Ludovico Iovino

What is needed for managing co-evolution in MDE?

Metamodels can be considered one of the cardinal concepts of Model-Driven Engineering, one which a number of coordinated entities such as models, transformations and tools, are dependent on. Analogously to any software artifact, metamodels are equally prone to evolution during their lifetime. As a consequence, whenever a metamodel changes, any related entity must be consistently adapted for preserving its wellformedness, consistency, or intrinsic correctness. This paper discusses the problem of co-adapting models, transformations, and tools. Different aspects are taken into account and a prospective and unifying characterization is given with the intent of clarifying the main difficulties and outline the basic requirements for possible solutions. In this respect, EMFMigrate a comprehensive approach to the metamodel co-evolution problem is proposed.

On the Impact Significance of Metamodel Evolution in MDE.

Harnessing metamodels to engineer application domains is at the core of Model-Driven Engineering. A large number of artifacts pursuing a common scope are defined starting from metamodels which represent the nucleus of an ecosystem. Analogously to any software artifact, metamodels are equally prone to evolution. However, changing a metamodel might affect the components of the ecosystem. In fact, when a metamodel undergoes modifications, the related artifacts might require to be consistently adapted in order to recovery their validity. This is an intrinsically difficult process. It requires different techniques for each specific kind of artifact and can easily lead to inconsistencies and irremediable information erosion, if based on spontaneous and individual skills. This paper discusses the problem of identifying, predicting and evaluating the significance of the metamodel change impact over the existing artifacts. The approach is agnostic of the adaptation technique and formalizes the whole ecosystem and the relatedness of the involved artifacts in terms of megamodels. This allows developers i) to establish relationships between the metamodel and its related artifacts, and ii) to automatically identify those elements within the various artifacts affected by the metamodel changes. The approach can be considered as preparatory to any systematic adaptation process.

Coupled Evolution in Model-Driven Engineering

Model-driven engineering bases a wide range of artifacts on metamodels. When such metamodels evolve, such as a new version of Unified Modeling Language or Business Process Execution Notation or a company-specific metamodel, underlying artifacts often become invalid. In this article, the authors provide an overview of coupled evolution methods and tools to handle such dependencies. I look forward to hearing from both readers and prospective authors about this column and the technologies you want to know more about.

Evolutionary togetherness: how to manage coupled evolution in metamodeling ecosystems

In Model-Driven Engineering (MDE) metamodels are cornerstones for defining a wide range of related artifacts interlaced with explicit or implicit correspondences. According to this view, models, transformations, editors, and supporting tools can be regarded as a whole pursuing a common scope and therefore constituting an ecosystem. Analogously to software, metamodels are subject to evolutionary pressures too. However, changing a metamodel might compromise the validity of the artifacts in the ecosystem which therefore require to co-evolve as well in order to restore their validity. Different approaches have been proposed to support at different extent the adaptation of artifacts according to the changes operated on the corresponding metamodels. Each technique is specialized in the adaptation of specific kind of artifact (e.g., models, or transformations) by forcing modelers to learn different technologies and languages. This paper discusses the different relations occurring in a typical metamodeling ecosystem among the metamodel and the related artifacts, and identifies the commonalities which can be leveraged to define a unifying and comprehensive adaptation process. A language and corresponding supporting tools are also proposed for the management of metamodel evolution and the corresponding togetherness with the related artifacts.

Collaborative Repositories in Model-Driven Engineering

Recently proposed model repositories aim to support specific needs--for example, collaborative modeling, the ability to use different modeling tools in software life-cycle management, tool interoperability, increased model reuse, and the integration of heterogeneous models.

A Methodological Approach for the Coupled Evolution of Metamodels and ATL Transformations

Model-Driven Engineering is a software discipline that relies on (meta) models as first class entities and that aims to develop, maintain and evolve software by exploiting model transformations. Analogously to software, metamodels are subject to evolutionary pressures which might compromise a wide range of artefacts including transformations. In contrast with the problem of metamodel/model co-evolution, the problem of adapting model transformations according to the changes operated on the corresponding metamodels is to a great extent unexplored. This is largely due to its intricacy but also to the difficulty in having a mature process which on one hand is able to evaluate the cost and benefits of adaptations, and on the other hand ensures that consistent methods are used to maintain quality and design integrity during the adaptation. This paper proposes a methodological approach to the coupled evolution of ATL transformations aiming at evaluating its sustainability prior to any adaptation step based on the assessment of change impact significance.

Mining metrics for understanding metamodel characteristics

Metamodels are a key concept in Model-Driven Engineering. Any artifact in a modeling ecosystem has to be defined in accordance to a metamodel prescribing its main qualities. Hence, understanding common characteristics of metamodels, how they evolve over time, and what is the impact of metamodel changes throughout the modeling ecosystem is of great relevance. Similarly to software, metrics can be used to obtain objective, transparent, and reproducible measurements on metamodels too. In this paper, we present an approach to understand structural characteristics of metamodels. A number of metrics are used to quantify and measure metamodels and cross-link different aspects in order to provide additional information about how metamodel characteristics are related. The approach is applied on repositories consisting of more than 450 metamodels.

Translational semantics of a co-evolution specific language with the EMF transformation virtual machine

Model-to-model transformations are often employed to establish translational semantics of Domain-Specific Languages (DSLs) by mapping high-level models into more concrete ones. Such semantics are also executable when there exists a target platform able to execute the target models. Conceiving a transformation that targets a low-level language still remains arduous due to the large semantic gap between the DSL and the corresponding target language. In this respect, depending on the domain of the DSL, this task can be made easier by reusing an existing platform and bytecode language for that domain, as for instance the EMF Transformation Virtual Machine (EMFTVM) for the domain of model transformation. This paper defines executable semantics for EMFMigrate, a model transformation language specifically designed for managing the coupled evolution in model-driven development. To this end, the approach considers EMFTVM as the runtime engine targeted by the proposed semantic mappings.

Automated Chaining of Model Transformations with Incompatible Metamodels

In Model-Driven Engineering (MDE) models are first-class entities that are manipulated by means of model transformations. 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.

Managing the evolution of data-intensive Web applications by model-driven techniques

The adoption of Model-Driven Engineering (MDE) in the development of Web Applications permitted to decouple the functional description of applications from the underlying implementation platform. This is of paramount relevance for preserving the intellectual property encoded in models and making applications, languages and processes resilient to technological changes. This paper proposes a model-driven approach for supporting the migration and evolution of data-intensive Web applications. In particular, model differencing techniques are considered to realize a migration facility capable of detecting the modifications a model underwent during its lifecycle and to automatically derive from them the programs that are capable of migrating/adapting also those aspects which are not directly derivable from the source models, as for instance the data persistently stored in a database and the page layout usually written using graphic templates. The approach is validated by considering applications described with the beContent and WebML modeling languages.