# Survey of Code Search Based on Deep Learning

arXiv: https://arxiv.org/abs/2305.05959 · HTML: https://mlsanity.com/paper/2305.05959/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 May 2023.

## Built on by (most cited first, 6 of 6)

- [Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Code](https://mlsanity.com/paper/2311.07989/) (2023)
- [Alibaba LingmaAgent: Improving Automated Issue Resolution via Comprehensive Repository Exploration](https://mlsanity.com/paper/2406.01422/) (2024)
- [On the Compression of Language Models for Code: An Empirical Study on CodeBERT](https://mlsanity.com/paper/2412.13737/) (2024)
- [Refining Joint Text and Source Code Embeddings for Retrieval Task with Parameter-Efficient Fine-Tuning](https://mlsanity.com/paper/2405.04126/) (2024)
- [LoRACode: LoRA Adapters for Code Embeddings](https://mlsanity.com/paper/2503.05315/) (2025)
- [One Model, Many Skills: Parameter-Efficient Fine-Tuning for Multitask Code Analysis](https://mlsanity.com/paper/2603.09978/) (2026)

## Abstract (arXiv)

Code writing is repetitive and predictable, inspiring us to develop various code intelligence techniques. This survey focuses on code search, that is, to retrieve code that matches a given query by effectively capturing the semantic similarity between the query and code. Deep learning, being able to extract complex semantics information, has achieved great success in this field. Recently, various deep learning methods, such as graph neural networks and pretraining models, have been applied to code search with significant progress. Deep learning is now the leading paradigm for code search. In this survey, we provide a comprehensive overview of deep learning-based code search. We review the existing deep learning-based code search framework which maps query/code to vectors and measures their similarity. Furthermore, we propose a new taxonomy to illustrate the state-of-the-art deep learning-based code search in a three-steps process: query semantics modeling, code semantics modeling, and matching modeling which involves the deep learning model training. Finally, we suggest potential avenues for future research in this promising field.