Software is increasingly complex. Many traditional software systems have also migrated to cloud computing platforms and are provided as cloud services. However, the current software development process is still largely a manual, time-consuming, and error-prone process. In the era of big data and artificial intelligence, we aim towards intelligent software and service engineering. During the development and maintenance of software and services, a vast amount of data is generated. These data include source code, operation logs, historical failures, performance counters, etc. Various machine learning and data analytics techniques can be utilized to mine these data to automate programming, testing, fault diagnosis, and maintenance tasks. As a result, software/service quality and development productivity could be improved. In this talk, I will briefly introduce some of my recent work on intelligent software and service engineering.
Hongyu Zhang is currently an Associate Professor at The University of Newcastle, Australia. Previously, he was a Lead Researcher at Microsoft Research Asia and an Associate Professor at Tsinghua University, Beijing, China. He received the PhD degree from National University of Singapore in 2003. His research is in the area of Software Engineering, in particular, software analytics, testing, maintenance, reuse, and online services. The main theme of his research is to improve software quality and productivity by mining software and service data. He has published more than 120 research papers in reputable international journals and conferences, including TSE, TOSEM, ICSE, FSE, POPL, AAAI, IJCAI, KDD, ASE, ISSTA, ICSME, ICDM, and USENIX ATC. With an H-index of 33, his papers have been cited over 4100 times (Google Scholar). He received two ACM Distinguished Paper awards. He has also served as a program committee member for many software engineering conferences, including PC co-chair of APSEC 2018 and General co-chair of ICSME 2020. He is on the editorial board of Journal of Systems and Software and Elsevier Array. More information about him can be found at: https://sites.google.com/site/hongyujohn/.