# Code2Snapshot: Using Code Snapshots for Learning Representations of Source Code

arXiv: https://arxiv.org/abs/2111.01097 · HTML: https://mlsanity.com/paper/2111.01097/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 33 references that are in the map.
- First posted on arXiv 1 Nov 2021.

## Builds on (most cited first, 21 of 21)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [code2vec: Learning Distributed Representations of Code](https://mlsanity.com/paper/1803.09473/) (2018)
- [A Survey of Machine Learning for Big Code and Naturalness](https://mlsanity.com/paper/1709.06182/) (2017)
- [Learning to Represent Programs with Graphs](https://mlsanity.com/paper/1711.00740/) (2017)
- [Machine Learning Testing: Survey, Landscapes and Horizons](https://mlsanity.com/paper/1906.10742/) (2019)
- [code2seq: Generating Sequences from Structured Representations of Code](https://mlsanity.com/paper/1808.01400/) (2018)
- [The Adverse Effects of Code Duplication in Machine Learning Models of Code](https://mlsanity.com/paper/1812.06469/) (2018)
- [A Convolutional Attention Network for Extreme Summarization of Source Code](https://mlsanity.com/paper/1602.03001/) (2016)
- [On the Generalizability of Neural Program Models with respect to Semantic-Preserving Program Transformations](https://mlsanity.com/paper/2008.01566/) (2020)
- [Learning Natural Coding Conventions](https://mlsanity.com/paper/1402.4182/) (2014)
- [A Survey on Machine Learning Techniques for Source Code Analysis](https://mlsanity.com/paper/2110.09610/) (2021)
- [Understanding Neural Code Intelligence Through Program Simplification](https://mlsanity.com/paper/2106.03353/) (2021)
- [Memorization and Generalization in Neural Code Intelligence Models](https://mlsanity.com/paper/2106.08704/) (2021)
- [A Literature Study of Embeddings on Source Code](https://mlsanity.com/paper/1904.03061/) (2019)
- [Probing Model Signal-Awareness via Prediction-Preserving Input Minimization](https://mlsanity.com/paper/2011.14934/) (2020)
- [Embedding Java Classes with code2vec: Improvements from Variable Obfuscation](https://mlsanity.com/paper/2004.02942/) (2020)
- [Towards Demystifying Dimensions of Source Code Embeddings](https://mlsanity.com/paper/2008.13064/) (2020)
- [What You See is What it Means! Semantic Representation Learning of Code based on Visualization and Transfer Learning](https://mlsanity.com/paper/2002.02650/) (2020)
- [Code2Image: Intelligent Code Analysis by Computer Vision Techniques and Application to Vulnerability Prediction](https://mlsanity.com/paper/2105.03131/) (2021)

## Built on by (most cited first, 4 of 4)

- [Syntax-Guided Program Reduction for Understanding Neural Code Intelligence Models](https://mlsanity.com/paper/2205.14374/) (2022)
- [Study of Distractors in Neural Models of Code](https://mlsanity.com/paper/2303.01739/) (2023)
- [Extracting Label-specific Key Input Features for Neural Code Intelligence Models](https://mlsanity.com/paper/2202.06474/) (2022)
- [A Study of Variable-Role-based Feature Enrichment in Neural Models of Code](https://mlsanity.com/paper/2303.04942/) (2023)

## Abstract (arXiv)

There are several approaches for encoding source code in the input vectors of neural models. These approaches attempt to include various syntactic and semantic features of input programs in their encoding. In this paper, we investigate Code2Snapshot, a novel representation of the source code that is based on the snapshots of input programs. We evaluate several variations of this representation and compare its performance with state-of-the-art representations that utilize the rich syntactic and semantic features of input programs. Our preliminary study on the utility of Code2Snapshot in the code summarization and code classification tasks suggests that simple snapshots of input programs have comparable performance to state-of-the-art representations. Interestingly, obscuring input programs have insignificant impacts on the Code2Snapshot performance, suggesting that, for some tasks, neural models may provide high performance by relying merely on the structure of input programs.