  Neural code summarization leverages deep learning models to automatically
generate brief natural language summaries of code snippets. The development of
Transformer models has led to extensive use of attention during model design.
While existing work has primarily and almost exclusively focused on static
properties of source code and related structural representations like the
Abstract Syntax Tree (AST), few studies have considered human attention, that
is, where programmers focus while examining and comprehending code. In this
paper, we develop a method for incorporating human attention into machine
attention to enhance neural code summarization. To facilitate this
incorporation and vindicate this hypothesis, we introduce EyeTrans, which
consists of three steps: (1) we conduct an extensive eye-tracking human study
to collect and pre-analyze data for model training, (2) we devise a
data-centric approach to integrate human attention with machine attention in
the Transformer architecture, and (3) we conduct comprehensive experiments on
two code summarization tasks to demonstrate the effectiveness of incorporating
human attention into Transformers. Integrating human attention leads to an
improvement of up to 29.91% in Functional Summarization and up to 6.39% in
General Code Summarization performance, demonstrating the substantial benefits
of this combination. We further explore performance in terms of robustness and
efficiency by creating challenging summarization scenarios in which EyeTrans
exhibits interesting properties. We also visualize the attention map to depict
the simplifying effect of machine attention in the Transformer by incorporating
human attention. This work has the potential to propel AI research in software
engineering by introducing more human-centered approaches and data.
