Automated Detection of Performance Regressions Using Regression Models on Clustered Performance Counters

Performance testing is conducted before deploying system updates in order to ensure that the performance of large software systems did not degrade (i.e., no performance regressions). During such testing, thousands of performance counters are collected. However, comparing thousands of performance counters across versions of a software system is very time consuming and error-prone. In an effort to automate such analysis, model-based performance regression detection approaches build a limited number (i.e., one or two) of models for a limited number of target performance counters (e.g., CPU or memory) and leverage the models to detect performance regressions. Such model-based approaches still have their limitations since selecting the target performance counters is often based on experience or gut feeling. In this paper, we propose an automated approach to detect performance regressions by analyzing all collected counters instead of focusing on a limited number of target counters. We first group performance counters into clusters to determine the number of performance counters needed to truly represent the performance of a system. We then perform statistical tests to select the target performance counters, for which we build regression models. We apply the regression models on new version of the system to detect performance regressions.