Sadaf Mustafiz

Sadaf Mustafiz, Marko Mijalkovic, Moharram Challenger

In this paper, we propose an approach that integrates end-to-end traceability with process modelling. OurprocessmodelsrepresentMDEworkflowsthatspan platform-independent-modelling, platform-specificmodelling, andcodegenerationphases. Processexecutionisautomated using megamodels and model transformation chains. The generation of end-to-end traceability information enables global model traceability, from high-level input models to generated code, forming the basis for traceability analysis. We have built an Eclipse-based framework, ProMoTA, to support our approach. ProMoTA extends the Acceleo model transformation language, introducing local traceability support. It also includes a global traceability map generator and end-to-end traceability analysis modules, providing users with a holistic view of the entire transformation process. Our framework is demonstrated with the use of a Wireless Sensor Network-Based IoT application.

Natalie Sinani, Sahil Salma, Paul Boutot et al.

In recent years, machine learning technologies have gained immense popularity and are being used in a wide range of domains. However, due to the complexity associated with machine learning algorithms, it is a challenge to make it user-friendly, easy to understand and apply. Machine learning applications are especially challenging for users who do not have proficiency in this area. In this paper, we use model-driven engineering (MDE) methods and tools for developing a domain-specific modelling environment to contribute towards providing a solution for this problem. We targeted reinforcement learning from the machine learning domain, and evaluated the proposed language, reinforcement learning modelling language (RLML), with multiple applications. The tool supports syntax-directed editing, constraint checking, and automatic generation of code from RLML models. The environment also provides support for comparing results generated with multiple RL algorithms. With our proposed MDE approach, we were able to help in abstracting reinforcement learning technologies and improve the learning curve for RL users.

Sadaf Mustafiz, Omar Hassane, Guillaume Dupont et al.

The Network Functions Virtualization (NFV) advent is making way for the rapid deployment of network services (NS) for telecoms. Automation of network service management is one of the main challenges currently faced by the NFV community. Explicitly defining a process for the design, deployment, and management of network services and automating it is therefore highly desirable and beneficial for NFV systems. The use of model-driven orchestration means has been advocated in this context. As part of this effort to support automated process execution, we propose a process enactment approach with NFV systems as the target application domain. Our process enactment approach is megamodel-based. An integrated process modelling and enactment environment, MAPLE, has been built into Papyrus for this purpose. Process modelling is carried out with UML activity diagrams. The enactment environment transforms the process model to a model transformation chain, and then orchestrates it with the use of megamodels. In this paper we present our approach and environment MAPLE, its recent extension with new features as well as application to an enriched case study consisting of NS design and onboarding process.