- The document discusses the vision of developing smart modeling environments to support engineering and scientific work, with a focus on model-driven engineering.
- Key challenges include developing exploratory, literate, and live programming capabilities; multi-view, polyglot, collaborative modeling frameworks; and modeling platforms for data-centric applications.
- Example applications discussed are systems engineering and design space exploration, DevOps and digital twins, and modeling for smart cyber-physical systems and sustainability evaluation.
Quality of Experience in Smart EnvironmentsPedro Costa
The democratisation of powerful mobile devices and ubiquitous communication networks have paved the way for smart environments, whose main goal is to enhance users' experiences. In this talk we will briefly explore some of the opportunities and challenges in such an environment. A user-centric framework is proposed for the assessment of users' quality of experience, as well as the delivery of personalised services with the potential to improve their experience and influence behaviour. A version of the platform is currently being implemented in the context of public transportation, which will be presented alongside some preliminary results.
The development and evolution of an advanced IDE for a Domain-Specific Language (DSL) is a tedious task. Recent efforts in language workbenches result in frameworks that automatically provide syntactic tooling such as advanced editors. However, defining the execution semantics of languages and their tooling remains mostly hand crafted. Similarly to editors that share code completion or syntax highlighting, the development of advanced debuggers, animators, and others execution analysis tools shares common facilities, which should be reused among various DSLs.
In this talk, I will present and make a demo of the execution framework offered by the GEMOC studio, an Eclipse-based language and modeling workbench. The framework provides a generic interface to plug-in different execution engines associated to their specific metalanguages used to define the discrete-event operational semantics of DSMLs (e.g., Kermeta/Xtend, xMOF, ALE…). It also integrates generic runtime services that are shared among the approaches used to implement the execution semantics, such as graphical animation and omniscient debugging (provided by Sirius Animator).
Quality of Experience in Smart EnvironmentsPedro Costa
The democratisation of powerful mobile devices and ubiquitous communication networks have paved the way for smart environments, whose main goal is to enhance users' experiences. In this talk we will briefly explore some of the opportunities and challenges in such an environment. A user-centric framework is proposed for the assessment of users' quality of experience, as well as the delivery of personalised services with the potential to improve their experience and influence behaviour. A version of the platform is currently being implemented in the context of public transportation, which will be presented alongside some preliminary results.
The development and evolution of an advanced IDE for a Domain-Specific Language (DSL) is a tedious task. Recent efforts in language workbenches result in frameworks that automatically provide syntactic tooling such as advanced editors. However, defining the execution semantics of languages and their tooling remains mostly hand crafted. Similarly to editors that share code completion or syntax highlighting, the development of advanced debuggers, animators, and others execution analysis tools shares common facilities, which should be reused among various DSLs.
In this talk, I will present and make a demo of the execution framework offered by the GEMOC studio, an Eclipse-based language and modeling workbench. The framework provides a generic interface to plug-in different execution engines associated to their specific metalanguages used to define the discrete-event operational semantics of DSMLs (e.g., Kermeta/Xtend, xMOF, ALE…). It also integrates generic runtime services that are shared among the approaches used to implement the execution semantics, such as graphical animation and omniscient debugging (provided by Sirius Animator).
Modeling For Sustainability: Or How to Make Smart CPS Smarter?Benoit Combemale
Various disciplines use models for different purposes. An engineering model, including a software engineering model, is often developed to guide the construction of a non-existent system. A scientific model is created to better understand an existing phenomenon (i.e., an already existing system or a physical phenomenon). An engineering model may incorporate scientific models to build a smart cyber-physical system (CPS) that require an understanding of the surrounding environment to decide of the relevant adaptation to apply. Sustainability systems, i.e., smart CPS managing resource production, transport and consumption for the sake of sustainability (e.g., smart grid, city, farming system…), are typical examples of smart CPS. Due to the inherent complex nature of sustainability that must delicately balance trade-offs between social, environmental, and economic concerns, modeling challenges abound for both the scientific and engineering disciplines.
In this talk, I will present a vision that promotes a unique approach combining engineering and scientific models to enable informed decision on the basis of open and scientific knowledge, a broader engagement of society for addressing sustainability concerns, and incorporate those decisions in the control loop of smart CPS. I will introduce a research roadmap to support this vision that emphasizes the socio-technical benefits of modeling.
Cognification is the application of knowledge to boost the performance and impact of a process. We believe cognification could be a revolution in the way software is built.
