# Deep Graph Matching and Searching for Semantic Code Retrieval

arXiv: https://arxiv.org/abs/2010.12908 · HTML: https://mlsanity.com/paper/2010.12908/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2020.

## Built on by (most cited first, 20 of 20)

- [Position: Graph Foundation Models are Already Here](https://mlsanity.com/paper/2402.02216/) (2024)
- [CoCoSoDa: Effective Contrastive Learning for Code Search](https://mlsanity.com/paper/2204.03293/) (2022)
- [Deep Learning for Code Intelligence: Survey, Benchmark and Toolkit](https://mlsanity.com/paper/2401.00288/) (2023)
- [Analyzing the Performance of Large Language Models on Code Summarization](https://mlsanity.com/paper/2404.08018/) (2024)
- [Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-Art](https://mlsanity.com/paper/2112.12310/) (2021)
- [CODE-MVP: Learning to Represent Source Code from Multiple Views with Contrastive Pre-Training](https://mlsanity.com/paper/2205.02029/) (2022)
- [A Survey on Pretrained Language Models for Neural Code Intelligence](https://mlsanity.com/paper/2212.10079/) (2022)
- [ERASE: Error-Resilient Representation Learning on Graphs for Label Noise Tolerance](https://mlsanity.com/paper/2312.08852/) (2023)
- [Functional Consistency of LLM Code Embeddings: A Self-Evolving Data Synthesis Framework for Benchmarking](https://mlsanity.com/paper/2508.19558/) (2025)
- [NS3: Neuro-Symbolic Semantic Code Search](https://mlsanity.com/paper/2205.10674/) (2022)
- [Towards Accurate Subgraph Similarity Computation via Neural Graph Pruning](https://mlsanity.com/paper/2210.10643/) (2022)
- [Constructing Contrastive samples via Summarization for Text Classification with limited annotations](https://mlsanity.com/paper/2104.05094/) (2021)
- [Is a Single Model Enough? MuCoS: A Multi-Model Ensemble Learning for Semantic Code Search](https://mlsanity.com/paper/2107.04773/) (2021)
- [Training Free Graph Neural Networks for Graph Matching](https://mlsanity.com/paper/2201.05349/) (2022)
- [CSSAM:Code Search via Attention Matching of Code Semantics and Structures](https://mlsanity.com/paper/2208.03922/) (2022)
- [Code Compliance Assessment as a Learning Problem](https://mlsanity.com/paper/2209.04602/) (2022)
- [On the Effectiveness of Adversarial Samples against Ensemble Learning-based Windows PE Malware Detectors](https://mlsanity.com/paper/2309.13841/) (2023)
- [RETROcode: Leveraging a Code Database for Improved Natural Language to Code Generation](https://mlsanity.com/paper/2504.05759/) (2025)
- [Contextual Tokenization for Graph Inverted Indices](https://mlsanity.com/paper/2510.22479/) (2025)
- [Charting the Design Space of Neural Graph Representations for Subgraph Matching](https://mlsanity.com/paper/2510.22897/) (2025)

## Abstract (arXiv)

Code retrieval is to find the code snippet from a large corpus of source code repositories that highly matches the query of natural language description. Recent work mainly uses natural language processing techniques to process both query texts (i.e., human natural language) and code snippets (i.e., machine programming language), however neglecting the deep structured features of query texts and source codes, both of which contain rich semantic information. In this paper, we propose an end-to-end deep graph matching and searching (DGMS) model based on graph neural networks for the task of semantic code retrieval. To this end, we first represent both natural language query texts and programming language code snippets with the unified graph-structured data, and then use the proposed graph matching and searching model to retrieve the best matching code snippet. In particular, DGMS not only captures more structural information for individual query texts or code snippets but also learns the fine-grained similarity between them by cross-attention based semantic matching operations. We evaluate the proposed DGMS model on two public code retrieval datasets with two representative programming languages (i.e., Java and Python). Experiment results demonstrate that DGMS significantly outperforms state-of-the-art baseline models by a large margin on both datasets. Moreover, our extensive ablation studies systematically investigate and illustrate the impact of each part of DGMS.