Bottom-up meta-modelling: An interactive approach

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-33666-9_2Proceedings of 15th International Conference, MODELS 2012, Innsbruck, Austria, September 30–October 5, 2012The intensive use of models in Model-Driven Engineering (MDE) raises the need to develop meta-models with different aims, like the construction of textual and visual modelling languages and the specification of source and target ends of model-to-model transformations. While domain experts have the knowledge about the concepts of the domain, they usually lack the skills to build meta-models. These should be tailored according to their future usage and specific implementation platform, which demands knowledge available only to engineers with great expertise in MDE platforms. These issues hinder a wider adoption of MDE both by domain experts and software engineers. In order to alleviate this situation we propose an interactive, iterative approach to meta-model construction enabling the specification of model fragments by domain experts, with the possibility of using informal drawing tools like Dia. These fragments can be annotated with hints about the intention or needs for certain elements. A meta-model is automatically induced, which can be refactored in an interactive way, and then compiled into an implementation meta-model using profiles and patterns for different platforms and purposes.This work was funded by the Spanish Ministry of Economy and Competitivity (project “Go Lite” TIN2011-24139) and the R&D programme of the Madrid Region (project “e-Madrid” S2009/TIC-1650

Weaving Rules into [email protected] for Embedded Smart Systems

Smart systems are characterised by their ability to analyse measured data in live and to react to changes according to expert rules. Therefore, such systems exploit appropriate data models together with actions, triggered by domain-related conditions. The challenge at hand is that smart systems usually need to process thousands of updates to detect which rules need to be triggered, often even on restricted hardware like a Raspberry Pi. Despite various approaches have been investigated to efficiently check conditions on data models, they either assume to fit into main memory or rely on high latency persistence storage systems that severely damage the reactivity of smart systems. To tackle this challenge, we propose a novel composition process, which weaves executable rules into a data model with lazy loading abilities. We quantitatively show, on a smart building case study, that our approach can handle, at low latency, big sets of rules on top of large-scale data models on restricted hardware.Comment: pre-print version, published in the proceedings of MOMO-17 Worksho

A research roadmap towards achieving scalability in model driven engineering

International audienceAs Model-Driven Engineering (MDE) is increasingly applied to larger and more complex systems, the current generation of modelling and model management technologies are being pushed to their limits in terms of capacity and eciency. Additional research and development is imperative in order to enable MDE to remain relevant with industrial practice and to continue delivering its widely recognised productivity , quality, and maintainability benefits. Achieving scalabil-ity in modelling and MDE involves being able to construct large models and domain-specific languages in a systematic manner, enabling teams of modellers to construct and refine large models in a collaborative manner, advancing the state of the art in model querying and transformations tools so that they can cope with large models (of the scale of millions of model elements), and providing an infrastructure for ecient storage, indexing and retrieval of large models. This paper attempts to provide a research roadmap for these aspects of scalability in MDE and outline directions for work in this emerging research area

Performance analysis of persistence technologies for cloud repositories of models

The growing adoption of Model Driven Development (MDD) in companies during last decade arises some model interchange problems. Companies need support to interchange models and reuse parts of them for developing new projects. Traditional tools for model edition and model interchange have different performance issues related to the models storage. There are mainly two styles to organize the persistence of models into repositories: a complex and large model or a large amount of small models. This last approach is common in companies that generate software from models. In this paper, we analyse performance properties of different persistence technologies to store small/medium-scale models, the analysis results should be considered in the design of model repositories in the cloud. With this aim, we have designed and developed a generic architecture to evaluate each persistence technology under similar situations

Lightweight String Reasoning in Model Finding

International audienceModels play a key role in assuring software quality in the model-driven approach. Precise models usually require the definition of well-formedness rules to specify constraints that cannot be expressed graphically. The Object Constraint Language (OCL) is a de-facto standard to define such rules. Techniques that check the satisfiability of such models and find corresponding instances of them are important in various activities, such as model-based testing and validation. Several tools for these activities have been developed, but to our knowledge, none of them supports OCL string operations on scale that is sufficient for, e.g., model-based testing. As, in contrast, many industrial models do contain such operations, there is evidently a gap. We present a lightweight solver that is specifically tailored to generate large solutions for tractable string constraints in model finding, and that is suitable for directly express the main operations of the OCL datatype String. It is based on constraint logic programming (CLP) and constraint handling rules (CHR), and can be seamlessly combined with other constraint solvers in CLP. We have integrated our solver into the EMFtoCSP model finder, and we show that our implementation efficiently solves several common string constraints on a large instances

A taxonomy of tool-related issues affecting the adoption of model-driven engineering

Although poor tool support is often blamed for the low uptake of model-driven engineering (MDE), recent studies have shown that adoption problems are as likely to be down to social and organizational factors as with tooling issues. This article discusses the impact of tools on MDE adoption and practice and does so while placing tooling within a broader organizational context. The article revisits previous data on MDE use in industry (19 in-depth interviews with MDE practitioners) and reanalyzes that data through the specific lens of MDE tools in an attempt to identify and categorize the issues that users had with the tools they adopted. In addition, the article presents new data: 20 new interviews in two specific companies—and analyzes it through the same lens. A key contribution of the paper is a loose taxonomy of tool-related considerations, based on empirical industry data, which can be used to reflect on the tooling landscape as well as inform future research on MDE tools