Is this Snippet Written by ChatGPT? An Empirical Study with a CodeBERT-Based Classifier

• URL: http://arxiv.org/abs/2307.09381v2
• Date: Mon, 7 Aug 2023 07:41:37 GMT
• Title: Is this Snippet Written by ChatGPT? An Empirical Study with a CodeBERT-Based Classifier
• Authors: Phuong T. Nguyen, Juri Di Rocco, Claudio Di Sipio, Riccardo Rubei, Davide Di Ruscio, Massimiliano Di Penta
• Abstract summary: This paper presents an empirical study to investigate the feasibility of automated identification of AI-generated code snippets. We propose a novel approach called GPTSniffer, which builds on top of CodeBERT to detect source code written by AI. The results show that GPTSniffer can accurately classify whether code is human-written or AI-generated, and outperforms two baselines.
• Score: 13.613735709997911
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Since its launch in November 2022, ChatGPT has gained popularity among users, especially programmers who use it as a tool to solve development problems. However, while offering a practical solution to programming problems, ChatGPT should be mainly used as a supporting tool (e.g., in software education) rather than as a replacement for the human being. Thus, detecting automatically generated source code by ChatGPT is necessary, and tools for identifying AI-generated content may need to be adapted to work effectively with source code. This paper presents an empirical study to investigate the feasibility of automated identification of AI-generated code snippets, and the factors that influence this ability. To this end, we propose a novel approach called GPTSniffer, which builds on top of CodeBERT to detect source code written by AI. The results show that GPTSniffer can accurately classify whether code is human-written or AI-generated, and outperforms two baselines, GPTZero and OpenAI Text Classifier. Also, the study shows how similar training data or a classification context with paired snippets helps to boost classification performances.

Related papers

• You Augment Me: Exploring ChatGPT-based Data Augmentation for Semantic Code Search [47.54163552754051]
  Code search plays a crucial role in software development, enabling developers to retrieve and reuse code using natural language queries. Recently, large language models (LLMs) have made remarkable progress in both natural and programming language understanding and generation. We propose a novel approach ChatDANCE, which utilizes high-quality and diverse augmented data generated by a large language model.
  arXiv  Detail & Related papers  (2024-08-10T12:51:21Z)
• Impact of the Availability of ChatGPT on Software Development: A Synthetic Difference in Differences Estimation using GitHub Data [49.1574468325115]
  ChatGPT is an AI tool that enhances software production efficiency. We estimate ChatGPT's effects on the number of git pushes, repositories, and unique developers per 100,000 people. These results suggest that AI tools like ChatGPT can substantially boost developer productivity, though further analysis is needed to address potential downsides such as low quality code and privacy concerns.
  arXiv  Detail & Related papers  (2024-06-16T19:11:15Z)
• Genetic Auto-prompt Learning for Pre-trained Code Intelligence Language Models [54.58108387797138]
  We investigate the effectiveness of prompt learning in code intelligence tasks. Existing automatic prompt design methods are very limited to code intelligence tasks. We propose Genetic Auto Prompt (GenAP) which utilizes an elaborate genetic algorithm to automatically design prompts.
  arXiv  Detail & Related papers  (2024-03-20T13:37:00Z)
• 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)
• Investigating the Utility of ChatGPT in the Issue Tracking System: An Exploratory Study [5.176434782905268]
  This study examines the interaction between ChatGPT and developers to analyze their prevalent activities and provide a resolution. Our investigation reveals that developers mainly use ChatGPT for brainstorming solutions but often opt to write their code instead of using ChatGPT-generated code.
  arXiv  Detail & Related papers  (2024-02-06T06:03:05Z)
• Fighting Fire with Fire: Can ChatGPT Detect AI-generated Text? [20.37071875344405]
  We evaluate the zero-shot performance of ChatGPT in the task of human-written vs. AI-generated text detection. We empirically investigate if ChatGPT is symmetrically effective in detecting AI-generated or human-written text.
  arXiv  Detail & Related papers  (2023-08-02T17:11:37Z)
• Unmasking the giant: A comprehensive evaluation of ChatGPT's proficiency in coding algorithms and data structures [0.6990493129893112]
  We evaluate ChatGPT's ability to generate correct solutions to the problems fed to it, its code quality, and nature of run-time errors thrown by its code. We look into patterns in the test cases passed in order to gain some insights into how wrong ChatGPT code is in these kinds of situations.
  arXiv  Detail & Related papers  (2023-07-10T08:20:34Z)
• To ChatGPT, or not to ChatGPT: That is the question! [78.407861566006]
  This study provides a comprehensive and contemporary assessment of the most recent techniques in ChatGPT detection. We have curated a benchmark dataset consisting of prompts from ChatGPT and humans, including diverse questions from medical, open Q&A, and finance domains. Our evaluation results demonstrate that none of the existing methods can effectively detect ChatGPT-generated content.
  arXiv  Detail & Related papers  (2023-04-04T03:04:28Z)
• A Complete Survey on Generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 All You Need? [112.12974778019304]
  generative AI (AIGC, a.k.a AI-generated content) has made headlines everywhere because of its ability to analyze and create text, images, and beyond. In the era of AI transitioning from pure analysis to creation, it is worth noting that ChatGPT, with its most recent language model GPT-4, is just a tool out of numerous AIGC tasks. This work focuses on the technological development of various AIGC tasks based on their output type, including text, images, videos, 3D content, etc.
  arXiv  Detail & Related papers  (2023-03-21T10:09:47Z)
• How Generative AI models such as ChatGPT can be (Mis)Used in SPC Practice, Education, and Research? An Exploratory Study [2.0841728192954663]
  Generative Artificial Intelligence (AI) models have the potential to revolutionize Statistical Process Control (SPC) practice, learning, and research. These tools are in the early stages of development and can be easily misused or misunderstood. We explore ChatGPT's ability to provide code, explain basic concepts, and create knowledge related to SPC practice, learning, and research.
  arXiv  Detail & Related papers  (2023-02-17T15:48:37Z)
• Improved Code Summarization via a Graph Neural Network [96.03715569092523]
  In general, source code summarization techniques use the source code as input and outputs a natural language description. We present an approach that uses a graph-based neural architecture that better matches the default structure of the AST to generate these summaries.
  arXiv  Detail & Related papers  (2020-04-06T17:36:42Z)

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.