Various disciplines use models for different purposes. Engineers, e.g., software engineers, use engineering models to represent the system to implement, and scientists, e.g., environmentalists, use scientific models to represent the complexity of the world to understand and reason over it for analysis purpose. While the former tries to integrate all the properties in between the various engineering involved in the development process, the latter use models to internalize all the possible externalities of any changes, and later perform trade-off analysis. With the advent of smart CPS, the combination of scientific and engineering models becomes essential, respectively for openly and freely involving massive open data and predictive models in the decision process (either for trade-off analysis or dynamic adaptation purposes), and engineering models to support the smart design and reconfiguration process of modern CPS. It urges to provide the relevant facilities to software engineers for integrating into the future CPS the various models existing from the scientific community, and thus to support informed decisions, a broader engagement of the various stakeholders (incl. scientists, decision makers and the general public), and dynamic adaptations with regards to the expected political impact of the smart CPS. To motivate this challenge, I present various application domains where the combination of the two kinds of models is more than expected. Then I highlight some important differences in the underlying foundations that currently prevent their possible combination in a given development project.

1. CHALLENGE @ AMM, SAN VIGILIO DI MAREBBE, IT.
SMART MODELING
On the Convergence of Scientific and Engineering Models
BENOIT COMBEMALE
PROFESSOR IN SOFTWARE ENGINEERING
UNIV. TOULOUSE, FRANCE
HTTP://COMBEMALE.FR
BENOIT.COMBEMALE@IRIT.FR
@BCOMBEMALE

2. Smart Modeling
Benoit Combemale @ WMM, Jan. 2018
From Software Systems
▸ software design models for functional
and non-functional properties
Engineers
System Models
Software

3. Smart Modeling
Benoit Combemale @ WMM, Jan. 2018
To Cyber-Physical Systems
▸ multi-engineering design models
for global system properties
▸ models @ runtime (i.e., included
into the control loop) for dynamic
adaptations
Engineers
System Models Cyber-Physical
System
sensors actuators
Physical
System
Software
<<controls>><<senses>>

4. Smart Modeling
Benoit Combemale @ WMM, Jan. 2018
To Cyber-Physical Systems
▸ multi-engineering design models
for global system properties
▸ models @ runtime (i.e., included
into the control loop) for dynamic
adaptations
Engineers
System Models Cyber-Physical
System
sensors actuators
Physical
System
Software
<<controls>><<senses>>

5. Smart Modeling
Benoit Combemale @ WMM, Jan. 2018
To Smart Cyber-Physical Systems
▸ analysis models (incl. large-scale simulation,
constraint solver) of the surrounding context
related to global phenomena (e.g. physical,
economical, and social laws)
▸ predictive models (predictive techniques from
AI, machine learning, SBSE, fuzzy logic)
▸ user models (incl., general public/community
preferences) and regulations (political laws)
Engineers
System Models Smart
Cyber-Physical System
Context
sensors actuators
Physical
System
Software
<<controls>><<senses>>

6. Smart Modeling
Benoit Combemale @ WMM, Jan. 2018
And… What about Scientific Modeling?
▸ Models (computational and data-intensive sciences) for
analyzing and understanding physical phenomena
Context
Heuristics-Laws
Scientists
Physical Laws
(economic, environmental, social)

7. Simulator
Context
Simulation
Processes
Heuristics-Laws
Scientists
Physical Laws
(economic, environmental, social)
<<represents>>
Smart Modeling
Benoit Combemale @ WMM, Jan. 2018
And… What about Scientific Modeling?
▸ Simulators for tradeoff analysis,
what-if scenarios, analysis of
alternatives and adaptations to
environmental changes, etc.

8. Smart Modeling
Benoit Combemale @ WMM, Jan. 2018
Smart Modeling: A Grand Challenge
▸ Convergence of engineering and scientific models
▸ Engineering requires scientific models
▸ Science requires engineering models
▸ Grand Challenge: a modeling framework to support the integration
of data from sensors, open data, laws, regulations, scientific models
(computational and data-intensive sciences), engineering models
and preferences.
▸ Domain-specific languages (DSLs) for socio-technical coordination
▸ to engage engineers, scientists, decision makers, communities and the general
public
▸ to integrate analysis/predictive/user models into the control loop of smart CPS

