Francesco Basciani

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.

Automated Clustering of Metamodel Repositories

Over the last years, several model repositories have been proposed in response to the need of the MDE community for advanced systems supporting the reuse of modeling artifacts. Modelers can interact with MDE repositories with different intents ranging from merely repository browsing, to searching specific artifacts satisfying precise requirements. The organization and browsing facilities provided by current repositories is limited since they do not produce structured overviews of the contained artifacts, and the ategorization mechanisms (if any) are based on manual activities. When dealing with large numbers of modeling artifacts, such limitations increase the effort for managing and reusing artifacts stored in model repositories. By focusing on metamodel repositories, in this paper we propose the application of clustering techniques to automatically organize stored metamodels and to provide users with overviews of the application domains covered by the available metamodels. The approach has been implemented in the MDEForge repository.

A tool for automatically selecting optimal model transformation chains

Developing model transformations can be a complex task and as such advanced reuse mechanisms are increasingly recognised as necessary for increasing both development productivity and quality of model transformations. Creating a new transformation by chaining existing ones is one of the possible ways to reuse existing transformations. However, chaining transformations can be an error prone and strenuous task especially in case of multiple potential chains. In this paper the CITRIC tool is presented as a solution to mitigate the problem of characterizing the multitude of transformation chains that can be defined by composing existing model transformations to bridge source and target metamodels that are of interest for the modeler. The tool is based on well-established algorithms, and it is able to support modelers when multiple transformation chains are available. The optimal chain is automatically selected based on two quality criteria i.e., metamodel coverage and information loss.

A Customizable Approach for the Automated Quality Assessment of Modelling Artifacts

In Model-Driven Engineering (MDE) giving a precise definition of quality models, identifying which quality attributes are of interest for specific stakeholders, and how relating and aggregating together quality attributes are still open issues. The main limitations of currently available quality approaches are limited extensibility, artifact specificity, and manual assessment. This paper proposes an approach supporting the definition of custom quality models consisting of hierarchically organized quality attributes whose evaluation depends on metrics specifically conceived and applied on the modeling artifacts to be analysed. A domain specific language is proposed to specify how quality attributes and metrics have to be aggregated. An execution environment is also provided to apply the defined quality models on actual modeling artifacts so to enable their automated quality assessment. Real applications of the approach are presented by defining and applying explanatory quality models suitably conceived to assess the quality of metamodels and transformations retrieved from public repositories.

Model-Based Architecture for Learning in Complex Organization

To improve their service quality modern organization employees have to understand and put in action latest procedures and rules while coping with quickly changing contexts and decreasing resources. To this end a model-based architecture with interrelated enriched models is required in order to fosters an informative learning approach in the learning-by-doing paradigm. Such architecture enables organization employees to learn by accessing and studying enriched business process models and related material in a process-driven learning approach. Zachman Framework is used to organize all the models through the definition of the relations among them.