Softeam is a French company with more than 20 years of experience producing UML-based modelling environments on top of its Modelio CASE tool. With the increase of complexity of models and modelling teams, came the demand for supporting scalable collaborative tools for modelling. This new demand lead to the creation of Constellation, Softeam’s enterprise model management solution. As part of this effort, Softeam joined the MONDO FP7 EU project, which develops tools and methodologies for dealing with the challenges of scalability in model driven engineering. In this experimentation, we will describe the integration of the model indexer Hawk produced by the Enterprise Systems research group at the University of York into the commercial modelling tool Modelio and into its Constellation collaboration tools, produced by Softeam. The focus of this presentation will be on the technical difficulties of integrating with commercial production grade tools, and on a benchmark of the performance of this integration. In summary, Hawk integration was performed by Softeam engineers; a functional prototype was obtained in three months, and this prototype was subsequently improved until the end of the project. On the benchmarking side, for big models, we found out that in some situations Hawk index requires half the space required by Modelio to store models, and that combining Hawk and EGL generates documents between two and three orders of magnitude faster than Modelio itself.

1. Integrating research grade
model indexing technologies to
commercial modelling tools:
feedback and benchmarks
Marcos Almeida, Antonin Abhervé, Alessandra
Bagnato
SOFTEAM – France
Antonio García-Domínguez, Konstantinos Barmpis
UoY– UK
May 2016 – Paris - ICSSEA 1
Integrating research grade model indexing technologies to
commercial modelling tools: feedback and benchmarks

2. Modelio for Software
and System Engineering
• UML editor with 20 years’ history
• SysML
• MARTE
• UTP
• Code generation
• Documentation
• Teamwork
• Available under open source
at Modelio.org!
May 2016 – Paris - ICSSEA 2
Integrating research grade model indexing technologies to
commercial modelling tools: feedback and benchmarks

3. The MONDO Project
 MONDO is a STREP FP7 EU project
 Start: 11/2013 End: 4/2016
 Total cost: 3.7M€
 Challenges:
 Model management languages struggle with
models containing more than a few 100Ks
model elements
 XMI is great for interoperability but its
performance is poor
 There is little guidance on designing large
DSLs / DSLs for large models
May 2016 – Paris - ICSSEA 3
Integrating research grade model indexing technologies to
commercial modelling tools: feedback and benchmarks

4. Partner roles
Use Cases, requirements
validation
 Ikerlan (ES)
 Softeam (FR)
 Soft-Maint (FR)
 UNINOVA (PT)
Dissemination and
industry standards
 Open Group (UK)
Technology providers
 Softeam (FR)
 UNINOVA (PT)
Research/development
 ARMINES (FR)
 Auton. Univ of Madrid (ES)
 Budapest University of Technology and
Economics (HU)
 Univ of York (UK)
May 2016 – Paris - ICSSEA 4
Integrating research grade model indexing technologies to
commercial modelling tools: feedback and benchmarks

5. May 2016 – Paris - ICSSEA 5
Integrating research grade model indexing technologies to
commercial modelling tools: feedback and benchmarks
From a single model to a constellation of
models
Enterprise level project
management
 Project catalog
 Fragments
organization
 Inter-projects links
 Versions and variants
Communication
– Reports generation
– Project dashboard
– News and activity feeds
Shared model
repository
– SVN Model Fragment
repository
– RAMC Model
Fragment repository
– HTTP Model Fragment
repository
 Constellation main features

6. Videos on Modelio &
Constellation
May 2016 – Paris - ICSSEA 6
Integrating research grade model indexing technologies to
commercial modelling tools: feedback and benchmarks

7. Goals
 Before Mondo
 Constellation knows
model fragments by
their names.
 We did not have
efficient tools
dedicated to querying
Models.
 After Mondo
 We looked for a way
to know the content
and organization of all
elements in model
fragments.
 We looked for a way
to query all of our
models.
May 2016 – Paris - ICSSEA 7
Integrating research grade model indexing technologies to
commercial modelling tools: feedback and benchmarks

8. Objectives
 Evaluate MONDO technologies within
Modelio
 Supporting large and complex model
repositories (sets of models)
 Supporting large collaborating teams
 Should:
 Implement a demonstrator
 Document experiences gained
May 2016 – Paris - ICSSEA 8
Integrating research grade model indexing technologies to
commercial modelling tools: feedback and benchmarks

9. Measures to evaluate
 Capability of MONDO to provide a user
friendly interface for managing
 ModelToModel transformations
 ModelToText transformations.
 Capability of MONDO to provide scalable
execution of transformation information.
 Capability of MONDO to provide scalable
execution of queries.
 Capability of MONDO to provide an
improved collaborative environment.
May 2016 – Paris - ICSSEA 9
Integrating research grade model indexing technologies to
commercial modelling tools: feedback and benchmarks

10. Hawk
 A heterogeneous model indexing
framework.
May 2016 – Paris - ICSSEA 10
Integrating research grade model indexing technologies to
commercial modelling tools: feedback and benchmarks

11. Hawk - Constellation Integration
 Integrated architecture
May 2016 – Paris - ICSSEA 11
Integrating research grade model indexing technologies to
commercial modelling tools: feedback and benchmarks

12. Hawk - Constellation : Results
 Integration of Hawk query engine to
constellation.
 Capability to execute
queries on all of our models
 Provide an holistic view of
the composition of our
repositories
May 2016 – Paris - ICSSEA 12
Integrating research grade model indexing technologies to
commercial modelling tools: feedback and benchmarks

13. Hawk - Constellation : Results
 Introducing Statistics based on all our
models of repositories.
 Implemented using specific Hawk
queries
May 2016 – Paris - ICSSEA 13
Integrating research grade model indexing technologies to
commercial modelling tools: feedback and benchmarks

