Volume 29 Issue 6
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ZHANG Xiaojiang, JIANG Ying. Research and Application of Machine Learning in Automatic Program Generation[J]. Chinese Journal of Electronics, 2020, 29(6): 1001-1015. doi: 10.1049/cje.2020.10.006
Citation: ZHANG Xiaojiang, JIANG Ying. Research and Application of Machine Learning in Automatic Program Generation[J]. Chinese Journal of Electronics, 2020, 29(6): 1001-1015. doi: 10.1049/cje.2020.10.006

Research and Application of Machine Learning in Automatic Program Generation

doi: 10.1049/cje.2020.10.006
Funds:  This work is supported by the National Key Research and Development Program of China (No.2018YFB1003904), the National Natural Science Foundation of China under Grant (No.61462049, No.60703116 and No.61063006), and Key Project of Yunnan Applied Basic Research (No.2017FA033), and the Scientific Research Fund Project of the Yunnan Education (No.2020Y0087).
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  • Corresponding author: JIANG Ying (corresponding author) was born in 1974, Yunnan, China. She received her Ph.D. degree in computer software and theory from Peking University, China, in 2005. She is currently a professor of Kunming University of Science and Technology, Supervisor of Ph.D. Candidate. Her research interests include software quality assurance and testing, cloud computing, big data analysis and intelligent software engineering. (Email:jy_910@163.com)
  • Received Date: 2019-10-31
  • Publish Date: 2020-12-25
  • With the development of artificial intelligence, machine learning has been applied in more and more domains. In order to improve the quality and efficiency of software, automatic program generation is becoming a research hotspot. In recent years, machine learning has also been gradually applied in automatic program generation. Decision trees, language models, and cyclic neural networks have been applied in code generation, code completion and code knowledge mining. The efficiency of software development has been improved to a certain extent using machine learning. Aimed at the automatic program generation, this paper analyzes and summarizes the models of machine learning, the modifications involved in the models and the application effects. The research direction is discussed from the aspects of programmer behavior and automatic program generation of machine learning.
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