Survey of Code Search Based on Deep Learning

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...

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Bibliographic Details
Published in:arXiv.org 2023-12
Main Authors: Xie, Yutao, Lin, Jiayi, Dong, Hande, Zhang, Lei, Wu, Zhonghai
Format: Article
Language:English
Subjects:
Deep learning
Graph neural networks
Modelling
Queries
Searching
Semantics
Similarity
Taxonomy
Online Access:Get full text
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Summary: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.
ISSN:2331-8422
DOI:10.48550/arxiv.2305.05959