In the model driven software development process, software is built by constructing one or more models and transforming these into other models.In turn these output models may be transformed into another set of models until finally the output consists of program code that can be executed. Ultimately, software is developed by triggering an intricate network of transformation executions.An open issue in this process is how to combine different transformation tools in a flexible and reliable manner in order to produce the required output. This paper presents a model transformation environment in which new transformation tools can be plugged in and used together with other available transformation tools. We describe how transformations can be composed. Furthermore, in the cause of answering the question where and how transformations can be successfully applied, we created a language-based taxonomy of model transformation applications.
Abstract. In this paper we describe an application of the theory of graph transformations to the practise of language design. In particular, we have defined the static and dynamic semantics of a small but realistic object-oriented language (called TAAL) by mapping the language constructs to graphs (the static semantics) and modelling their effect by graph transformation rules (the dynamic semantics). This gives rise to execution models for all TAAL-programs, which can be used as the basis for formal verification.This work constitutes a first step towards a method for defining all aspects of software languages, besides their concrete syntax, in a consistent and rigorous manner. Such a method facilitates the integration of formal correctness in the software development trajectory.
In the model driven world languages are usually specified by a (meta) model of their abstract syntax. For textual languages this is different from the traditional approach, where the language is specified by a (E)BNF grammar. Support for the designer of textual languages, e.g. a parser generator, is therefore normally based on grammars. This paper shows that similar support for language design based on metamodels is not only possible, but is even more powerful than the support based on grammars. In this paper we describe how an integrated development environment for a language can be generated from the language's abstract syntax metamodel, thus providing the language designer with the possibility to quickly, and with little effort, create not only a new language but also the tooling necessary for using this language.
Model transformations support a model-driven design by providing an automatic translation of abstract models into more concrete ones, and eventually program code. Crucial to a successful application of model transformations is their correctness, in the sense that the meaning (semantics) of the models is preserved. This is especially important if the models not only describe the structure but also the intended behaviour of the systems. Reasoning about and showing correctness is, however, often impossible as the source and target models typically lack a precise definition of their semantics.In this paper, we take a first step towards provably correct behavioural model transformations. In particular, we develop transformations from UML Activities (which are visual models) to programs in TAAL, which is a textual Java-like programming language. Both languages come equipped with formal behavioural semantics, which, moreover, have the same semantic domain. This sets the stage for showing correctness, which in this case comes down to showing that the behaviour of every (well-formed) UML Activity coincides with that of the corresponding TAAL program, in a well-defined sense.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.