Localized model transformations for building large-scale transformations

International audienceModel-Driven Engineering (MDE) exploits well-defined, tool-supported modelling languages and operations applied to models created using these languages. Model transformation is a critical part of the use of MDE. It has been argued that transformations must be engineered systematically, particularly when the languages to which they are applied are large and complicated – e.g., UML 2.x and profiles such as MARTE – and when the transformation logic itself is complex. We present an approach to designing large model transformations for large languages, based on the principle of separation of concerns. Specifically, we define a notion of localized transformations that are restricted to apply to a subset of a modelling language; a composition of localized transformations is then used to satisfy particular MDE objectives, such as the design of very large transformations. We illustrate the use of localized transformations in a concrete example applied to large transformations for system-on-chip co-design

Qualifying chains of transformation with coverage based evaluation criteria

Abstract. In Model-Driven Engineering (MDE) the development of complex and large transformations can benefit from the reuse of smaller ones that can be composed according to user requirements. Composing transformations is a complex problem: typically smaller transformations are discovered and selected by developers from different and heterogeneous sources. Then the identified transformations are chained by means of manual and error-prone composition processes. Based on our approach, when we propose one or more transformation chains to the user, it is difficult for him to choose one path instead of another without considering the semantic properties of a transformation. In this paper when multiple chains are proposed to the user, according to his requirements, we propose an approach to classify these suitable chains with respect to the coverage of the metamodels involved in the transformation. Based on coverage value, we are able to qualify the transformation chains with an evaluation criteria which gives as an indication of how much information a transformation chain covers over another

Generator-Composition for Aspect-Oriented Domain-Specific Languages

Software systems are complex, as they must cover a diverse set of requirements describing functionality and the environment. Software engineering addresses this complexity with Model-Driven Engineering ( MDE ). MDE utilizes different models and metamodels to specify views and aspects of a software system. Subsequently, these models must be transformed into code and other artifacts, which is performed by generators. Information systems and embedded systems are often used over decades. Over time, they must be modified and extended to fulfill new and changed requirements. These alterations can be triggered by the modeling domain and by technology changes in both the platform and programming languages. In MDE these alterations result in changes of syntax and semantics of metamodels, and subsequently of generator implementations. In MDE, generators can become complex software applications. Their complexity depends on the semantics of source and target metamodels, and the number of involved metamodels. Changes to metamodels and their semantics require generator modifications and can cause architecture and code degradation. This can result in errors in the generator, which have a negative effect on development costs and time. Furthermore, these errors can reduce quality and increase costs in projects utilizing the generator. Therefore, we propose the generator construction and evolution approach GECO, which supports decoupling of generator components and their modularization. GECO comprises three contributions: (a) a method for metamodel partitioning into views, aspects, and base models together with partitioning along semantic boundaries, (b) a generator composition approach utilizing megamodel patterns for generator fragments, which are generators depending on only one source and one target metamodel, (c) an approach to modularize fragments along metamodel semantics and fragment functionality. All three contributions together support modularization and evolvability of generators