9. Smart Modeling
Benoit Combemale @ WMM, Jan. 2018
Smart Modeling: A Grand Challenge
▸ Foundational
▸ Alignment and/or combination of different foundational, native, concepts
▸ Scale change, uncertainty, conflicting views are of primary importance in scientific models
▸ Diversity/complexity of DSL relationships
▸ Far beyond structural/behavioral alignment, refinement, decomposition
▸ Separation of concerns vs. Zoom-in/Zoom-out
▸ Data-intensive vs. Computational Models
▸ Integration of analysis and predictive models into DSL (syntax and semantics)
▸ Technological
▸ Live and collaborative (meta)modeling
▸ Minimize the round trip between the DSL specification, the model, and its application
(interpretation/compilation)
▸ Model experiencing environments (MEEs): what-if/for scenarios, trade-off analysis, design-space
exploration
▸ Web-based, cloud-based... modeling environments
▸ Social
▸ Model, model interpretation, uncertainty… rendering
▸ Social translucence

10. Smart Modeling
Benoit Combemale @ WMM, Jan. 2018
The Sustainability Evaluation ExperienceR (SEER)
Sustainability System
(e.g., smart farm)
(
Context
sensors actuators
Production/
Consumption
System
(e.g. farm)
Software
<<controls>><<senses>>
▸ Smart Cyber-Physical Systems
Modeling for Sustainability
B. Combemale, B. H.C. Cheng, A. Moreira, J.-M. Bruel, J. Gray
In Modeling in Software Engineering 2016 (MiSE'16)

11. Smart Modeling
Benoit Combemale @ WMM, Jan. 2018
The Sustainability Evaluation ExperienceR (SEER)
Heuristics-Laws
Scientists
Open Data
Scientific Models / Physical Laws
(economic, environmental, social)
SEER
Sustainability System
(e.g., smart farm)
(
Context
sensors actuators
Production/
Consumption
System
(e.g. farm)
Software
<<controls>><<senses>>
<<supplement
field data>>
<<feed>>
<<integrate>>
<<explore model
relations (tradeoff,
impact and conflict)>>
▸ Based on informed decisions
▸ with environmental, social and economic laws
▸ with open data
Modeling for Sustainability
B. Combemale, B. H.C. Cheng, A. Moreira, J.-M. Bruel, J. Gray
In Modeling in Software Engineering 2016 (MiSE'16)

12. Smart Modeling
Benoit Combemale @ WMM, Jan. 2018
The Sustainability Evaluation ExperienceR (SEER)
Heuristics-Laws
Scientists
Open Data
General Public
(e.g., individuals)
Policy Makers
(e.g., mayor)
MEEs
("what-if" scenarios)
Scientific Models / Physical Laws
(economic, environmental, social)
SEER
Sustainability System
(e.g., smart farm)
(
Context
sensors actuators
Production/
Consumption
System
(e.g. farm)
Software
<<controls>><<senses>>Communities
(e.g., farmers)
<<supplement
field data>>
<<provide configuration,
preferences, questions>>
<<present possible future
and variable indicators>>
<<feed>>
<<integrate>>
<<explore model
relations (tradeoff,
impact and conflict)>>
▸ Providing a broader engagement
▸ with "what-if" scenarios for general public and policy makers
Modeling for Sustainability
B. Combemale, B. H.C. Cheng, A. Moreira, J.-M. Bruel, J. Gray
In Modeling in Software Engineering 2016 (MiSE'16)

13. Smart Modeling
Benoit Combemale @ WMM, Jan. 2018
The Sustainability Evaluation ExperienceR (SEER)
Heuristics-Laws
Scientists
Open Data
General Public
(e.g., individuals)
Policy Makers
(e.g., mayor)
MEEs
("what-if" scenarios)
Scientific Models / Physical Laws
(economic, environmental, social)
SEER
Sustainability System
(e.g., smart farm)
(
Context
sensors actuators
Production/
Consumption
System
(e.g. farm)
Software
<<controls>><<senses>>Communities
(e.g., farmers)
<<adapt>>
<<supplement
field data>>
<<provide configuration,
preferences, questions>>
<<present possible future
and variable indicators>>
<<feed>>
<<integrate>>
<<explore model
relations (tradeoff,
impact and conflict)>>
▸ Supporting automatic adaptation
▸ for dynamically adaptable systems
Modeling for Sustainability
B. Combemale, B. H.C. Cheng, A. Moreira, J.-M. Bruel, J. Gray
In Modeling in Software Engineering 2016 (MiSE'16)

