We present a neural model for representing snippets of code as continuous
distributed vectors ("code embeddings"). The main idea is to represent a code
snippet as a single fixed-length $\textit{code vector}$, which can be used to
predict semantic properties of the snippet. This is performed by decomposing
code to a collection of paths in its abstract syntax tree, and learning the
atomic representation of each path $\textit{simultaneously}$ with learning how
to aggregate a set of them. We demonstrate the effectiveness of our approach by
using it to predict a method's name from the vector representation of its body.
We evaluate our approach by training a model on a dataset of 14M methods. We
show that code vectors trained on this dataset can predict method names from
files that were completely unobserved during training. Furthermore, we show
that our model learns useful method name vectors that capture semantic
similarities, combinations, and analogies. Comparing previous techniques over
the same data set, our approach obtains a relative improvement of over 75%,
being the first to successfully predict method names based on a large,
cross-project, corpus. Our trained model, visualizations and vector
similarities are available as an interactive online demo at
this http URL The code, data, and trained models are available at
this https URL

本文介绍了一种基于神经网络模型的代码嵌入方法，通过将代码片段表示为单一的固定长度代码向量，学习抽象语法树中路径的原子表示，并同时学习集合聚合的方法，从而预测代码的语义属性。通过训练一个包含 14M 个方法的数据集，我们证明了该方法的有效性，并通过相应的数据比较表明，该方法比先前的技术有着相对提高超过 75％，是第一个成功预测大型跨项目语料库上的方法名的方法。