14. Hawk - Constellation : Results
 Capability to provide an improved
collaborative environment.
 Ex : Help customer to compose a project in
constellation using Hawk query to filter
available fragments,…
May 2016 – Paris - ICSSEA 14
Integrating research grade model indexing technologies to
commercial modelling tools: feedback and benchmarks

15. Video on Constellation
integration
May 2016 – Paris - ICSSEA 15
Integrating research grade model indexing technologies to
commercial modelling tools: feedback and benchmarks

16. Benchmarks
 Hawk space use & indexing time
 Model to Text transformations
May 2016 – Paris - ICSSEA 16
Integrating research grade model indexing technologies to
commercial modelling tools: feedback and benchmarks

17. Benchmark architecture
ModelioEclipse
Model
Generator
HawkHawk local
resource
Index
Model Repository
Model
EGL
Transformation
Output File
Document
Generator
May 2016 – Paris - ICSSEA 17
Integrating research grade model indexing technologies to
commercial modelling tools: feedback and benchmarks

18. Benchmarks: Hawk space use &
indexing time – 1/2
 Model repository
 3.44 GB / 1,239,829 model elements
 1M model elements of many different types 
Modelio cannot load it
 Generated models
 From 1K elements to 1M elements (most typical
types: Class, Packages, Operations etc.)
 Environment
 Machine: Dual core 2.7 GHz / 8GB RAM Dell
Notebook
• This is more about trends than absolute numbers!
 Modelio 3.4.1b
 Hawk 1.0.0.201602181354
May 2016 – Paris - ICSSEA 18
Integrating research grade model indexing technologies to
commercial modelling tools: feedback and benchmarks

19. Benchmarks: Hawk space use &
indexing time – 2/2
 Questions
 Is storage space and indexing time linear?
• How does it compare to Modelio storage
space linearity?
 How long does it take to index huge
models?
May 2016 – Paris - ICSSEA 19
Integrating research grade model indexing technologies to
commercial modelling tools: feedback and benchmarks

20. Results – Generated models - Trends
 Disk space grows linearly (good sign!)
 Growth is less steep than Modelio’s 
Hawk tends to require less space for
very big models.
May 2016 – Paris - ICSSEA 20
Integrating research grade model indexing technologies to
commercial modelling tools: feedback and benchmarks

21. Results – Generated models - Trends
 Indexing time grows linearly (good sign!)
 Even if indexing may take a lot of time,
re-indexing is quite fast.
 On a later version, indexing is 25x faster!
May 2016 – Paris - ICSSEA 21
Integrating research grade model indexing technologies to
commercial modelling tools: feedback and benchmarks

22. Results – Model repository
 Indexing time
 4 days and 2 hours
• Remember: Modelio can’t handle all these
models at the same time!!
 Update time
 26 min
• Still good for back-end tasks, like
computing stats, generating docs, etc.
May 2016 – Paris - ICSSEA 22
Integrating research grade model indexing technologies to
commercial modelling tools: feedback and benchmarks

23. Results – Model repository – on a
later version
 Indexing time
 5 hours 14 minutes (yes, 18x faster!)
• Helped optimizing Hawk for models
composed of lots of small files.
 Update time
 50 s
• Quite good for collaborative scenarios!
May 2016 – Paris - ICSSEA 23
Integrating research grade model indexing technologies to
commercial modelling tools: feedback and benchmarks

24. Model to Text transformations
 Document generation task (Markdown)
 Implemented in
 MONDO: EGL (Epislon Generation
Language)
 Modelio: Jython
May 2016 – Paris - ICSSEA 24
Integrating research grade model indexing technologies to
commercial modelling tools: feedback and benchmarks

25. Results – M2T
 Here’s where the use of MONDO
technologies pays the most
 Hawk + EGL 175 to 602 times faster than
Modelio
~3.3h
~19s
May 2016 – Paris - ICSSEA 25
Integrating research grade model indexing technologies to
commercial modelling tools: feedback and benchmarks

26. Video on Hawk indexing and
Model to text
transformations
May 2016 – Paris - ICSSEA 26
Integrating research grade model indexing technologies to
commercial modelling tools: feedback and benchmarks

27. Specific measures – Full compliance
Measure Conclusions
Time improvement for change
propagation and notification among
concurrent users
Re-indexing time under 5ms
Time improvement percentage on
query execution
Queries on document generation
from 6 to 700 times faster than
Modelio Desktop
Time improvement percentage on the
execution of transformations for text
generation
Document generation from 6 to
700 times faster than Modelio
Desktop
Time improvement percentage on the
execution of transformations for
model generation
● Generation 2-17 times faster
than Desktop Modelio on a
well configured server and
moderately large models
● Provides functionality that was
not available in Constellation
before
May 2016 – Paris - ICSSEA 27
Integrating research grade model indexing technologies to
commercial modelling tools: feedback and benchmarks

28. Conclusion
 Hawk provides us a way to index all
our model fragment whatever their
hosting technology.
 Hawk provides a powerful query
engine which allow us to know the
content of our model fragment on
Constellation side.
 Packaged as JAR, the integration of
Hawk to our commercial tool was easy.
May 2016 – Paris - ICSSEA 28
Integrating research grade model indexing technologies to
commercial modelling tools: feedback and benchmarks

29. Further enhancements
 Improvements
 MONDO collaboration tools should support
other modelling technologies besides EMF.
 Future plans
 MEASURE collaboration
 CloudATL (model to model transformations
tool) integration to Constellation
May 2016 – Paris - ICSSEA 29
Integrating research grade model indexing technologies to
commercial modelling tools: feedback and benchmarks

30. Questions ?
May 2016 – Paris - ICSSEA 30
Integrating research grade model indexing technologies to
commercial modelling tools: feedback and benchmarks