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Meta Minesweeper: Scalable Statistical Root Cause Analysis on App Telemetry
Manage episode 487366638 series 3670304
The Binary Breakdown에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 The Binary Breakdown 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.
This research paper introduces Minesweeper, a novel technique for automated root cause analysis (RCA) of software bugs at scale. Leveraging telemetry data, Minesweeper efficiently identifies statistically significant patterns in user app traces that correlate with bugs, even in the absence of detailed debugging information. The method uses sequential pattern mining, specifically the PrefixSpan algorithm, for pattern extraction and incorporates statistical measures of precision and recall to rank patterns by distinctiveness. Practical challenges like handling numeric data and mitigating redundant patterns are addressed, and the system's scalability and accuracy are demonstrated through real-world evaluations on Facebook's app data. The results show Minesweeper significantly improves the speed and accuracy of RCA, aiding engineers in quickly identifying and resolving bugs.
https://arxiv.org/pdf/2010.09974
44 에피소드
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