Hans Vangheluwe

AToM3: A Tool for Multi-formalism and Meta-modelling

This article introduces the combined use of multiformalism modelling and meta-modelling to facilitate computer assisted modelling of complex systems. The approach allows one to model different parts of a system using different formalisms. Models can be automatically converted between formalisms thanks to information found in a Formalism Transformation Graph (FTG), proposed by the authors. To aid in the automatic generation of multi-formalism modelling tools, formalisms are modelled in their own right (at a meta-level) within an appropriate formalism. This has been implemented in the interactive tool AToM3. This tool is used to describe formalisms commonly used in the simulation of dynamical systems, as well as to generate custom tools to process (create, edit, transform, simulate, optimise, ...) models expressed in the corresponding formalism. AToM3 relies on graph rewriting techniques and graph grammars to perform the transformations between formalisms as well as for other tasks, such as code generation and operational semantics specification.

Computer Automated Multi-Paradigm Modeling: An Introduction

Modeling and simulation are quickly becoming the primary enablers for complex system design. They allow the representation of intricate knowledge at various levels of abstraction and allow automated analysis as well as synthesis. The heterogeneity of the design process, as much as of the system itself, however, requires a manifold of formalisms tailored to the specific task at hand. Efficient design approaches aim to combine different models of a system under study and maximally use the knowledge captured in them. Computer Automated Multi-Paradigm Modeling (CAMPaM) is the emerging field that addresses the issues involved and formulates a domain-independent framework along three dimensions: (1) multiple levels of abstraction, (2) multiformalism modeling, and (3) meta-modeling. This article presents an overview of the CAMPaM field and shows how transformations assume a central place. These transformation are, in turn, explicitly modeled themselves by graph grammars.

Meta-modelling and graph grammars for multi-paradigm modelling in AToM3

This paper presents the combined use of meta-modelling and graph grammars for the generation of visual modelling tools for simulation formalisms. In meta-modelling, formalisms are described at a meta-level. This information is used by a meta-model processor to generate modelling tools for the described formalisms. We combine meta-modelling with graph grammars to extend the model manipulation capabilities of the generated modelling tools: edit, simulate, transform into another formalism, optimize and generate code. We store all (meta-)models as graphs, and thus, express model manipulations as graph grammars.We present the design and implementation of these concepts in AToM3 (A_To_ol for M_ulti-formalism, M_eta-M_odelling). AToM3 supports modelling of complex systems using different formalisms, all meta-modelled in their own right. Models in different formalisms may be transformed into a single common formalism for further processing. These transformations are specified by graph grammars. Mosterman and Vangheluwe [18] introduced the term multi-paradigm modelling to denote the combination of multiple formalisms, multiple abstraction levels, and meta-modelling. As an example of multi-paradigm modelling we present a meta-model for the Object-Oriented Continuous Simulation Language OOCSMP, in which we combine ideas from UML class diagrams (to express the OOCSMP model structure), Causal Block Diagrams (CBDs), and Statecharts (to specify the methods of the OOCSMP classes). A graph grammar is able to generate OOCSMP code, and then a compiler for this language (C-OOL) generates Java applets for the simulation execution.

Defining visual notations and their manipulation through meta-modelling and graph transformation

This paper presents a framework for the definition of visual notations (both syntax and semantics) based on meta-modelling and graph transformation. With meta-modelling it is possible to define the syntax of the notations we want to deal with. Meta-modelling tools are able to generate environments which accept models in the defined formalisms. These can be provided with further functionality by defining operations that can be performed to the models. One of the ways of defining such manipulations is through graph grammars, because models and meta-models can be represented as attributed, typed graphs. In this way, computations become high-level models expressed in the formal, graphical and intuitive notation of graph grammars. As an example, AToM 3 is used to automatically generate a tool for a Discrete Event Simulation notation. The tool’s functionality has been completely defined in a visual way through graph grammars, and includes a simulator (formalism’s operational semantics), a transformation into Timed Transition Petri nets (denotational semantics), an optimizer and a code generator for a GPSS simulator. r 2004 Elsevier Ltd. All rights reserved.

Computer Aided Multi-paradigm Modelling to Process Petri-Nets and Statecharts

This paper proposes a Multi-Paradigm approach to the modelling of complex systems. The approach consists of the combination of meta-modelling, multi-formalism modelling, and modelling at multiple levels of abstraction. We implement these concepts in AToM3, A Tool for Multi-formalism, Meta-Modelling. In AToM3, modelling formalisms are modelled in their own right at a meta-level within an appropriate formalism. AToM3 uses the information found in the meta-models to automatically generate tools to process (create, edit, check, optimize, transform and generate simulators for) the models in the described formalism. Model processing is described at a meta-level by means of models in the graph grammar formalism. As an example, meta-models for both syntax and semantics of Statecharts (without hierarchy) and Petri-Nets are presented. This includes a graph grammar modelling the transformation between Statecharts and Petri-Nets.

