  Deep learning models have been successfully applied to a variety of software
engineering tasks, such as code classification, summarisation, and bug and
vulnerability detection. In order to apply deep learning to these tasks, source
code needs to be represented in a format that is suitable for input into the
deep learning model. Most approaches to representing source code, such as
tokens, abstract syntax trees (ASTs), data flow graphs (DFGs), and control flow
graphs (CFGs) only focus on the code itself and do not take into account
additional context that could be useful for deep learning models. In this
paper, we argue that it is beneficial for deep learning models to have access
to additional contextual information about the code being analysed. We present
preliminary evidence that encoding context from the call hierarchy along with
information from the code itself can improve the performance of a
state-of-the-art deep learning model for two software engineering tasks. We
outline our research agenda for adding further contextual information to source
code representations for deep learning.
