会议简介

Big Data technology is playing an important role nowadays in many domains. It improves the data process and data analysis. Software Engineering is a traditional research field that explores the theories and techniques for software construction and application. The Big Data technology will also lead to important opportunities for the Software Engineering filed. By leveraging Big Data tech.-based intelligent methods, such as deep learning and reinforcement learning, Software Engineering researchers will not suffer from the troubles of massive data processing. They are able to explore more implicit and complex patterns of the software systems and services. With the new patterns, more novel and brilliant approaches can be developed that rapidly facilitate Software Engineering research in different focuses, such as software architecture, safety and reliability, security, and scalability. Therefore, the research of Big Data technology-based Software Engineering method is becoming a significant future investigation direction. The SEBD workshop is intended to provide researchers a forum to present the latest innovation and share experience in the cross-domain of Software Engineering and Big Data. 
 

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作者指南

Format and submission guideline 
Submit original papers (not published or submitted elsewhere) with a maximum of eight pages. Include the title of the paper, the name and affiliation of each author, a 150-word abstract, and up to 6 keywords. The format of your submission must follow the IEEE conference proceedings format.

征稿主题

The topics of interest include, but are not limited to: 
(1) Software architecture based on big data 
(2) Semantic software engineering based on big data 
(3) Software design method based on big data 
(4) Software testing based on big data 
(5) Software security based on big data 
(6) Software safety and reliability based on big data 
(7) Software visualization based on big data 
(8) Software engineering based on blockchain 
(9) Software applications based on big data 

组委

Workshop Organizers 
• Jian Wang, Institute of Automation, Chinese Academy of Sciences, China 
(1984123@qq.com) 
• Yi Yang, Institute of Automation, Chinese Academy of Sciences, China 
(yangyi@ia.ac.cn) 

Workshop program committee members 
• Jun Ai, Beihang University, China 
• Jianlin Cheng, University of Missouri, USA 
• Beibei Huang, University of Texas M. D. Anderson Cancer Center, USA 
• Mengxing Huang, Hainan University, China 
• Jie (Peter) Liu, Carleton University, Canada 
• Pan Liu, Shanghai Business School, China 
• Xiaoping Ouyang, Xiangtan University, China 
• Yan Wan, University of Texas at Arlington, USA 
• Yichen Wang, Beihang University, China 
• Ming Xin, University of Missouri, USA 
• Chunxiao Xing, Tsinghua University, China 
• Zhenning (Jimmy) Xu, University of Southern Maine, USA 
• En Zhu, Southeast University, China 
 

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  • 王建
  • jian.wang@ia.ac.cn