Title: Modeling Systems from Logs of their Behavior

Abstract:
Logged messages are invaluable for debugging and diagnosing problems.
Unfortunately, many execution logs are inscrutable in their raw form.
For example, a production Google system may generate a billion-line
log file in a single day. In my talk, I will detail two log-analysis
tools that I developed to deal with this problem -- Synoptic and InvariMint.
Both tools infer concise and precise models from large execution logs
of sequential systems.  Both tools enable new kinds of program analyses
and make logs more useful to developers. For example, my empirical
experiments show that developers find the inferred models useful for
identifying bugs, confirming bugs that were previously known, and
increasing their confidence in their implementations.

Bio:
Ivan is an assistant professor at the University of British Columbia.
His current research interests lie at the intersection of systems and
software engineering.