System performance in various computing systems is measured using various benchmarks. A benchmark allows users to observe a set of performance metrics of the system as a function of time and workload, and to determine if a performance metric has deviated or regressed. However, different regression analyzers are suitable for different metrics and finding accurate analyzers often requires substantial manual effort that needs to be repeated whenever a variable that impacts a performance metric changes. This disclosure describes techniques that obtain historical data (sourced from stable workloads) about the pattern of a performance metric and use the data to train a machine learning algorithm to analyze a performance metric and determine a suitable analyzer. The analyzer configuration is selected based upon classification of the metric as noisy or not noisy, and on what is suitable for the particular metric.
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Sharma, Shubham and Puri, Varun, "Automatically Identifying Regression Detection Conditions for System Performance Metrics", Technical Disclosure Commons, (January 17, 2023)