摘要 : Software developers usually rely on in-house performance testing to detect performance regressions and locate their root causes. Such performance testing is typically resource and time-consuming, making it impractical to conduct w... 展开 Software developers usually rely on in-house performance testing to detect performance regressions and locate their root causes. Such performance testing is typically resource and time-consuming, making it impractical to conduct when the software is delivered in fast-paced release cycles. On the other hand, the operational data generated in the field environment provides rich information about the performance of a software system and its runtime activities. Therefore, this work explores the idea of leveraging the readily-available field operational data to locate the root causes of performance regression instead of running expensive performance tests. However, due to the ever-changing workloads from the end users and the noise from the field, directly analyzing performance metrics such as response time of the system may not be able to help locate the root causes of performance regressions. In this paper, we report our experience of designing and adopting an approach that automatically locates the root causes of performance regressions while the software systems are deployed and running in the field. First, our approach uses black-box performance models to capture the relationship between the performance of a system and its runtime activities. Then, our approach analyzes the performance models and uses statistical techniques to suggest the problematic system runtime activities (i.e., the root causes) that are related to a performance regression. Our evaluation considered three open-source projects and one industrial product. In the three open-source systems, we find that our approach can successfully locate the root causes of all arbitrarily injected synthetic performance regressions. Our approach has successfully detected and located the root causes of three performance regressions in an industry system and it has been adopted by our industrial partner and used in practice on a daily basis over a 12-month period. In addition, we share the challenges that we encountered during the design and adoption of our approach, how we address those challenges, and the lessons that we learned during the process. We believe that our novel approach together with our documented experience can benefit practitioners and researchers who wish to leverage the field-operation data of a software system to conduct performance assurance activities. 收起