István Dávid

Viatra 3: A Reactive Model Transformation Platform

Model-driven tools frequently rely on advanced technologies to support model queries, view maintenance, design rule validation, model transformations or design space exploration. Some of these features are initiated explicitly by domain engineers batch execution while others are executed automatically when certain trigger events are detected live execution. Unfortunately, their integration into a complex industrial modeling environment is difficult due to hidden interference and unspecified interaction between different features. In this paper, we present a reactive, event-driven model transformation platform over EMF models, which captures tool features as model queries and transformations, and provides a systematic, well-founded integration between a variety of such tool features. Viatra 3 offers a family of internal DSLs i.e. dedicated libraries to specify advanced tool features built on top of existing languages like EMF-IncQuery and Xtend. Its main innovation is a source incremental execution scheme built on the reactive programming paradigm ssupported by an event-driven virtual machine.

Streaming Model Transformations By Complex Event Processing

Streaming model transformations represent a novel class of transformations dealing with models whose elements are continuously produced or modified by a background process [1]. Executing streaming transformations requires efficient techniques to recognize the activated transformation rules on a potentially infinite input stream. Detecting a series of events triggered by compound structural changes is especially challenging for a high volume of rapid modifications, a characteristic of an emerging class of applications built on runtime models.

Foundations for Streaming Model Transformations by Complex Event Processing

Streaming model transformations represent a novel class of transformations to manipulate models whose elements are continuously produced or modified in high volume and with rapid rate of change. Executing streaming transformations requires efficient techniques to recognize activated transformation rules over a live model and a potentially infinite stream of events. In this paper, we propose foundations of streaming model transformations by innovatively integrating incremental model query, complex event processing (CEP) and reactive (event-driven) transformation techniques. Complex event processing allows to identify relevant patterns and sequences of events over an event stream. Our approach enables event streams to include model change events which are automatically and continuously populated by incremental model queries. Furthermore, a reactive rule engine carries out transformations on identified complex event patterns. We provide an integrated domain-specific language with precise semantics for capturing complex event patterns and streaming transformations together with an execution engine, all of which is now part of the Viatra reactive transformation framework. We demonstrate the feasibility of our approach with two case studies: one in an advanced model engineering workflow; and one in the context of on-the-fly gesture recognition.

Ontological reasoning for consistency in the design of cyber-physical systems

The design of Cyber-Physical Systems (CPS) involves a multitude of stakeholders. Each of these stakeholders has a specific view on the system under design. Unfortunately, when designers create artefacts in their different views in a concurrent manner, the integration of the different views may reveal inconsistencies. This leads to time consuming, iterative design processes where inconsistencies are resolved, in turn possibly creating new ones. It is hence necessary to reason explicitly about the view-specific properties that depend on, and influence properties of other views. This enables consistency during integration and reduces the development time and effort. In this paper we formalise the interrelationships between the different views, in the context of different design processes, to allow designers to meaningfully and efficiently manage inconsistencies. Our formalisation introduces ontological domain properties and their relations as the link between the view-specific properties used by the stakeholders. Thus, our approach combines the state of the art of Model-Based Systems Engineering (MBSE) and Semantic Web. The relevance of this approach is demonstrated by means of a motivating example.

Automated testing support for reactive domain-specific modelling languages

Domain-specific modelling languages (DSML) enable domain users to model systems in their problem domain, using concepts and notations they are familiar with. The process of domain-specific modelling (DSM) consists of two stages: a language engineering stage where a DSML is created, and a system modelling stage where the DSML is used. Because techniques such as metamodelling and model transformation allow for a efficient creation of DSMLs, and using DSMLs significantly increases productivity, DSM is very suitable for early prototyping. Many systems that are modelled using DSMLs are reactive, meaning that during their execution, they respond to external input. Because of the complexity of input and response behaviour of reactive systems, it is desirable to test models as early as possible. However, while dedicated testing support for specific DSMLs has been provided, no systematic support exists for testing DSML models according to DSM principles. In this paper, we introduce a technique to automatically generate a domain-specific testing framework from an annotated DSML definition. In our approach, the DSML definition consists of a metamodel, a concrete syntax definition and operational semantics described as a schedule of graph rewrite rules, thus covering a large class of DSMLs. Currently, DSMLs with deterministic behaviour are supported, but we provide an outlook to other (nondeterministic, real-time or continuous-time) DSMLs. We illustrate the approach with a DSML for describing an elevator controller. We evaluate the approach and conclude that compared to the state-of-the-art, our testing support is significantly less costly, and similar or better (according to DSM principles) testing support is achieved. Additionally, the generative nature of the approach makes testing support for DSMLs less error-prone while catering the need for early testing.