Abel Gómez

Neo4EMF, A Scalable Persistence Layer for EMF Models

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.

Map-Based Transparent Persistence for Very Large Models

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, model-driven 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.

Distributed model-to-model transformation with ATL on MapReduce

Efficient processing of very large models is a key requirement for the adoption of Model-Driven Engineering (MDE) in some industrial contexts. One of the central operations in MDE is rule-based model transformation (MT). It is used to specify manipulation operations over structured data coming in the form of model graphs. However, being based on computationally expensive operations like subgraph isomorphism, MT tools are facing issues on both memory occupancy and execution time while dealing with the increasing model size and complexity. One way to overcome these issues is to exploit the wide availability of distributed clusters in the Cloud for the distributed execution of MT. In this paper, we propose an approach to automatically distribute the execution of model transformations written in a popular MT language, ATL, on top of a well-known distributed programming model, MapReduce. We show how the execution semantics of ATL can be aligned with the MapReduce computation model. We describe the extensions to the ATL transformation engine to enable distribution, and we experimentally demonstrate the scalability of this solution in a reverse-engineering scenario.

EMF-REST: generation of RESTful APIs from models

In the last years, there has been an increasing interest for Model-Driven Engineering (MDE) solutions in the Web. Web-based modeling solutions can leverage on better support for distributed management (i.e., the Cloud) and collaboration. However, current modeling environments and frameworks are usually restricted to desktop-based scenarios and therefore their capabilities to move to the Web are still very limited. In this paper we present an approach to generate Web APIs out of models, thus paving the way for managing models and collaborating on them online. The approach, called EMF-REST, takes Eclipse Modeling Framework (EMF) data models as input and generates Web APIs following the REST principles and relying on well-known libraries and standards, thus facilitating its comprehension and maintainability. Also, EMF-REST integrates model and Web-specific features to provide model validation and security capabilities, respectively, to the generated API.

NeoEMF: A Multi-database Model Persistence Framework for Very Large Models

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.

Towards a UML profile for data intensive applications

Data intensive applications that leverage Big Data technologies are rapidly gaining market trend. However, their design and quality assurance are far from satisfying software engineers needs. In fact, a CapGemini research shows that only 13% of organizations have achieved full-scale production for their Big Data implementations. We aim at addressing an early design and a quality evaluation of data intensive applications,being our goal to help software engineers on assessing quality metrics, such as the response time of theapplication. We address this goal by means of a quality analysis tool-chain.At the core of the tool, we are developing a Profile that converts the Unified Modeling Language into a domain specific modeling language for quality evaluation of data intensive applications.

Enforcing reuse and customization in the development of learning objects: a product line approach

The growing use of information technologies in the educational cycles has raised new requirements for the development of Interactive Learning Materials in terms of content reuse, customization, and ease of creation and efficiency of production. In practical terms, the goal is the development of tools for creating reusable, granular, durable, and interoperable learning objects, and to compose such objects into meaningful courseware pieces. Current learning object development tools require special technical skills in the instructors to exploit reuse and customization features, leading sometimes to unsatisfactory user experiences. In this paper, we explore a new way to reuse and customization following Product Line Engineering principles and tools. We have applied product line-based document engineering tools to create the so-called Learning Object Authoring Tool (LOAT), which supports the development of learning materials following the Ciscos Reusable Information Object strategy. We describe the principles behind LOAT, outline its design, and give clues about how it may be used by instructors to create learning objects in their own disciplines.

On the Opportunities of Scalable Modeling Technologies: An Experience Report on Wind Turbines Control Applications Development

Scalability in modeling has many facets, including the ability to build larger models and domain specific languages (DSLs) efficiently. With the aim of tackling some of the most prominent scalability challenges in Model-based Engineering (MBE), the MONDO EU project developed the theoretical foundations and open-source implementation of a platform for scalable modeling and model management. The platform includes facilities for building large DSLs, for splitting large models into sets of smaller interrelated fragments, and enables modelers to construct and refine complex models collaboratively, among other features.

Traceability Mappings as a Fundamental Instrument in Model Transformations

Technological importance of traceability mappings for model transformations is well-known, but they have often been considered as an auxiliary element generated during the transformation execution and providing accessory information. This paper argues that traceability mappings should instead be regarded as a core aspect of the transformation definition, and a key instrument in the transformation management. We will show how a transformation can be represented as the result of execution of a metamodel mapping, which acts as a special encoding of the transformation definition. Since mappings enjoy Boolean operations as sets of links and sequential composition as sets of directed links, encoding transformations by mappings makes it possible to define these operations for transformations as well, which can be useful for model transformation reuse, compositional design, and chaining.

The MegaM@Rt2 ECSEL project: MegaModelling at Runtime – Scalable model-based framework for continuous development and runtime validation of complex systems

Abstract 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 traceability, integration, and communication between two fundamental system life cycle phases (design time and runtime) is another challenge requiring the scalability of MDE. This paper presents an overview of the ECSEL 1 project entitled “MegaModelling at runtime – Scalable model-based framework for continuous development and runtime validation of complex systems” (MegaM@Rt2), whose aim is to address the above mentioned challenges facing MDE. Driven by both large and small industrial enterprises, with the support of research partners and technology providers, MegaM@Rt2 aims to deliver a framework of tools and methods for: 1) system engineering/design and continuous development, 2) related runtime analysis and 3) global models and traceability management. Diverse industrial use cases (covering strategic domains such as aeronautics, railway, construction and telecommunications) will integrate and demonstrate the validity of the MegaM@Rt2 solution. This paper provides an overview of the MegaM@Rt2 project with respect to its approach, mission, objectives as well as to its implementation details. It further introduces the consortium as well as describes the work packages and few already produced deliverables.