Explicit transformation modeling

Despite the pivotal significance of transformations for model-driven approaches, there have not been any attempts to explicitly model transformation languages yet. This paper presents a novel approach for the specification of transformations by modeling model transformation languages as domain-specific languages. For each pair of domain, the metamodel of the rules are (quasi-)automatically generated to create a language tailored to the transformation. Moreover, this method is very efficient when the transformation domains are the transformation rules themselves, which facilitates the design of higher-order transformations.

A framework for evolution of modelling languages

In model-driven engineering, evolution is inevitable over the course of the complete life cycle of complex software-intensive systems and more importantly of entire product families. Not only instance models, but also entire modelling languages are subject to change. This is in particular true for domain-specific languages, whose language constructs are tightly coupled to an application domain. The most popular approach to evolution in the modelling domain is a manual process, with tedious and error-prone migration of artefacts such as instance models as a result. This paper provides a taxonomy for evolution of modelling languages and discusses the different evolution scenarios for various kinds of modelling artefacts, such as instance models, meta-models, and transformation models. Subsequently, the consequences of evolution and the required remedial actions are decomposed into primitive scenarios such that all possible evolutions can be covered exhaustively. These primitives are then used in a high-level framework for the evolution of modelling languages. We suggest that our structured approach enables the design of (semi-)automatic modelling language evolution solutions.

Model-driven assessment of system dependability

Designers of complex real-time systems need to address dependability requirements early on in the development process. This paper presents a model-based approach that allows developers to analyse the dependability of use cases and to discover more reliable and safe ways of designing the interactions of the system with the environment. The hardware design and the dependability of the hardware to be used also needs to be considered. We use a probabilistic extension of statecharts to formally model the interaction requirements defined in the use cases. The model is then evaluated analytically based on the success and failure probabilities of events. The analysis may lead to further refinement of the use cases by introducing detection and recovery measures to ensure dependable system interaction. A visual modelling environment for our extended statecharts formalism supporting automatic probability analysis has been implemented in AToM3, A Tool for Multi-formalism and Meta-Modelling. Our approach is illustrated with an elevator control system case study.

Guest editorial: Special issue on computer automated multi-paradigm modeling

ion Models of system behavior can be described at different levels of abstraction or detail as well as by means of different formalisms. The particular formalism and level of abstraction chosen depends on the background and goals of the modeler as much as on the system modeled. This level of abstraction, which may be different for each of the components or views of a complex system, is determined by the available knowledge, the questions to be answered about the system’s behavior, the required accuracy of answers, etc. In “Modeling Methodology for Integrated Simulation of Embedded Systems” by Ledeczi, Davis, Neema, and Agrawal (forthcoming in a regular issue of TOMACS), it is shown how the execution (or interpretation) of models at different levels of abstraction can be facilitated. A model interpreter translates the model into an executable form. This multigrained simulation allows simulation of individual subsystems in isolation as well as replacing detailed behavior of aggregate subsystems by a high-level behavioral model that is more efficient. It even allows concurrent hardware/software simulation. Here, synchronous and asynchronous dataflow constitute the glue to combine the separate parts. Because modelers frequently switch between different levels of abstraction to answer different questions, inherent support for this is indispensable in modern system design projects. Introducing hierarchy and subsequently replacing parts of the hierarchy by atomic components is an intuitively appealing way to “abstract” models. Abstracting can be seen as a special kind of transformation which preserves some properties of the system. The challenge is to model these transformations, and use these models to automate model abstraction and abstraction level selection.

Towards Domain-specific Model Editors with Automatic Model Completion

Integrated development environments such as Eclipse allow users to write programs quickly by presenting a set of recommendations for code completion. Similarly, word processing tools such as Microsoft Word present corrections for grammatical errors in sentences. Both of these existing structure editors use a set of constraints expressed in the form of a natural language grammar to restrict/correct the user ( syntax-directed editing) or formal grammar (language-directed editing ) to aid document completion. Taking this idea further, in this paper we present an integrated software system capable of generating recommendations for model completion of partial models built in editors for domain-specific modeling languages. We present a methodology to synthesize model editors equipped with automatic completion from a modeling language’s declarative specification consisting of a meta-model with a visual syntax. This meta-model directed completion feature is powered by a first-order relational logic engine implemented in ALLOY. We incorporate automatic completion in the generative tool AToM3. We use the finite state machines modeling language as a concise running example. Our approach leverages a correct by construction philosophy that renders subsequent simulation of models considerably less error-prone.