# CONCORD: Towards a DSL for Configurable Graph Code Representation

arXiv: https://arxiv.org/abs/2401.17967 · HTML: https://mlsanity.com/paper/2401.17967/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 26 of its 69 references that are in the map.
- First posted on arXiv 31 Jan 2024.

## Builds on (most cited first, 26 of 26)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [Gated Graph Sequence Neural Networks](https://mlsanity.com/paper/1511.05493/) (2015)
- [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)
- [SySeVR: A Framework for Using Deep Learning to Detect Software Vulnerabilities](https://mlsanity.com/paper/1807.06756/) (2018)
- [Retrieval-Augmented Generation for Code Summarization via Hybrid GNN](https://mlsanity.com/paper/2006.05405/) (2020)
- [Improved Code Summarization via a Graph Neural Network](https://mlsanity.com/paper/2004.02843/) (2020)
- [Detecting Code Clones with Graph Neural Networkand Flow-Augmented Abstract Syntax Tree](https://mlsanity.com/paper/2002.08653/) (2020)
- [Multi-Modal Attention Network Learning for Semantic Source Code Retrieval](https://mlsanity.com/paper/1909.13516/) (2019)
- [Improved Automatic Summarization of Subroutines via Attention to File Context](https://mlsanity.com/paper/2004.04881/) (2020)
- [Combining Graph Neural Networks with Expert Knowledge for Smart Contract Vulnerability Detection](https://mlsanity.com/paper/2107.11598/) (2021)
- [Project-Level Encoding for Neural Source Code Summarization of Subroutines](https://mlsanity.com/paper/2103.11599/) (2021)
- [A Survey on Machine Learning Techniques for Source Code Analysis](https://mlsanity.com/paper/2110.09610/) (2021)
- [Diet Code Is Healthy: Simplifying Programs for Pre-trained Models of Code](https://mlsanity.com/paper/2206.14390/) (2022)
- [GraphSearchNet: Enhancing GNNs via Capturing Global Dependencies for Semantic Code Search](https://mlsanity.com/paper/2111.02671/) (2021)
- [Semantic Code Repair using Neuro-Symbolic Transformation Networks](https://mlsanity.com/paper/1710.11054/) (2017)
- [A domain-specific language for describing machine learning datasets](https://mlsanity.com/paper/2207.02848/) (2022)
- [Multimodal Representation for Neural Code Search](https://mlsanity.com/paper/2107.00992/) (2021)
- [Modeling Functional Similarity in Source Code with Graph-Based Siamese Networks](https://mlsanity.com/paper/2011.11228/) (2020)
- [Assessing Software Defection Prediction Performance: Why Using the Matthews Correlation Coefficient Matters](https://mlsanity.com/paper/2003.01182/) (2020)
- [A Library for Representing Python Programs as Graphs for Machine Learning](https://mlsanity.com/paper/2208.07461/) (2022)
- [RMove: Recommending Move Method Refactoring Opportunities using Structural and Semantic Representations of Code](https://mlsanity.com/paper/2212.12195/) (2022)
- [DACOS-A Manually Annotated Dataset of Code Smells](https://mlsanity.com/paper/2303.08729/) (2023)

## Abstract (arXiv)

Deep learning is widely used to uncover hidden patterns in large code corpora. To achieve this, constructing a format that captures the relevant characteristics and features of source code is essential. Graph-based representations have gained attention for their ability to model structural and semantic information. However, existing tools lack flexibility in constructing graphs across different programming languages, limiting their use. Additionally, the output of these tools often lacks interoperability and results in excessively large graphs, making graph-based neural networks training slower and less scalable. We introduce CONCORD, a domain-specific language to build customizable graph representations. It implements reduction heuristics to reduce graphs' size complexity. We demonstrate its effectiveness in code smell detection as an illustrative use case and show that: first, CONCORD can produce code representations automatically per the specified configuration, and second, our heuristics can achieve comparable performance with significantly reduced size. CONCORD will help researchers a) create and experiment with customizable graph-based code representations for different software engineering tasks involving DL, b) reduce the engineering work to generate graph representations, c) address the issue of scalability in GNN models, and d) enhance the reproducibility of experiments in research through a standardized approach to code representation and analysis.