Observability-Driven QA for Serverless and PaaS Architectures: A Trace-Informed, SLO-Oriented Benchmarking Framework

Serverless and Platform-as-a-Service (PaaS) architectures have fundamentally reshaped cloud-native application development by abstracting infrastructure management, automating resource provisioning, and enabling rapid, elastic scaling with cost-efficient consumption models. While these capabilities accelerate deployment and reduce operational complexity, they simultaneously introduce significant challenges for Quality Assurance (QA), particularly in areas such as performance validation, distributed traceability, observability, and the reproducibility of test results. Unlike traditional systems with stable, controllable execution environments, serverless and PaaS applications operate across highly dynamic, provider-managed infrastructures where ephemeral runtimes, multi-tenant resource sharing, cold starts, and asynchronous event flows make behavior inherently nondeterministic and difficult to analyze. These characteristics complicate the design of meaningful performance tests, obscure root-cause analysis, and demand new approaches for capturing end-to-end visibility through fine-grained logs, metrics, and traces. To address these gaps, this article examines modern QA methodologies, tools, and research contributions that focus on systematic benchmarking, distributed tracing, and automated quality validation tailored to cloud-native architectures. Drawing upon publicly available figures, benchmarking frameworks, and representative academic and industry studies published between 2000 and 2022, the paper synthesizes these insights into a unified QA framework designed to support the reliability, scalability, and traceable operation of event-driven serverless and PaaS applications.