When engineering complex systems, models are used to represent various systems aspects. These models are often heterogeneous in terms of modeling language, provenance, number or scale. They can be notably managed by different persistence frameworks adapted to their nature. As a result, the information relevant to engineers is usually split into several interrelated models. To be useful in practice, these models need to be integrated together to provide global views over the system under study. Model view approaches have been proposed to tackle such an issue. They provide an unification mechanism to combine and query heterogeneous models in a transparent way. These views usually target specific engineering tasks such as system design, monitoring, evolution, etc. In our present context, the MegaM@Rt2 industrially-supported European initiative defines a set of large-scale use cases where model views can be beneficial for tracing runtime and design time data. However, existing model view solutions mostly rely on in-memory constructs and low-level modeling APIs that have not been designed to scale in the context of large models stored in different kinds of sources. This paper presents the current status of our work towards a general solution to efficiently support scalable model views on heterogeneous model resources. It describes our integration approach between model view and model persistence frameworks. This notably implies the refinement of the view framework for the construction of large views from multiple model storage solutions. This also requires to study how parts of queries can be computed on the contributing models rather than on the view. Our solution has been benchmarked on a practical large-scale use case from the MegaM@Rt2 project, implementing a runtime -design time feedback loop. The corresponding EMF-based tooling support and modeling resources are fully available online.• Software and its engineering → Model-driven software engineering; Abstraction, modeling and modularity; • Information systems → Database design and models;

When engineering complex systems, models are typically used to represent various systems aspects. These models are often heterogeneous in terms of modeling languages, provenance, number or scale. As a result, the information actually relevant to engineers is usually split into different kinds of interrelated models. To be useful in practice, these models need to be properly integrated to provide global views over the system. This has to be made possible even when those models are serialized or stored in different formats adapted to their respective nature and scalability needs. Model view approaches have been proposed to tackle this issue. They provide unification mechanisms to combine and query various different models in a transparent way. These views usually target specific engineering tasks such as system design, monitoring, evolution, etc. In an industrial context, there can be many large-scale use cases where model views can be beneficial, in order to trace runtime and design-time data for example. However, existing model view solutions are generally designed to work on top of one single modeling technology (even though

Abstract. In this position paper we argue that aspects are wellsuited to describe and implement a range of strategies to make secure JavaScript-based applications. To this end, we review major categories of approaches to make client-side applications secure and discuss uses of aspects that exist for some of them. We also propose aspect-based techniques for the categories that have not yet been studied. We give examples of applications where aspects are useful as a general means to flexibly express and implement security policies for JavaScript.

Automotive, aerospace, industrial control, and railway systems are examples of application domains which are particularly characterized by the need for developing and managing critical systems. Model-driven engineering is recognized as an effective solution to leverage abstraction and automation while developing complex systems. One of the major and key challenges in the model-driven engineering of critical software systems is the integration of design and runtime aspects. Even though several methods and tools are available for performing measurements of runtime properties, the ability to trace them with design models is still limited. In the context of a real railway system, this paper presents a model-based approach that has been conceived to analyze runtime data (coming from different sensors), to produce corresponding traceability models and to automatically infer from them potential design issues that might need to be fixed in order to solve detected system malfunctionings.