Cloud applications and services have significantly increased the importance of system and service configuration activities. These activities include updating(i) these services, (ii) their dependencies on third parties,(iii) their configurations, (iv) the configuration of the execution environment, (v) network configurations. The high frequency of updates results in significant configuration complexity that can lead to failures or performance drops.To mitigate these risks, service providers extensively rely on testing techniques, such as metamorphic testing, to detect these failures before moving to production. How-ever, the development and maintenance of these tests are costly, especially the oracle, which must determine whether a system’s performance remains within acceptable boundaries. This paper explores the use of a learning method called Principal Component Analysis (PCA) to learn about acceptable performance metrics on cloud-native services and identify a metamorphic relationship between the nominal service behavior and the value of these metrics. We investigate the following research question: Is it possible to combine the metamorphic testing technique with learning methods on service monitoring data to detect error-prone reconfigurations before moving to production? We remove the developers’ burden to define a specific oracle in detecting these configuration issues. For validation, we applied this proposal on a distributed media streaming application whose authentication was managed by an external identity and access management services.This application illustrates both the heterogeneity of the technologies used to build this type of service and its large configuration space. Our proposal demonstrated the ability to identify error-prone reconfigurations using PC