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Integrating research grade
model indexing technologies to
commercial modelling tools:
feedback and benchmarks
Marcos Almei...
Modelio for Software
and System Engineering
• UML editor with 20 years’ history
• SysML
• MARTE
• UTP
• Code generation
• ...
The MONDO Project
 MONDO is a STREP FP7 EU project
 Start: 11/2013 End: 4/2016
 Total cost: 3.7M€
 Challenges:
 Model...
Partner roles
Use Cases, requirements
validation
 Ikerlan (ES)
 Softeam (FR)
 Soft-Maint (FR)
 UNINOVA (PT)
Disseminat...
May 2016 – Paris - ICSSEA 5
Integrating research grade model indexing technologies to
commercial modelling tools: feedback...
Videos on Modelio &
Constellation
May 2016 – Paris - ICSSEA 6
Integrating research grade model indexing technologies to
co...
Goals
 Before Mondo
 Constellation knows
model fragments by
their names.
 We did not have
efficient tools
dedicated to ...
Objectives
 Evaluate MONDO technologies within
Modelio
 Supporting large and complex model
repositories (sets of models)...
Measures to evaluate
 Capability of MONDO to provide a user
friendly interface for managing
 ModelToModel transformation...
Hawk
 A heterogeneous model indexing
framework.
May 2016 – Paris - ICSSEA 10
Integrating research grade model indexing te...
Hawk - Constellation Integration
 Integrated architecture
May 2016 – Paris - ICSSEA 11
Integrating research grade model i...
Hawk - Constellation : Results
 Integration of Hawk query engine to
constellation.
 Capability to execute
queries on all...
Hawk - Constellation : Results
 Introducing Statistics based on all our
models of repositories.
 Implemented using speci...
Hawk - Constellation : Results
 Capability to provide an improved
collaborative environment.
 Ex : Help customer to comp...
Video on Constellation
integration
May 2016 – Paris - ICSSEA 15
Integrating research grade model indexing technologies to
...
Benchmarks
 Hawk space use & indexing time
 Model to Text transformations
May 2016 – Paris - ICSSEA 16
Integrating resea...
Benchmark architecture
ModelioEclipse
Model
Generator
HawkHawk local
resource
Index
Model Repository
Model
EGL
Transformat...
Benchmarks: Hawk space use &
indexing time – 1/2
 Model repository
 3.44 GB / 1,239,829 model elements
 1M model elemen...
Benchmarks: Hawk space use &
indexing time – 2/2
 Questions
 Is storage space and indexing time linear?
• How does it co...
Results – Generated models - Trends
 Disk space grows linearly (good sign!)
 Growth is less steep than Modelio’s 
Hawk ...
Results – Generated models - Trends
 Indexing time grows linearly (good sign!)
 Even if indexing may take a lot of time,...
Results – Model repository
 Indexing time
 4 days and 2 hours
• Remember: Modelio can’t handle all these
models at the s...
Results – Model repository – on a
later version
 Indexing time
 5 hours 14 minutes (yes, 18x faster!)
• Helped optimizin...
Model to Text transformations
 Document generation task (Markdown)
 Implemented in
 MONDO: EGL (Epislon Generation
Lang...
Results – M2T
 Here’s where the use of MONDO
technologies pays the most
 Hawk + EGL 175 to 602 times faster than
Modelio...
Video on Hawk indexing and
Model to text
transformations
May 2016 – Paris - ICSSEA 26
Integrating research grade model ind...
Specific measures – Full compliance
Measure Conclusions
Time improvement for change
propagation and notification among
con...
Conclusion
 Hawk provides us a way to index all
our model fragment whatever their
hosting technology.
 Hawk provides a p...
Further enhancements
 Improvements
 MONDO collaboration tools should support
other modelling technologies besides EMF.
...
Questions ?
May 2016 – Paris - ICSSEA 30
Integrating research grade model indexing technologies to
commercial modelling to...
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Integrating research grade model indexing technologies to commercial modelling tools: feedback and benchmarks

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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.

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Integrating research grade model indexing technologies to commercial modelling tools: feedback and benchmarks

  1. 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. 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. 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. 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. 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. 6. Videos on Modelio & Constellation May 2016 – Paris - ICSSEA 6 Integrating research grade model indexing technologies to commercial modelling tools: feedback and benchmarks
  7. 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. 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. 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. 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. 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. 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. 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. 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. 15. Video on Constellation integration May 2016 – Paris - ICSSEA 15 Integrating research grade model indexing technologies to commercial modelling tools: feedback and benchmarks
  16. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 30. Questions ? May 2016 – Paris - ICSSEA 30 Integrating research grade model indexing technologies to commercial modelling tools: feedback and benchmarks

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