluation on VersiCode, which reveals that version-controllable code generation is indeed a significant challenge.
  arXiv  Detail & Related papers  (2024-06-11T16:15:06Z)
• Uncovering LLM-Generated Code: A Zero-Shot Synthetic Code Detector via Code Rewriting [78.48355455324688]
  We propose a novel zero-shot synthetic code detector based on the similarity between the original code and its LLM-rewritten variants.<n>Our results demonstrate a significant improvement over existing SOTA synthetic content detectors.
  arXiv  Detail & Related papers  (2024-05-25T08:57:28Z)
• CodeIP: A Grammar-Guided Multi-Bit Watermark for Large Language Models of Code [56.019447113206006]
  Large Language Models (LLMs) have achieved remarkable progress in code generation. CodeIP is a novel multi-bit watermarking technique that embeds additional information to preserve provenance details. Experiments conducted on a real-world dataset across five programming languages demonstrate the effectiveness of CodeIP.
  arXiv  Detail & Related papers  (2024-04-24T04:25:04Z)
• Does Your Neural Code Completion Model Use My Code? A Membership Inference Approach [66.51005288743153]
  We investigate the legal and ethical issues of current neural code completion models. We tailor a membership inference approach (termed CodeMI) that was originally crafted for classification tasks. We evaluate the effectiveness of this adapted approach across a diverse array of neural code completion models.
  arXiv  Detail & Related papers  (2024-04-22T15:54:53Z)
• CodeCloak: A Method for Evaluating and Mitigating Code Leakage by LLM Code Assistants [22.342331134131744]
  CodeCloak is a novel deep reinforcement learning agent that manipulates the prompts before sending them to the code assistant service. CodeCloak aims to achieve the following two contradictory goals: (i) minimizing code leakage, while (ii) preserving relevant and useful suggestions for the developer.
  arXiv  Detail & Related papers  (2024-04-13T19:30:58Z)
• Iterative Refinement of Project-Level Code Context for Precise Code Generation with Compiler Feedback [29.136378191436396]
  We present CoCoGen, a new code generation approach that uses compiler feedback to improve the LLM-generated code. CoCoGen first leverages static analysis to identify mismatches between the generated code and the project's context. It then iteratively aligns and fixes the identified errors using information extracted from the code repository.
  arXiv  Detail & Related papers  (2024-03-25T14:07:27Z)
• Whodunit: Classifying Code as Human Authored or GPT-4 Generated -- A case study on CodeChef problems [0.13124513975412253]
  We use 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 study shows that code stylometry is a promising approach for distinguishing between GPT-4 generated code and human-authored code.
  arXiv  Detail & Related papers  (2024-03-06T19:51:26Z)
• StarCoder 2 and The Stack v2: The Next Generation [105.93298676368798]
  We train StarCoder2 models with 3B, 7B, and 15B parameters on 3.3 to 4.3 trillion tokens. We thoroughly evaluate them on a comprehensive set of Code LLM benchmarks. Our large model, StarCoder2- 15B, significantly outperforms other models of comparable size.
  arXiv  Detail & Related papers  (2024-02-29T13:53:35Z)
• Assessing AI Detectors in Identifying AI-Generated Code: Implications for Education [8.592066814291819]
  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. Our results demonstrate that existing AIGC Detectors perform poorly in distinguishing between human-written code and AI-generated code.
  arXiv  Detail & Related papers  (2024-01-08T05:53:52Z)
• CodeT5+: Open Code Large Language Models for Code Understanding and Generation [72.1638273937025]
  Large language models (LLMs) pretrained on vast source code have achieved prominent progress in code intelligence. CodeT5+ is a family of encoder-decoder LLMs for code in which component modules can be flexibly combined to suit a wide range of downstream code tasks. We extensively evaluate CodeT5+ on over 20 code-related benchmarks in different settings, including zero-shot, finetuning, and instruction-tuning.
  arXiv  Detail & Related papers  (2023-05-13T14:23:07Z)
• Chatbots As Fluent Polyglots: Revisiting Breakthrough Code Snippets [0.0]
  The research applies AI-driven code assistants to analyze a selection of influential computer code that has shaped modern technology. The original contribution of this study was to examine half of the most significant code advances in the last 50 years.
  arXiv  Detail & Related papers  (2023-01-05T23:17:17Z)
• AstBERT: Enabling Language Model for Code Understanding with Abstract Syntax Tree [3.1087379479634927]
  We propose the AstBERT model, a pre-trained language model aiming to better understand the programming language (PL) using the abstract syntax tree (AST) Specifically, we collect a colossal amount of source codes (both java and python) from GitHub, in which information of the source codes can be interpreted and integrated. Experiment results show that our AstBERT model achieves state-of-the-art performance on both downstream tasks.
  arXiv  Detail & Related papers  (2022-01-20T03:27:26Z)
• Measuring Coding Challenge Competence With APPS [54.22600767666257]
  We introduce APPS, a benchmark for code generation. Our benchmark includes 10,000 problems, which range from having simple one-line solutions to being substantial algorithmic challenges. Recent models such as GPT-Neo can pass approximately 15% of the test cases of introductory problems.
  arXiv  Detail & Related papers  (2021-05-20T17:58:42Z)
• COSEA: Convolutional Code Search with Layer-wise Attention [90.35777733464354]
  We propose a new deep learning architecture, COSEA, which leverages convolutional neural networks with layer-wise attention to capture the code's intrinsic structural logic. COSEA can achieve significant improvements over state-of-the-art methods on code search tasks.
  arXiv  Detail & Related papers  (2020-10-19T13:53:38Z)
• Incorporating External Knowledge through Pre-training for Natural Language to Code Generation [97.97049697457425]
  Open-domain code generation aims to generate code in a general-purpose programming language from natural language (NL) intents. We explore the effectiveness of incorporating two varieties of external knowledge into NL-to-code generation: automatically mined NL-code pairs from the online programming QA forum StackOverflow and programming language API documentation. Our evaluations show that combining the two sources with data augmentation and retrieval-based data re-sampling improves the current state-of-the-art by up to 2.2% absolute BLEU score on the code generation testbed CoNaLa.
  arXiv  Detail & Related papers  (2020-04-20T01:45:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.