论文标题
神经嵌入中程序的句法和语义表示的比较
Comparison of Syntactic and Semantic Representations of Programs in Neural Embeddings
论文作者
论文摘要
在过去的几年中，神经方法的综合和理解方法已广泛增殖。同时，基于图形的神经网络已成为有希望的新工具。这项工作旨在是第一个经验研究，比较了自然语言模型和基于静态分析图表模型在代表深度学习系统中的程序中的有效性。它在程序嵌入的任务中使用不同的图表比较图形卷积网络。它表明，控制流程图的稀疏性和图形卷积网络的隐式聚集导致这些模型的性能比幼稚的模型差。因此，它得出的结论是，由于形式属性的细微差别，纯粹的语言或统计模型使用正式信息的表现不佳，而与图形卷积网络的结构相比，引入更多的噪声。
Neural approaches to program synthesis and understanding have proliferated widely in the last few years; at the same time graph based neural networks have become a promising new tool. This work aims to be the first empirical study comparing the effectiveness of natural language models and static analysis graph based models in representing programs in deep learning systems. It compares graph convolutional networks using different graph representations in the task of program embedding. It shows that the sparsity of control flow graphs and the implicit aggregation of graph convolutional networks cause these models to perform worse than naive models. Therefore it concludes that simply augmenting purely linguistic or statistical models with formal information does not perform well due to the nuanced nature of formal properties introducing more noise than structure for graph convolutional networks.
