Abstract: Software systems usually record important runtime information in their logs. Logs help practitioners understand system runtime behaviors and diagnose field failures. As logs are usually very large in size, automated log analysis is needed to assist practitioners in their software operation and maintenance efforts. The success of adopting log analysis in practice often depends on sophisticated preprocessing. In particular, to enable systematic analysis of logs, logs are often parsed that converts the raw logs from unstructured text to a structured format and groups based particular IDs of time slots. In this talk, I provide an overview of some of our recent work on automated techniques of log pre-processing especially considering the adoption of log analysis in practice.
Weiyi Shang is an Associate Professor at the University of Waterloo. His research interests include AIOps, big bata software engineering, software log analytics and software performance engineering. He serves as a Steering committee member of the SPEC Research Group. He is ranked top worldwide SE research stars in a recent bibliometrics assessment of software engineering scholars. He is a recipient of various premium awards, including the SIGSOFT Distinguished paper award at ICSE 2013 and ICSE 2020, best paper award at WCRE 2011 and the Distinguished reviewer award for the Empirical software Engineering journal. His research has been adopted by industrial collaborators (e.g., BlackBerry and Ericsson) to improve the quality and performance of their software systems that are used by millions of users worldwide. Contact him at wshang@uwaterloo.ca; uwaterloo.ca/electrical-computer-engineering/profile/wshang.
This panel explores how AIOps is evolving from simple automation and monitoring to enabling predictive, proactive, and ultimately autonomous IT operations. Experts from AI, DevOps, cloud service, and enterprise IT will discuss the transformative impact of AIOps on modern IT environments, the challenges in adoption and what the next 5–10 years hold for the intersection of AI and Ops.