Using black-box performance models to detect performance regressions under varying workloads: an empirical study

Performance regressions of large-scale software systems often lead to both financial and reputational losses. In order to detect performance regressions, performance tests are typically conducted in an in-house (non-production) environment using test suites with predefined workloads. Then, performance analysis is performed to check whether a software version has a performance regression against an earlier version. However, the real workloads in the field are constantly changing, making it unrealistic to resemble the field workloads in predefined test suites. More importantly, performance testing is usually very expensive as it requires extensive resources and lasts for an extended period. In this work, we leverage black-box machine learning models to automatically detect performance regressions in the field operations of large-scale software systems. Practitioners can leverage our approaches to complement or replace resource-demanding performance tests that may not even be realistic in a fast-paced environment. Our approaches use black-box models to capture the relationship between the performance of a software system (e.g., CPU usage) under varying workloads and the runtime activities that are recorded in the readily-available logs. Then, our approaches compare the black-box models derived from the current software version with an earlier version to detect performance regressions between these two versions. We performed empirical experiments on two open-source systems and applied our approaches on a large-scale industrial system. Our results show that such black-box models can effectively and timely detect real performance regressions and injected ones under varying workloads that are unseen when training these models. Our approaches have been adopted in practice to detect performance regressions of a large-scale industry system on a daily basis.