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.
The list of topics includes, but is not limited to:
Authors are invited to submit original unpublished research papers as well as industrial practice papers. Simultaneous submissions to other conferences are not permitted. Detailed instructions for electronic paper submission, panel proposals, and review process can be found at QRS submission.
Each submission can have a maximum of ten pages. It should include a title, the name and affiliation of each author, a 300-word abstract, and up to 6 keywords. Shorter version papers (up to six pages) are also allowed.
All papers must conform to the QRS conference proceedings format (PDF | Word DOCX | Latex) and Submission Guideline set in advance by QRS 2023. At least one of the authors of each accepted paper is required to pay the full registration fee and present the paper at the workshop. Submissions must be in PDF format and uploaded to the conference submission site. Arrangements are being made to publish extended version of top-quality papers in selected SCI journals.
Submission| Name | Affiliation |
|---|---|
| Jun Ai | Beihang University |
| Jianlin Cheng | University of Missouri |
| Beibei Huang | University of Texas M. D. Anderson Cancer Center |
| Mengxing Huang | Hainan University |
| Jie (Peter) Liu | Carleton University |
| Pan Liu | Shanghai Business School |
| Xiaoping Ouyang | Xiangtan University |
| Yan Wan | University of Texas at Arlington |
| Yichen Wang | Beihang University |
| Ming Xin | University of Missouri |
| Chunxiao Xing | Tsinghua University |
| Zhenning (Jimmy) Xu | California State University, Bakersfield |
| En Zhu | Southeast University |