14. Smart Modeling
Benoit Combemale @ WMM, Jan. 2018
The Sustainability Evaluation ExperienceR (SEER)
Heuristics-Laws
Scientists
Open Data
General Public
(e.g., individuals)
Policy Makers
(e.g., mayor)
MEEs
("what-if" scenarios)
Scientific Models / Physical Laws
(economic, environmental, social)
SEER
Sustainability System
(e.g., smart farm)
(
Context
sensors actuators
Production/
Consumption
System
(e.g. farm)
Software
<<controls>><<senses>>Communities
(e.g., farmers)
<<adapt>>
<<supplement
field data>>
<<provide configuration,
preferences, questions>>
<<present possible future
and variable indicators>>
<<feed>>
<<integrate>>
<<explore model
relations (tradeoff,
impact and conflict)>>
▸ Application to health, farming system, smart grid…
Modeling for Sustainability
B. Combemale, B. H.C. Cheng, A. Moreira, J.-M. Bruel, J. Gray
In Modeling in Software Engineering 2016 (MiSE'16)

15. Smart Modeling
Benoit Combemale @ WMM, Jan. 2018
The Sustainability Evaluation ExperienceR (SEER)
Heuristics-Laws
Scientists
Open Data
General Public
(e.g., individuals)
Policy Makers
(e.g., mayor)
MEEs
("what-if" scenarios)
Scientific Models / Physical Laws
(economic, environmental, social)
SEER
Sustainability System
(e.g., smart farm)
(
Context
sensors actuators
Production/
Consumption
System
(e.g. farm)
Software
<<controls>><<senses>>Communities
(e.g., farmers)
<<adapt>>
<<supplement
field data>>
<<provide configuration,
preferences, questions>>
<<present possible future
and variable indicators>>
<<feed>>
<<integrate>>
<<explore model
relations (tradeoff,
impact and conflict)>>
Farmers
Agronomist
Irrigation
System
in collaboration with
MDE in Practice for Computational Science
Jean-Michel Bruel, Benoit Combemale, Ileana Ober, Hélène Raynal
In International Conference on Computational Science (ICCS), 2015

16. - 16
https://github.com/gemoc/farmingmodeling
FARMING SYSTEM MODELING (/ SOFTWARE DEFINED FARMING)
Modeling for Sustainability – B. Combemale (Univ. Rennes 1) – December 4th, 2016

17. - 17
WATER FLOOD PREDICTION
Modeling for Sustainability – B. Combemale (Univ. Rennes 1) – December 4th, 2016

18. - 18
HIGH PERFORMANCE COMPUTING
Modeling for Sustainability – B. Combemale (Univ. Rennes 1) – December 4th, 2016

19. - 19
HIGH PERFORMANCE COMPUTING
Modeling for Sustainability – B. Combemale (Univ. Rennes 1) – December 4th, 2016

20. Smart Modeling
Benoit Combemale @ WMM, Jan. 2018
Conclusion
▸ Integration of scientific models in the control loop of
smart CPS is key to provide more informed decisions, a
broader engagement, and eventually relevant runtime
reconfigurations
▸ SEER is a particular instantiation of such a vision for
sustainability systems

21. Smart Modeling
Benoit Combemale @ WMM, Jan. 2018
Take Away Messages
▸ From MDE to SLE
▸ Language workbenches support DS(M)L development
▸ On the globalization of modeling languages (GEMOC Initiative)
▸ Integrate heterogeneous models representing different engineering
concerns
▸ Language interfaces to support structural and behavioral relationships
between domains (i.e., DSLs)
▸ From software systems to smart CPS
▸ Interactions with the physical world limited to (i.e., fixed, in closed world)
control laws and data from the sensors
▸ What about the broader context in which the system involves?
▸ Physical / social / economic laws
▸ Predictive models
▸ Regulations, user preferences

22. Smart Modeling
Abstract.
Various disciplines use models for different purposes. Engineers, e.g., software engineers,
use engineering models to represent the system to implement, and scientists, e.g.,
environmentalists, use scientific models to represent the complexity of the world to
understand and reason over it for analysis purpose. While the former tries to integrate all
the properties in between the various engineering involved in the development process,
the latter use models to internalize all the possible externalities of any changes, and later
perform trade-off analysis.
With the advent of smart CPS, the combination of scientific and engineering models
becomes essential, respectively for openly and freely involving massive open data and
predictive models in the decision process (either for trade-off analysis or dynamic
adaptation purposes), and engineering models to support the smart design and
reconfiguration process of modern CPS. It urges to provide the relevant facilities to software
engineers for integrating into the future CPS the various models existing from the scientific
community, and thus to support informed decisions, a broader engagement of the various
stakeholders (incl. scientists, decision makers and the general public), and dynamic
adaptations with regards to the expected political impact of the smart CPS.
To motivate this challenge, I will present various application domains where the
combination of the two kinds of models is more than expected. Then I will highlight some
important differences in the underlying foundations that currently prevent their possible
combination in a given development project.
Smart Modeling
Benoit Combemale @ WMM, Jan. 2018