Deep learning for code generation:a survey  

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

机构地区：[1]Key Lab of High Confidence Software Technologies(Peking University),Ministry of Education,Beijing 100871,China [2]School of Computer Science,Peking University,Beijing 100871,China [3]School of Computer Science and Engineering,Beihang University,Beijing 100191,China

出　　处：《Science China(Information Sciences)》2024年第9期1-36,共36页中国科学（信息科学）（英文版）

基　　金：supported by National Natural Science Foundation of China(Grant Nos.62192733,62192731,61751210,62072007,61832009,62192730);supported by National Natural Science Foundation of China(Grant No.62302021)。

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.

关 键 词：code generation automated software engineering deep learning large model artificial intelligence 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]