An effective and efficient encoding of the source code of a computer program
is critical to the success of sequence-to-sequence deep neural network models
for tasks in computer program comprehension, such as automated code
summarization and documentation. A significant challenge is to find a
sequential representation that captures the structural/syntactic information in
a computer program and facilitates the training of the learning models.
In this paper, we propose to use the Pr\"ufer sequence of the Abstract Syntax
Tree (AST) of a computer program to design a sequential representation scheme
that preserves the structural information in an AST. Our representation makes
it possible to develop deep-learning models in which signals carried by lexical
tokens in the training examples can be exploited automatically and selectively
based on their syntactic role and importance. Unlike other recently-proposed
approaches, our representation is concise and lossless in terms of the
structural information of the AST. Empirical studies on real-world benchmark
datasets, using a sequence-to-sequence learning model we designed for code
summarization, show that our Pr\"ufer-sequence-based representation is indeed
highly effective and efficient, outperforming significantly all the
recently-proposed deep-learning models we used as the baseline models.

本文提出了一种基于 Abstract Syntax Tree 的 Prüfer 序列设计的时序表示方案，以捕获计算机程序的结构 / 句法信息，并用于代码概括的发展深度学习模型。经实验证明，我们的方法具有高效性和优越性。