Deep learning for code generation:a survey

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外文摘要：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.

外文关键词：

code generationautomated software engineeringdeep learninglarge modelartificial intelli-gence

作者：

Huangzhao ZHANG、Kechi ZHANG、Zhuo LI、Jia LI、Yongmin LI、Yunfei ZHAO、Yuqi ZHU、Fang LIU、Ge LI、Zhi JIN

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作者单位：

Key Lab of High Confidence Software Technologies(Peking University),Ministry of Education,Beijing 100871,China

School of Computer Science,Peking University,Beijing 100871,China

School of Computer Science and Engineering,Beihang University,Beijing 100191,China

基金：

National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of China

项目编号：

62192733621927316175121062072007618320096219273062302021

出版年：

2024

DOI：

10.1007/s11432-023-3956-3

中国科学:信息科学(英文版)

中国科学院

中国科学:信息科学(英文版)

CSTPCDEI

影响因子：0.715

ISSN：1674-733X

年,卷(期)：2024.67(9)