Promoting Space-Aware Coordination: ReSpecT as a Spatial Computing Virtual Ma...Stefano Mariani
Situatedness is a fundamental requirement for to- day’s complex software systems—as well as for the computation models and programming languages used to build them. Spatial situatedness, in particular, is an essential feature for coordination models and languages, as they represent the most effective approach to face the critical issues of interaction. Following some seminal works [1], [2], [3], in this paper we try to bring some novel results from the Coordination field into the Spatial Computing perspective, by identifying a minimal set of primitives that could be used to build a virtual machine for a space-aware coordination language, using ReSpecT as our reference example.
Practical Aggregate Programming with Protelis @ SASO2017Danilo Pianini
Collective adaptive systems are an emerging class of networked and situated computational systems with a wide range of applications, such as in the Internet of Things, wireless sensor networks, and smart cities.
Engineering such systems poses a number of challenges, and in particular many approaches, based upon designing the machine-to-machine interaction directly, suffer from a local-to-global abstraction problem.
In this tutorial, we introduce the aggregate computing approach, rooted in the field calculus and practically available through the Protelis programming language, as a means to build collective, situated adaptive systems.
The approach focuses on programming the overall aggregate behaviour, making use of a ``resilience API,'' while leaving to these libraries and the language machinery the responsibility of mapping this to the behavior of individual devices.
This tutorial was first presented at the 11th IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2017) in Tucson, AZ, USA.
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To make sure that tomorrow's computing systems provide support for doing research reliably, computational scientists need to establish a dialog with designers of programming languages and systems, and that is my goal with this presentation. I will describe the particularities of computational science: data-centric approaches, situated software, exploration vs. consolidation of computational models, the role of specifications, interfacing independently developed components, and the central scientific requirement of inquirability. I will also outline how today's computing systems are insufficient, and discuss some of my own attempts to contribute to improving them.
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This is my CV for research positions. I am open for Applied research projects with tie-ups with University labs and with corporate keen to demonstrate their skills and proof of concepts.
Modeling For Sustainability: Or How to Make Smart CPS Smarter?Benoit Combemale
Various disciplines use models for different purposes. An engineering model, including a software engineering model, is often developed to guide the construction of a non-existent system. A scientific model is created to better understand an existing phenomenon (i.e., an already existing system or a physical phenomenon). An engineering model may incorporate scientific models to build a smart cyber-physical system (CPS) that require an understanding of the surrounding environment to decide of the relevant adaptation to apply. Sustainability systems, i.e., smart CPS managing resource production, transport and consumption for the sake of sustainability (e.g., smart grid, city, farming system…), are typical examples of smart CPS. Due to the inherent complex nature of sustainability that must delicately balance trade-offs between social, environmental, and economic concerns, modeling challenges abound for both the scientific and engineering disciplines.
In this talk, I will present a vision that promotes a unique approach combining engineering and scientific models to enable informed decision on the basis of open and scientific knowledge, a broader engagement of society for addressing sustainability concerns, and incorporate those decisions in the control loop of smart CPS. I will introduce a research roadmap to support this vision that emphasizes the socio-technical benefits of modeling.
Cognification is the application of knowledge to boost the performance and impact of a process. We believe cognification could be a revolution in the way software is built.
Promoting Space-Aware Coordination: ReSpecT as a Spatial Computing Virtual Ma...Stefano Mariani
Situatedness is a fundamental requirement for to- day’s complex software systems—as well as for the computation models and programming languages used to build them. Spatial situatedness, in particular, is an essential feature for coordination models and languages, as they represent the most effective approach to face the critical issues of interaction. Following some seminal works [1], [2], [3], in this paper we try to bring some novel results from the Coordination field into the Spatial Computing perspective, by identifying a minimal set of primitives that could be used to build a virtual machine for a space-aware coordination language, using ReSpecT as our reference example.
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Collective adaptive systems are an emerging class of networked and situated computational systems with a wide range of applications, such as in the Internet of Things, wireless sensor networks, and smart cities.
Engineering such systems poses a number of challenges, and in particular many approaches, based upon designing the machine-to-machine interaction directly, suffer from a local-to-global abstraction problem.
In this tutorial, we introduce the aggregate computing approach, rooted in the field calculus and practically available through the Protelis programming language, as a means to build collective, situated adaptive systems.
The approach focuses on programming the overall aggregate behaviour, making use of a ``resilience API,'' while leaving to these libraries and the language machinery the responsibility of mapping this to the behavior of individual devices.
This tutorial was first presented at the 11th IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2017) in Tucson, AZ, USA.
