English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Despite engineering workflows that aim to prevent buggy code from beingdeployed, bugs still make their way into the Facebook app. When symptoms ofthese bugs, such as user submitted reports and automatically captured crashes,are reported, finding their root causes is an important step in resolving them.However, at Facebook's scale of billions of users, a single bug can manifest asseveral different symptoms according to the various user and executionenvironments in which the software is deployed. Root cause analysis (RCA)therefore requires tedious manual investigation and domain expertise to extractout common patterns that are observed in groups of reports and use them fordebugging.  We propose Minesweeper, a technique for RCA that moves towards automaticallyidentifying the root cause of bugs from their symptoms. The method is based ontwo key aspects: (i) a scalable algorithm to efficiently mine patterns fromtelemetric information that is collected along with the reports, and (ii)statistical notions of precision and recall of patterns that help point towardsroot causes. We evaluate Minesweeper's scalability and effectiveness in findingroot causes from symptoms on real world bug and crash reports from Facebook'sapps. Our evaluation demonstrates that Minesweeper can perform RCA for tens ofthousands of reports in less than 3 minutes, and is more than 85% accurate inidentifying the root cause of regressions.

Scalable Statistical Root Cause Analysis on App Telemetry