Inbound traffic engineering (ITE)---the process of announcing routes to, e.g., maximize revenue or minimize congestion---is an essential task for Autonomous Systems (ASes). AS Path Prepending (ASPP) is an easy to use and well-known ITE technique that routing manuals show as one of the first alternatives to influence other ASes' routing decisions. We observe that origin ASes currently prepend more than 25% of all IPv4 prefixes.

Online model-based testing is one of the most suitable techniques to assess the proper behavior of service orchestrations. However, the diverse panorama in terms of modeling languages and test case generation tools is a limitation to widespread adoption. We advocate that the application of Model-Driven Engineering principles as meta-modeling and model transformation can cope with this problem, improving the interoperability of artifacts in the test case generation process, thus bringing benefits in case of agile development processes, where system and technology evolution is frequent. In this paper, we present our contribution to this idea, introducing i) a reference metamodel, which stores the business process behavior and the information to generate input models for testing tools, and ii) transformations from orchestration languages towards testing tools. The proposed approach is implemented in a testing framework and evaluated on a case study where multiple orchestrations are expressed in two languages. Also, the paper presents how test cases are appropriately generated and successfully executed, starting from an orchestration model as a consequence of successful transformations.

Service Oriented Architecture (SOA) is a popular design pattern that allows building applications composed of loosely-coupled and autonomous services. Such services may evolve and change at runtime, often outside the control of the owner of the application. Consequently, typical validation approaches, like offline testing performed before services deployment, are necessary but not sufficient: offline testing cannot assure the correct behavior of the SOA during its execution. To cope with the evolution of services and their orchestrations, in this paper we present a Self-Adaptive Model-BAsed online testing framework called SAMBA. SAMBA aims to assess the proper behavior of a SOA during its lifecycle executing model-based online testing at runtime, under the coordination of a MAPE-K control loop. SAMBA is assessed in a case study, where its detection capability are proved through functional, mutation and fault injection tests.