Open-Source Frameworks for Deep Learning: an OverviewVincenzo Lomonaco
The rise of deep learning over the last decade has led to profound changes in the landscape of the machine learning software stack both for research and production. In this talk we will provide a comprehensive overview of the open-source deep learning frameworks landscape with both a theoretical and hands-on approach. After a brief introduction and historical contextualization, we will highlight common features and distinctions of their recent developments. Finally, we will take at deeper look into three of the most used deep learning frameworks today: Caffe, Tensorflow, PyTorch; with practical examples and considerations worth reckoning in the choice of such libraries.
Talk of Ali Mousavi "Event-Modelling An Engineering Solution for Control and Analysis of Complex Systems" at 116th regular meeting of INCOSE Russian chapter, 14-Sep-2016
A computational scientist's wish list for tomorrow's computing systemskhinsen
Like many areas of modern life, scientific research has been transformed profoundly by information technology. Most of today's research relies on computers and software for core tasks such as data analysis and model exploration. This has created both new opportunities and new danger zones. The much discussed reproducibility crisis, for example, is largely the result of inappropriate use of computational tools.
To make sure that tomorrow's computing systems provide support for doing research reliably, computational scientists need to establish a dialog with designers of programming languages and systems, and that is my goal with this presentation. I will describe the particularities of computational science: data-centric approaches, situated software, exploration vs. consolidation of computational models, the role of specifications, interfacing independently developed components, and the central scientific requirement of inquirability. I will also outline how today's computing systems are insufficient, and discuss some of my own attempts to contribute to improving them.
Teodoro Montanaro councluded his Ph.D. in Control and Computer Engineering on Monday, September 10, 2018, with the final presentation and defense.
He presented his thesis "IoT Notifications: from Disruption to Benefit - Architectures for the Future of Notifications in the IoT", refereed by Giuliana A. Franceschinis (Università degli Studi del Piemonte Orientale) and Ana M. Bernardos (Universidad Politecnica de Madrid - ETSIDI) in front of the commission composed by the referees and Antonio Servetti (Politecnico di Torino), Marco Torchiano (Politecnico di Torino), and Cristina Gena (Università degli Studi di Torino).
This is my CV for research positions. I am open for Applied research projects with tie-ups with University labs and with corporate keen to demonstrate their skills and proof of concepts.
Software has eaten the world and will continue to impact society, spanning numerous application domains, including the way we're doing science and acquiring knowledge.
Software variability is key since developers, engineers, entrepreneurs, scientists can explore different hypothesis, methods, and custom products to hopefully fit a diversity of needs and usage.
In this talk, I will first illustrate the importance of software variability using different concrete examples.
I will then show that, though highly desirable, software variability also introduces an enormous complexity due to the combinatorial explosion of possible variants.
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Summer School EIT Digital.
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UBIQUITOUS COMPUTING Its Paradigm, Systems & Middlewarevivatechijri
This paper offers a survey of ubiquitous computing research which is the developing a scope that
gears communication technologies into routine life accomplishments. This study paper affords a types of the
studies that extents at the ubiquitous computing exemplar. In this paper, we present collective structure principles
of ubiquitous systems and scrutinize important developments in context-conscious ubiquitous structures. In toting,
this studies work affords a novel structure of ubiquitous computing system and an evaluation of sensors needed
for applications in ubiquitous computing. The goal of this studies work are 3-fold: i) help as a parameter for
researchers who're first-hand to ubiquitous computing and want to subsidize to this research expanse, ii) provide
a unique machine architecture for ubiquitous computing system, and iii) offer auxiliary studies ways necessary
for exceptional-of-provider assertion of ubiquitous computing..
Smart Modeling: On the Convergence of Scientific and Engineering ModelsBenoit Combemale
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.
Re-Engineering Graphical User Interfaces from their Resource Files with UsiRe...Jean Vanderdonckt
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1. Towards Smart Modeling (Environments)
Looking Into the Future of
Engineering and Scientific Environments
Prof. Benoit Combemale
University of Rennes 1
DiverSE (IRISA & Inria)
http://combemale.fr / @bcombemale
2. Full Professor of Software Engineering @ University of Rennes 1
Head of the Computer Science Department at ESIR
Researcher at IRISA & Inria (DiverSE team)
Adjunct Researcher at IRIT (SM@RT team)
Agility and Safety of complex software-intensive systems
Research interest in Software Engineering, incl.: Model-Driven Engineering, Software Language Engineering, Domain-Specific Languages,
Software-Product Lines, Software Validation & Verification, Resilience Engineering, Cyber-Physical Systems, ICT for Sustainability, Scientific
Computing.
