  Source code summarization of a subroutine is the task of writing a short,
natural language description of that subroutine. The description usually serves
in documentation aimed at programmers, where even brief phrase (e.g.
"compresses data to a zip file") can help readers rapidly comprehend what a
subroutine does without resorting to reading the code itself. Techniques based
on neural networks (and encoder-decoder model designs in particular) have
established themselves as the state-of-the-art. Yet a problem widely recognized
with these models is that they assume the information needed to create a
summary is present within the code being summarized itself - an assumption
which is at odds with program comprehension literature. Thus a current research
frontier lies in the question of encoding source code context into neural
models of summarization. In this paper, we present a project-level encoder to
improve models of code summarization. By project-level, we mean that we create
a vectorized representation of selected code files in a software project, and
use that representation to augment the encoder of state-of-the-art neural code
summarization techniques. We demonstrate how our encoder improves several
existing models, and provide guidelines for maximizing improvement while
controlling time and resource costs in model size.
