Does Your Neural Code Completion Model Use My Code? A Membership Inference Approach

📄 arXiv: 2404.14296v2 📥 PDF

作者: Yao Wan, Guanghua Wan, Shijie Zhang, Hongyu Zhang, Pan Zhou, Hai Jin, Lichao Sun

分类: cs.SE, cs.AI

发布日期: 2024-04-22 (更新: 2024-09-07)

💡 一句话要点

提出CodeMI以解决神经代码补全模型的成员推断问题

🎯 匹配领域: 支柱九：具身大模型 (Embodied Foundation Models)

关键词: 代码补全 成员推断 深度学习 伦理问题 模型透明性 影子模型 版权问题

📋 核心要点

  1. 现有神经代码补全模型在使用开源代码训练时可能引发法律和伦理问题，尤其是版权侵犯。
  2. 论文提出了一种名为CodeMI的成员推断方法，通过训练影子模型来推断特定代码是否用于训练目标模型。
  3. 实验结果显示，LSTM和CodeGPT模型的成员泄漏问题明显，而CodeGen和StarCoder的成员状态则难以检测，提供了改进的方向。

📝 摘要（中文）

近年来，基于深度学习的自动代码补全模型取得了显著进展。然而，使用GitHub上的源代码训练这些模型可能引发法律和伦理问题。本文探讨了当前神经代码补全模型的法律和伦理问题，提出了一种名为CodeMI的成员推断方法，旨在判断特定代码是否用于训练目标模型。通过训练多个影子模型来模拟目标模型的行为，利用其输出训练成员分类器，从而有效推断给定代码样本的成员状态。实验结果表明，LSTM和CodeGPT模型存在成员泄漏问题，而CodeGen和StarCoder的成员状态则较难检测，显示出进一步改进的空间。

🔬 方法详解

问题定义：本文旨在解决神经代码补全模型的成员推断问题，现有方法在透明性和可访问性方面存在不足，无法有效判断特定代码是否用于训练目标模型。

核心思路：通过构建影子模型来模拟目标模型的行为，利用影子模型的输出训练一个成员分类器，从而推断给定代码样本的成员状态。此方法克服了目标模型作为黑箱的限制。

技术框架：整体流程包括训练多个影子模型以获取其输出，利用这些输出训练成员分类器，最后通过分类器判断特定代码的成员状态。主要模块包括影子模型训练、输出获取和成员分类器训练。

关键创新：最重要的创新在于将成员推断方法从分类任务扩展到代码补全任务，提出了适应性强的CodeMI方法，显著提升了成员推断的有效性。

关键设计：在模型设计中，影子模型的数量和结构、损失函数的选择以及分类器的训练策略都是关键因素，确保了方法的准确性和鲁棒性。

🖼️ 关键图片

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📊 实验亮点

实验结果显示，LSTM和CodeGPT模型的成员泄漏问题明显，准确率分别为0.842和0.730，而CodeGen和StarCoder的成员状态则难以检测，显示出该方法在不同模型上的适应性和有效性，为未来研究提供了重要参考。

🎯 应用场景

该研究的潜在应用领域包括软件开发、代码审查和开源项目管理。通过有效识别训练数据的来源，开发者可以更好地遵循法律和伦理规范，避免潜在的版权问题。此外，该方法也为未来的代码补全模型提供了改进的方向，促进了模型的透明性和可解释性。

📄 摘要（原文）

Recent years have witnessed significant progress in developing deep learning-based models for automated code completion. Although using source code in GitHub has been a common practice for training deep-learning-based models for code completion, it may induce some legal and ethical issues such as copyright infringement. In this paper, we investigate the legal and ethical issues of current neural code completion models by answering the following question: Is my code used to train your neural code completion model? To this end, we tailor a membership inference approach (termed CodeMI) that was originally crafted for classification tasks to a more challenging task of code completion. In particular, since the target code completion models perform as opaque black boxes, preventing access to their training data and parameters, we opt to train multiple shadow models to mimic their behavior. The acquired posteriors from these shadow models are subsequently employed to train a membership classifier. Subsequently, the membership classifier can be effectively employed to deduce the membership status of a given code sample based on the output of a target code completion model. We comprehensively evaluate the effectiveness of this adapted approach across a diverse array of neural code completion models, (i.e., LSTM-based, CodeGPT, CodeGen, and StarCoder). Experimental results reveal that the LSTM-based and CodeGPT models suffer the membership leakage issue, which can be easily detected by our proposed membership inference approach with an accuracy of 0.842, and 0.730, respectively. Interestingly, our experiments also show that the data membership of current large language models of code, e.g., CodeGen and StarCoder, is difficult to detect, leaving ampler space for further improvement. Finally, we also try to explain the findings from the perspective of model memorization.