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Location: Room 3022, West Hall
Date: Wednesday, March 20
Time: 10:30 am - 11:30 am
Pass Type: All Access, GDC Conference + Summits, GDC Conference, GDC Summits, Expo Plus, Audio Conference + Tutorial, Indie Games Summit - Get your pass now!
Format: Sponsored Session
Vault Recording: Not Recorded
Audience Level: Intermediate
Game crashes and hangs are frustrating, and should be treated differently than other bugs because they are easy to fix relative to their severity. This talk dives into the challenges of building a scalable post-mortem information capture and aggregation platform that allows developer teams to quickly identify and fix important stability problems. The talk will dive into how Roblox and Backtrace collect error data on the order of terabytes a month, scale the automated analysis of memory dumps and discusses the latest developments in bringing automation to forensic debugging.
Learn from Roblox as to how they manage software stability across 70M+ monthly active users and from Backtrace to the challenges of memory dump analysis at scale. Topics covered include systems performance, compiler debug information formats, feature flags, fuzzy matching, static analysis and more.
Attendees of the talk will walk away with an improved understanding of the challenges and possible solutions to capturing error data from their users and subsequently, actioning on that data to improve stability. For members of the team that are responsible for triage, learn how Roblox and Backtrace brought automation to that process. For members of the team that are responsible for root cause investigation, learn new techniques for extracting signal out of a memory dump and how to apply static analysis to post-mortem debugging.
This talk is intended for gameplay programmers, systems programmers, quality assurance engineers, infrastructure engineers and engineering leads interested in learning tried and true approaches to capturing and actioning on error data at scale, as well as learn new techniques for forensic debugging in the large.