Model-Driven Engineering (MDE) refers to the systematic use of models as first class entities throughout the software development life cycle. Over the last few years, many MDE technologies have been conceived for developing domain specific modeling languages, and for supporting a wide range of model management activities. However, existing modeling platforms neglect a number of important features that if missed reduce the acceptance and the relevance of MDE in industrial contexts, e.g., the possibility to search and reuse already developed modeling artifacts, and to adopt model management tools as a service. In this presentation we propose MDEForge a novel extensible Web-based modeling platform specifically conceived to foster a community-based modeling repository, which underpins the development, analysis and reuse of modeling artifacts.~Moreover, it enables the adoption of model management tools as software-as-a-service that can be remotely used without overwhelming the users with intricate and error-prone installation and configuration procedures.

1. MDEForge: an extensible
Web-based modeling platform
Dipartimento di Ingegneria e Scienze
dell’Informazione e Matematica
Università degli Studi dell’Aquila
Francesco Basciani
Juri Di Rocco
Davide Di Ruscio
Amleto Di Salle
Ludovico Iovino
Alfonso Pierantonio

2. 2 Introduction
Over the last decades many MDE technologies have
been conceived to support a wide range of modeling
and model management activities
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3. 3 Challenges
• A wider adoption of MDE technologies is still an
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issue*
* Whittle, J., Hutchinson, J., Rouncefield, M., Burden, H., Heldal, R.: Industrial Adoption of Model-
Driven Engineering: Are the Tools Really the Problem? In: MODELS. Volume 8107 of LNCS. Springer
Berlin Heidelberg (2013) 1–17

4. 4 Challenges
• A wider adoption of MDE technologies is still an
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issue
• Discovery and reuse of existing modeling
artefacts is very limited

5. 5 Challenges
• A wider adoption of MDE technologies is still an
CloudMDE2014 – 30 SEPT 2014, Valencia, Spain
issue
• Discovery and reuse of existing modeling
artefacts is very limited

6. 6 Challenges
• A wider adoption of MDE technologies is still an
CloudMDE2014 – 30 SEPT 2014, Valencia, Spain
issue*
• Discovery and reuse of existing modeling
artefacts is very limited

7. 7 Challenges
• A wider adoption of MDE technologies is still an
CloudMDE2014 – 30 SEPT 2014, Valencia, Spain
issue*
• Discovery and reuse of existing modeling
artefacts is very limited
• Modelling and model management tools are
distributed as software packages that need to be
downloaded and installed

8. 8 Challenges
• A wider adoption of MDE technologies is still an
CloudMDE2014 – 30 SEPT 2014, Valencia, Spain
issue*
• Discovery and reuse of existing modeling
artefacts is very limited
• Modelling and model management tools are
distributed as software packages that need to be
downloaded and installed

9. 9 Some tools providing storage features…
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10. 10
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11. 11 …we need more
An extensible platform that permits to
• store and manage any kind of modeling artefacts
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and tools
• use model management tools as software as a
service
• Modeling as a Service (MaaS) initiative*
*Hugo Bruneliere, Jordi Cabot and Frédéric Jouault, Combining Model-Driven Engineering and Cloud
Computing. MDA4ServiceCloud'10 (ECMFA 2010)

12. 12 MDEForge
• Community-based repository of modeling artifacts
• It enables the adoption of model management
tools as software as a service
• It is modular and extensible
• It will support advanced mechanisms to query the
repository and find the required modeling artifacts
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The platform is at its early stages
http://www.mdeforge.org/

13. 13 MDEForge users
• Developers of modeling artifacts: communities
of users that might want to share their tools and
enable their adoption and refinement by other
users
• Developers of MDEForge extensions:
experienced users might contribute by proposing
new extensions to be included in the platform
• End-users: By means of the Web access and the
REST API the platform enables end-users to
search and use (meta)models, transformations,
and editors available in the repository
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14. 14 MDEForge architecture
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15. 15 The MDEForge Repository
Repository metamodel fragment
Simple repository content
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16. 16 The MDEForge Core
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17. 17
Examples of MDEForge extensions
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18. 18 Automated chaining of model transformations
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Problem

19. 19 Automated chaining of model transformations
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Solution: chaining process

20. 20 Automated chaining of model transformations
Solution: representation of the related artifacts
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21. 21 Automated chaining of model transformations
Solution: developed MDEForge extension
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22. 22 Measuring metamodels
• Understanding typical metamodel characteristics
• Different metrics applied on a corpus of more than
450 metamodels have been correlated
• to measure metamodeling aspects that modelers
typically use
• abstraction, inheritance, composition, …
• to reveal what are the common characteristics that can
increase the complexity of metamodels
J. Di Rocco, D. Di Ruscio, L. Iovino and A. Pierantonio, Mining metrics for understanding metamodel
characteristics, in: ICSE 2014 Workshop on Modeling in Software Engineering (MiSE 2014), Hyderabad,
India, 2014
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23. 23 Measuring metamodels
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24. 24 Measuring metamodels
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25. 25 Measuring metamodels
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26. 26 Public and Private
MDEForge installations
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Public MDEForge
Private MDEForge 1 ….. Private MDEForge n

27. 27 Conclusion and future works
• Lack of reusability during model-driven engineering
(e.g., creating a DSL from scratch)
• Need to install and configure desktop-based
applications to perform MDE development activities
• MDEForge has been proposed as an extensible
modeling platform supporting the creation of a
community-based modeling repository
• core services that can be extended and all of them are
remotely available as software as a service
• users are not overwhelmed with intricate and error-prone
installation and configuration procedures
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28. 28 Conclusion and future works
We want to:
• investigate how to support advanced queries of
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the repository
• manage public and private installations of the
MDEForge
• investigate issues that are typical in Cloud
computing, e.g., scalability of the platform, and
workload management
• deal with licensing issues

29. 29
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