Abstract. Several industrial contexts require software engineering methods and tools able to handle large-size artifacts. The central idea of abstraction makes model-driven engineering (MDE) a promising approach in such contexts, but current tools do not scale to very large models (VLMs): already the task of storing and accessing VLMs from a persisting support is currently inefficient. In this paper we propose a scalable persistence layer for the de-facto standard MDE framework EMF. The layer exploits the efficiency of graph databases in storing and accessing graph structures, as EMF models are. A preliminary experimentation shows that typical queries in reverse-engineering EMF models have good performance on such persistence layer, compared to file-based backends.
The growing use of Model Driven Engineering (MDE) techniques in industry has emphasized scalability of existing model persistence solutions as a major issue. Specifically, there is a need to store, query, and transform very large models in an efficient way. Several persistence solutions based on relational and NoSQL databases have been proposed to achieve scalability. However, existing solutions often rely on a single data store, which suits a specific modeling activity, but may not be optimized for other use cases. In this article we present NEOEMF, a multi-database model persistence framework able to store very large models in key-value stores, graph databases, and wide column databases. We introduce NEOEMF core features, and present the different data stores and their applications. NEOEMF is open source and available online.
Abstract. The progressive industrial adoption of Model-Driven Engineering (MDE) is fostering the development of large tool ecosystems like the Eclipse Modeling project. These tools are built on top of a set of base technologies that have been primarily designed for small-scale scenarios, where models are manually developed. In particular, efficient runtime manipulation for large-scale models is an under-studied problem and this is hampering the application of MDE to several industrial scenarios. In this paper we introduce and evaluate a map-based persistence model for MDE tools. We use this model to build a transparent persistence layer for modeling tools, on top of a map-based database engine. The layer can be plugged into the Eclipse Modeling Framework, lowering execution times and memory consumption levels of other existing approaches. Empirical tests are performed based on a typical industrial scenario, modeldriven reverse engineering, where very large software models originate from the analysis of massive code bases. The layer is freely distributed and can be immediately used for enhancing the scalability of any existing Eclipse Modeling tool.
A major challenge for the European electronic industry is to enhance productivity by ensuring quality of development, integration and maintenance while reducing the associated costs. Model-Driven Engineering (MDE) principles and techniques have already shown promising capabilities, but they still need to scale up to support real-world scenarios implied by the full deployment and use of complex electronic components and systems. Moreover, maintaining efficient
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