论文标题
关于源代码的复发神经网络中变量的嵌入
On the Embeddings of Variables in Recurrent Neural Networks for Source Code
论文作者
论文摘要
源代码处理在很大程度上依赖于自然语言处理（NLP）中广泛使用的方法，但涉及需要考虑的具体内容以实现更高的质量。该特异性的一个例子是，变量的语义不仅由其名称定义，而且由变量发生的上下文定义。在这项工作中，我们开发了动态嵌入，这是一种经常性的机制，当变量获得有关变量在程序中的作用的更多信息时，可以调整该变量的学习语义。我们表明，在代码完成和错误修复任务中，使用提出的动态嵌入可显着提高复发性神经网络的性能。
Source code processing heavily relies on the methods widely used in natural language processing (NLP), but involves specifics that need to be taken into account to achieve higher quality. An example of this specificity is that the semantics of a variable is defined not only by its name but also by the contexts in which the variable occurs. In this work, we develop dynamic embeddings, a recurrent mechanism that adjusts the learned semantics of the variable when it obtains more information about the variable's role in the program. We show that using the proposed dynamic embeddings significantly improves the performance of the recurrent neural network, in code completion and bug fixing tasks.
