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Deep learning for code generation: a survey
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DOI：10.1007/s11432-023-3956-3.png)
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摘要
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In the past decade, thanks to the powerfulness of deep-learning techniques, we have witnessed a whole new era of automated code generation. To sort out developments, we have conducted a comprehensive review of solutions to deep learning-based code generation. In this survey, we generally formalize the pipeline and procedure of code generation and categorize existing solutions according to taxonomy from perspectives of architecture, model-agnostic enhancing strategy, metrics, and tasks. In addition, we outline the challenges faced by current dominant large models and list several plausible directions for future research. We hope that this survey may provide handy guidance to understanding, utilizing, and developing deep learning-based code-generation techniques for researchers and practitioners.
Keyword:
code generation
automated software engineering
deep learning
large model
artificial intelligence
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7.6
论文数:
4.9K
被引数:
8.9K
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