Application domains: (smart) cyber-physical systems (transport, defense), internet of things (telecommunication, cities/farming, industry 4.0)
and environmental sciences (climate change, sustainability).
Chief Science Advisor at CosApp
Scientific Advisor in Software Engineering
Collaborations with Airbus, Safran, Thales, Orange, CEA, DGA, Obeo, Akka…
Leader of the Research Consortium and Project GEMOC at the Eclipse Foundation
Deputy Editor-in-Chief JOT Journal, Steering Committee chair SLE Conference
Prof. Benoit Combemale
Exploring Wild Software
benoit.combemale@irisa.fr
http://combemale.fr
@bcombemale
3. Disclaimers
● No, this is not yet another cool talk about AI applications!
○ but rather a reflection on how to intelligently design smart systems, and the various roles of
the different types of models involved and corresponding software languages
● I present a vision, not a solution
○ just a story (vision and experimentations), for the sake of science and fun!
○ based on intensive discussions with the scientific and industrial communities
● From an MDE point of view, where models and modeling languages are
(subjectively) cornerstone
6. Structured and Sound Programming
▸ Abstractions (modularity, resources,
computation, application domain…)
▸ Automation (dev/doc/test,
compilation/integration,
deployment, delivery…)
▸ Validation & Verification
▸ Great support to implement
▸ once we know what to implement!
6
Coding! Programming Modeling
7. Smart Modeling
Polyglot, literate
programming
7
Lightweight, modular,
customizable, distributed and
self-adaptable platform…
Web-based, Collaborative
modeling, modeling flow, social
engineering
Exploratory and live programming, digital twin
Coding! Programming Modeling
Socio-technical
coordination
8. Vision
● Environments for engineers and scientists?
A platform that bring all stakeholders together, enhance the collaboration, support
the social-technical coordination of the various artefacts/models, and foster the
exploration of innovative solutions, at any points of the systems’ lifecycle.
● Some scientific challenges:
○ Sound combination of exploratory, literate and live programming
○ Multi-view, polyglot, collaborative and lightweight Virtual Lab
○ Modeling framework for data-centric applications
8
Breakthroughs in future smart cyber-physical systems
require tools & methods for innovative thinking
11. Systems Engineering and DSE
● Drive complex multi-physics simulation from systems engineering models
○ Automatic coordination of simulation models according to the system architecture
○ Support for impact and tradeoff analysis, and design space exploration
○ A step towards live and exploratory CPS modeling
11
Opportunities in intelligent modeling assistance
Gunter Mussbacher, Benoît Combemale, Jörg Kienzle et al. Softw. Syst. Model. 19(5): 1045-1053 (2020).
in collaboration with
12. DevOps and Digital Twins
12
● Support of the social-technical coordination in space and time
○ Efficiency: through the adaptation and application of DevOps principles
○ Affordance: with the adoption of principles from agile methods
○ Satisfaction: thanks to gamification
Towards Model-Driven Digital Twin Engineering: Current Opportunities and Future Challenges
Francis Bordeleau, Benoît Combemale, Romina Eramo, Mark van den Brand, Manuel Wimmer. ICSMM 2020: 43-54.
in collaboration with
14. Smart Cyber-Physical Systems
14
Toward model-driven sustainability evaluation
Jörg Kienzle, Gunter Mussbacher, Benoit Combemale, et al.. Commun. ACM 63, 3 (March 2020), 80–91.
Scientific computing (e.g.,
numerical analysis)
Tradeoff analysis and
decision making (e.g.,
circular economy, territory
development)
Smart systems (e.g., smart
cities, farming, grid…)
15. Smart Cyber-Physical Systems
15
Toward model-driven sustainability evaluation
Jörg Kienzle, Gunter Mussbacher, Benoit Combemale, et al.. Commun. ACM 63, 3 (March 2020), 80–91.
