论文标题
语法指导的计划减少以了解神经代码智能模型
Syntax-Guided Program Reduction for Understanding Neural Code Intelligence Models
论文作者
论文摘要
神经代码智能（CI）模型是不透明的黑盒，几乎没有关于他们在预测中使用的功能的见解。这种不透明度可能会导致他们的预测不信任，并阻碍其在安全至关重要的应用中的广泛采用。最近，已经提出了输入程序减少技术来确定输入程序中的关键特征，以提高CI模型的透明度。但是，这种方法是语法 - 诺瓦雷，不考虑编程语言的语法。在本文中，我们采用了语法引导的减少技术，该技术在减少过程中考虑了输入程序的语法。我们对不同类型输入程序的多个模型进行的实验表明，语法引导的程序减少技术更快，并且在简化程序中提供了较小的关键令牌集。我们还表明，关键令牌可用于生成对抗性示例，最多可用于65％的输入程序。
Neural code intelligence (CI) models are opaque black-boxes and offer little insight on the features they use in making predictions. This opacity may lead to distrust in their prediction and hamper their wider adoption in safety-critical applications. Recently, input program reduction techniques have been proposed to identify key features in the input programs to improve the transparency of CI models. However, this approach is syntax-unaware and does not consider the grammar of the programming language. In this paper, we apply a syntax-guided program reduction technique that considers the grammar of the input programs during reduction. Our experiments on multiple models across different types of input programs show that the syntax-guided program reduction technique is faster and provides smaller sets of key tokens in reduced programs. We also show that the key tokens could be used in generating adversarial examples for up to 65% of the input programs.
