WebRTC comprises a set of novel technologies and standards that provide Real-Time Communication on Web browsers. WebRTC makes simple the embedding of voice and video communications in all types of applications. However, releasing those applications to production is still very challenging due to the complexity of their testing. Validating a WebRTC service requires assessing many functional (e.g. signaling logic, media connectivity, etc.) and non-functional (e.g. quality of experience, interoperability, scalability, etc.) properties on large, complex, distributed and heterogeneous systems that spawn across client devices, networks and cloud infrastructures. In this article, we present a novel methodology and an associated tool for doing it at scale and in an automated way. Our strategy is based on a blackbox end-to-end approach through which we use an automated containerized cloud environment for instrumenting Web browser clients, which benchmark the SUT (system under test), and fake clients, that load it. Through these benchmarks, we obtain, in a reliable and statistically significant way, both network-dependent QoS (Quality of Service) metrics and media-dependent QoE (Quality of Experience) indicators. These are fed, at a second stage, to a number of testing assertions that validate the appropriateness of the functional and non-functional properties of the SUT under controlled and configurable load and fail conditions. To finish, we illustrate our experiences using such tool and methodology in the context of the Kurento open source software project and conclude that they are suitable for validating large and complex WebRTC systems at scale.