Source code summarization is the task of writing natural language
descriptions of source code. A typical use case is generating short summaries
of subroutines for use in API documentation. The heart of almost all current
research into code summarization is the encoder-decoder neural architecture,
and the encoder input is almost always a single subroutine or other short code
snippet. The problem with this setup is that the information needed to describe
the code is often not present in the code itself -- that information often
resides in other nearby code. In this paper, we revisit the idea of ``file
context'' for code summarization. File context is the idea of encoding select
information from other subroutines in the same file. We propose a novel
modification of the Transformer architecture that is purpose-built to encode
file context and demonstrate its improvement over several baselines. We find
that file context helps on a subset of challenging examples where traditional
approaches struggle.

代码摘要是编写源代码的自然语言描述的任务，这篇论文重新考虑了 “文件上下文” 的概念，提出了一种专门用于编码文件上下文的修改过的 Transformer 架构，并证明它相较于几个基线方法取得了改进。

重访源代码概要的文件上下文

Revisiting File Context for Source Code Summarization

Source code summarization is the task of writing natural language
descriptions of source code behavior. Code summarization underpins software
documentation for programmers. Short descriptions of code help programmers
understand the program quickly without having to read the code itself. Lately,
neural source code summarization has emerged as the frontier of research into
automated code summarization techniques. By far the most popular targets for
summarization are program subroutines. The idea, in a nutshell, is to train an
encoder-decoder neural architecture using large sets of examples of subroutines
extracted from code repositories. The encoder represents the code and the
decoder represents the summary. However, most current approaches attempt to
treat the subroutine as a single unit. For example, by taking the entire
subroutine as input to a Transformer or RNN-based encoder. But code behavior
tends to depend on the flow from statement to statement. Normally dynamic
analysis may shed light on this flow, but dynamic analysis on hundreds of
thousands of examples in large datasets is not practical. In this paper, we
present a statement-based memory encoder that learns the important elements of
flow during training, leading to a statement-based subroutine representation
without the need for dynamic analysis. We implement our encoder for code
summarization and demonstrate a significant improvement over the
state-of-the-art.

源代码摘要是编写源代码行为的自然语言描述的任务。近来，神经源代码摘要已成为研究自动化代码摘要技术的前沿，本文介绍了一种基于语句的记忆编码器，通过训练学习了流程的重要元素，从而实现了基于语句的子程序表示，并展示了与最新技术相比的显著改进。