Software-intensive systems provide various services to millions of users, thus requiring high availability and reliability. These systems often produce a large volume of logs to record runtime status and events. Log Intelligence, which utilizes AI/ML technologies to analyze systems logs for more effective system operation and maintenance, has drawn much attention from both researchers and practitioners in recent decades. In this talk, I will first introduce the research landscape of fault detection and Log Intelligence. I will then present a suite of Log Intelligence-based fault detection models. I will also discuss the challenges and opportunities in future research on Log Intelligence.

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, China. He received his PhD degree from National University of Singapore in 2003. His research is in the area of Software Engineering, in particular, software analytics, testing, maintenance, and reuse. The main theme of his research is to improve software quality and productivity by mining software data. He has published more than 160 research papers in international journals and conferences, including TSE, TOSEM, ICSE, FSE, POPL, AAAI, IJCAI, KDD, ASE, ISSTA, ICSME, ICDM, and USENIX. He received three ACM Distinguished Paper awards. He has also served as a program committee member for many software engineering conferences. More information about him can be found at: https://sites.google.com/site/hongyujohn/ .