Deep learning-based software engineering:progress,challenges,and opportunities  

作　　者：Xiangping CHEN Xing HU Yuan HUANG He JIANG Weixing JI Yanjie JIANG Yanyan JIANG Bo LIU Hui LIU Xiaochen LI Xiaoli LIAN Guozhu MENG Xin PENG Hailong SUN Lin SHI Bo WANG Chong WANG Jiayi WANG Tiantian WANG Jifeng XUAN Xin XIA Yibiao YANG Yixin YANG Li ZHANG Yuming ZHOU Lu ZHANG 

机构地区：[1]Key Laboratory of High Confidence Software Technologies(Peking University),Ministry of Education,School of Computer Science,Peking University,Beijing 100871,China [2]School of Journalism and Communication,Sun Yat-sen University,Guangzhou 510275,China [3]School of Software Technology,Zhejiang University,Hangzhou 310058,China [4]School of Software Engineering,Sun Yat-sen University,Guangzhou 510275,China [5]School of Software,Dalian University of Technology,Dalian 116024,China [6]School of Computer Science and Technology,Beijing Institute of Technology,Beijing 100081,China [7]State Key Laboratory for Novel Software Technology,Nanjing University,Nanjing 210023,China [8]School of Computer Science and Engineering,Beihang University,Beijing 100191,China [9]Institute of Information Engineering,Chinese Academy of Sciences,Beijing 100864,China [10]School of Computer Science,Fudan University,Shanghai 200433,China [11]State Key Laboratory of Complex&Critical Software Environment(CCSE),School of Software,Beihang University,Beijing 100191,China [12]School of Computer and Information Technology,Beijing Jiaotong University,Beijing 100044,China [13]School of Computer Science and Technology,Harbin Institute of Technology,Harbin 150001,China [14]School of Computer Science,Wuhan University,Wuhan 430072,China [15]Huawei Technologies,Hangzhou 310056,China

出　　处：《Science China(Information Sciences)》2025年第1期54-141,共88页中国科学(信息科学)(英文版)

摘　　要：Researchers have recently achieved significant advances in deep learning techniques,which in turn has substantially advanced other research disciplines,such as natural language processing,image processing,speech recognition,and software engineering.Various deep learning techniques have been successfully employed to facilitate software engineering tasks,including code generation,software refactoring,and fault localization.Many studies have also been presented in top conferences and journals,demonstrating the applications of deep learning techniques in resolving various software engineering tasks.However,although several surveys have provided overall pictures of the application of deep learning techniques in software engineering,they focus more on learning techniques,that is,what kind of deep learning techniques are employed and how deep models are trained or fine-tuned for software engineering tasks.We still lack surveys explaining the advances of subareas in software engineering driven by deep learning techniques,as well as challenges and opportunities in each subarea.To this end,in this study,we present the first task-oriented survey on deep learning-based software engineering.It covers twelve major software engineering subareas significantly impacted by deep learning techniques.Such subareas spread out through the whole lifecycle of software development and maintenance,including requirements engineering,software development,testing,maintenance,and developer collaboration.As we believe that deep learning may provide an opportunity to revolutionize the whole discipline of software engineering,providing one survey covering as many subareas as possible in software engineering can help future research push forward the frontier of deep learning-based software engineering more systematically.For each of the selected subareas,we highlight the major advances achieved by applying deep learning techniques with pointers to the available datasets in such a subarea.We also discuss the challenges and opportunities concerning

关 键 词：deep learning software engineering software benchmark software artifact representation survey 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TP311.5[自动化与计算机技术—控制科学与工程]