# Adding Context to Source Code Representations for Deep Learning

arXiv: https://arxiv.org/abs/2208.00203 · HTML: https://mlsanity.com/paper/2208.00203/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 33 references that are in the map.
- First posted on arXiv 30 Jul 2022.

## Builds on (most cited first, 5 of 5)

- [Evaluating the visualization of what a Deep Neural Network has learned](https://mlsanity.com/paper/1509.06321/) (2015)
- [Methods for Interpreting and Understanding Deep Neural Networks](https://mlsanity.com/paper/1706.07979/) (2017)
- [Pooling Methods in Deep Neural Networks, a Review](https://mlsanity.com/paper/2009.07485/) (2020)
- [Improved Automatic Summarization of Subroutines via Attention to File Context](https://mlsanity.com/paper/2004.04881/) (2020)
- [Does This Apply to Me? An Empirical Study of Technical Context in Stack Overflow](https://mlsanity.com/paper/2204.00110/) (2022)

## Built on by (most cited first, 2 of 2)

- [JEMMA: An Extensible Java Dataset for ML4Code Applications](https://mlsanity.com/paper/2212.09132/) (2022)
- [Enhancing Neural Code Representation with Additional Context](https://mlsanity.com/paper/2510.12082/) (2025)

## Abstract (arXiv)

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.