  Modern mobile applications include instrumentation that sample internal
application metrics at regular intervals. Following a crash, sample metrics are
collected and can potentially be valuable for root-causing difficult to
diagnose crashes. However, the fine-grained nature and overwhelming wealth of
available application metrics, coupled with frequent application updates,
renders their use for root-causing crashes extremely difficult.
  We propose KabOOM, a method to automatically cluster telemetry reports in
intuitive, distinct crash categories. Uniquely, KabOOM relies on multivariate
timeseries fingerprinting; an auto-encoder coupled with a cluster centroid
optimization technique learns embeddings of each crash report, which are then
used to cluster metric timeseries based crash reports. We demonstrate the
effectiveness of KabOOM on both reducing the dimensionality of the incoming
crash reports and producing crash categories that are intuitive to developers.
