The blockchain ecosystem has evolved into a multi-chain world with various blockchains vying for use. Although each blockchain may have its own native cryptocurrency or digital assets, there are use cases to transfer these assets between blockchains. Systems that bring these digital assets across blockchains are called bridges, and have become important parts of the ecosystem. The designs of bridges vary and range from quite primitive to extremely complex. However, they typically consist of smart contracts holding and releasing digital assets, as well as nodes that help facilitate user interactions between chains. In this paper we first provide a high level break-down of components in a bridge and the different processes for some bridge designs. Then, we analyse past exploits in the blockchain ecosystem that specifically targeted bridges. In doing this, we identify risks associated with bridge components.

In industry, evaluating candidate architectures for automotive embedded systems is routinely done during the design process. Today's engineers, however, are limited in the number of candidates that they are able to evaluate in order to find the optimal architectures. This limitation results from the difficulty in defining the candidates as it is a mostly manual process. In this work, we propose a way to synthesize multilevel, multi-perspective candidate architectures and to explore them across the different layers and perspectives. Using a reference model similar to the EAST-ADL domain model but with a focus on early design, we explore the candidate architectures for two case studies: an automotive power window system and the central door locking system. Further, we provide a comprehensive set of questions, based on the different layers and perspectives, that engineers can ask to synthesize only the candidates relevant to their task at hand. Finally, using the modeling language Clafer, which is supported by automated backend reasoners, we show that it is possible to synthesize and explore optimal candidate architectures for two highly configurable automotive subsystems.

The decision-making process in Product Line Engineering (PLE) is often concerned with variant qualities such as cost, battery life, or security. Pareto-optimal variants, with respect to a set of objectives such as minimizing a variant's cost while maximizing battery life and security, are variants in which no single quality can be improved without sacrificing other qualities. We propose a novel method and a tool for visualization and exploration of a multi-dimensional space of optimal variants (i.e., a Pareto front). The visualization method is an integrated, interactive, and synchronized set of complementary views onto a Pareto front specifically designed to support PLE scenarios, including: understanding differences among variants and their positioning with respect to quality dimensions; solving trade-offs; selecting the most desirable variants; and understanding the impact of changes during product line evolution on a variant's qualities. We present an initial experimental evaluation showing that the visualization method is a good basis for supporting these PLE scenarios.