We develop a machine-learned similarity metric for Windows failure reports using telemetry data gathered from clients describing the failures. The key feature is a tuned callstack edit distance with learned costs for seven fundamental edits based on callstack frames. We present results of a failure similarity classifier based on this and other features. We also describe how the model can be deployed to conduct a global search for similar failures across a failure database.

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In: SysML08: Third Workshop on Tackling Computer Systems Problems with Machine Learning Techniques

Publisher: USENIX
All copyrights reserved by USENIX 2007

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