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 re-gressions). 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 auto-mate such analysis, model-based performance regression de-tection approaches build a limited number (i.e., one or two) of models for a limited number of target performance coun-ters (e.g., CPU or memory) and leverage the models to de-tect performance regressions. Such model-based approaches still have their limitations since selecting the target perfor-mance counters is often based on experience or gut feeling