KabOOM: Unsupervised Crash Categorization through Timeseries Fingerprinting
This addresses the challenge of diagnosing crashes in mobile applications for developers, but it is incremental as it builds on existing telemetry and clustering techniques.
The paper tackles the problem of clustering telemetry reports from mobile app crashes to aid root-cause analysis, proposing KabOOM, which uses multivariate timeseries fingerprinting to automatically categorize crashes and demonstrates effectiveness in reducing dimensionality and producing intuitive categories for developers.
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.