This paper presents an improved loss function for neural source code
summarization. Code summarization is the task of writing natural language
descriptions of source code. Neural code summarization refers to automated
techniques for generating these descriptions using neural networks. Almost all
current approaches involve neural networks as either standalone models or as
part of a pretrained large language models e.g., GPT, Codex, LLaMA. Yet almost
all also use a categorical cross-entropy (CCE) loss function for network
optimization. Two problems with CCE are that 1) it computes loss over each word
prediction one-at-a-time, rather than evaluating a whole sentence, and 2) it
requires a perfect prediction, leaving no room for partial credit for synonyms.
We propose and evaluate a loss function to alleviate this problem. In essence,
we propose to use a semantic similarity metric to calculate loss over the whole
output sentence prediction per training batch, rather than just loss for each
word. We also propose to combine our loss with traditional CCE for each word,
which streamlines the training process compared to baselines. We evaluate our
approach over several baselines and report an improvement in the vast majority
of conditions.

改进的损失函数用于神经源代码摘要自动生成，并与传统的分类交叉熵相结合，以整个句子预测损失，并提供语义相似性度量。

神经源代码摘要的语义相似性损失

Semantic Similarity Loss for Neural 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。

基于语句的神经源代码摘要的记忆

Statement-based Memory for Neural Source Code Summarization

Neural source code summarization is the task of generating natural language
descriptions of source code behavior using neural networks. A fundamental
component of most neural models is an attention mechanism. The attention
mechanism learns to connect features in source code to specific words to use
when generating natural language descriptions. Humans also pay attention to
some features in code more than others. This human attention reflects
experience and high-level cognition well beyond the capability of any current
neural model. In this paper, we use data from published eye-tracking
experiments to create a model of this human attention. The model predicts which
words in source code are the most important for code summarization. Next, we
augment a baseline neural code summarization approach using our model of human
attention. We observe an improvement in prediction performance of the augmented
approach in line with other bio-inspired neural models.

该研究利用发布的眼动实验数据创建了人类关注模型，并使用它来改进基于神经网络的源代码摘要方法，以预测在生成自然语言描述时代码中最重要的单词。