Deep learning has significantly advanced the field of code intelligence, enabling the development of tools that enhance programming productivity and quality. This survey explores the landscape of deep learning techniques applied to code intelligence, focusing on key areas such as code representation, model architectures, application tasks, and challenges.

Key Areas of Focus

1. Code Representation Learning: Code representation is foundational for code intelligence. Techniques like abstract syntax trees (ASTs), control/data flow graphs, and token-based embeddings are widely used to capture the structural and semantic properties of code. Models such as CodeBERT and GraphCodeBERT leverage these representations to improve understanding and generation tasks.

2. Deep Learning Techniques: Architectures like transformers, graph neural networks (GNNs), and sequence-to-sequence models dominate this domain. For instance, models like CodeT5 and UniXcoder utilize encoder-decoder frameworks for tasks like code summarization and generation.

3. Application Tasks: Deep learning models are applied to a variety of tasks, including: Code Generation: Automating code writing from natural language descriptions. Code Summarization: Generating concise descriptions of code functionality. Bug Detection and Repair: Identifying and fixing vulnerabilities or errors in code. Code Translation: Converting code between programming languages^2^^3^.

4. Benchmarks and Toolkits: Benchmarks like CodeXGLUE and HumanEval provide standardized datasets for evaluating code intelligence models. Open-source toolkits facilitate rapid prototyping and experimentation with deep learning models for code.