Scientific computing (e.g.,
numerical analysis)
Tradeoff analysis and
decision making (e.g.,
circular economy, territory
development)
Smart systems (e.g., smart
cities, farming, grid…)
16. - 16
HPC FOR NUMERICAL ANALYSIS
in collaboration with
Fostering metamodels and grammars within a dedicated environment for HPC: the NabLab environment
Benoît Lelandais, Marie-Pierre Oudot, Benoît Combemale
In International Conference on Software Language Engineering (SLE), 2018
17. - 17
SCIENTIFIC COMPUTING - PERSPECTIVES
Debugging for Scientific Computing
Approach: sound combination of monitors and loggers
Data Integration
Approach: service-oriented simulation processes
Polyglot programming and execution environments
Approach: Truffle-based interoperability for DSLs
- 17
in collaboration with
18. Reliability in Scientific Computing
The more general-purpose the language is the more flexibility it will provide, but
also the more rigorous engineering principles and V&V activities it will require from
the language user
18
When Scientific Software Meets Software Engineering
Dorian Leroy, June Sallou, Johann Bourcier, Benoit Combemale. IEEE Computer, 2021.
19. MoniLog: runtime monitoring and logging
● Analyzing complex or data-intensive behaviors requires insightful data
○ alternative to debugging in scientific computing
● MoniLog: a unifying framework for defining:
○ loggers: extract data from program state and format it as messages
○ runtime monitors: evaluation of temporal properties on programs
○ moniloggers: combinations of loggers and monitors
● Moniloggers are defined in a language-agnostic way, relying on an
instrumentation interface provided by DSLs
19
Monilogging for Executable Domain-Specific Languages
Dorian Leroy, Benoît Lelandais, Marie-Pierre Oudot, Benoit Combemale. SLE 2021.
20. MoniLog: runtime monitoring and logging
20
Monilogging for Executable Domain-Specific Languages
Dorian Leroy, Benoît Lelandais, Marie-Pierre Oudot, Benoit Combemale. SLE 2021.
Implementation on the JVM,
using either AspectJ or Truffle
21. Smart Cyber-Physical Systems
21
Toward model-driven sustainability evaluation
Jörg Kienzle, Gunter Mussbacher, Benoit Combemale, et al.. Commun. ACM 63, 3 (March 2020), 80–91.
Scientific computing (e.g.,
numerical analysis)
Tradeoff analysis and
decision making (e.g.,
circular economy, territory
development)
Smart systems (e.g., smart
cities, farming, grid…)
22. Water Flood Prediction
● Integrated environment for scientific computing and decision making
○ Flexible, agile, collaborative, distributed & adaptive
● Application to environmental sciences
○ in collaboration with Osur (UR1)
○ other collaborations with Lancaster University (e.g., Data Science of the Natural Environment)
22
23. - 23
TRADE-OFF ANALYSIS - PERSPECTIVES
Virtual lab for scientific computing
Approach: web-based and scalable deployment of modeling environment
Challenge: process elicitation & structuration, model composition/integration,
continuous integration/deployment, calibration & sensibility analysis
Exploration for decision making and education
Approach: approximate computing techniques
Challenge: error estimate, uncertainty management, etc.
Domain-specific indicators for impact and
tradeoff analysis
Approach: domain-specific languages and active mapping
Challenge: advanced debugging, live modeling (i.e., immediate feedback
and direct manipulation) for what-if/how-to scenarios
24. ● Reduce the simulation time to better support trade-off analysis and decision making
● Application of approximate computing to scientific computing
● Work on the simulation code (white box) or the input data (black box)
Approximate Scientific Computing
24
Loop Aggregation for Approximate Scientific Computing
June Sallou, Alexandre Gauvain, Johann Bourcier, Benoît Combemale,
Jean-Raynald de Dreuzy. ICCS (2) 2020: 141-155.
25. Smart Cyber-Physical Systems
25
Toward model-driven sustainability evaluation
Jörg Kienzle, Gunter Mussbacher, Benoit Combemale, et al.. Commun. ACM 63, 3 (March 2020), 80–91.
Scientific computing (e.g.,
numerical analysis)
Tradeoff analysis and
decision making (e.g.,
circular economy, territory
development)
Smart systems (e.g., smart
cities, farming, grid…)
28. Towards Self-Adaptable Languages
Modern software systems
● Evolve in complex/changing environment (e.g, Cloud,
embedded systems)
● Need dynamic adaptation to best deliver the service
Self-adaptable languages
● Abstracts the design and execution of feedback loops
○ in the design-time environment, and
○ the run-time environment
● Free the language user from the implementation of :
○ The feedback loop
○ The trade-off analysis
● Allow continuous and automatic evolution of itself
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Towards Self-Adaptable Languages
Gwendal Jouneaux, Olivier Barais, Benoit Combemale, Gunter Mussbacher. Onward! 2021.
SEALS: A framework for building Self-Adaptive Virtual Machines
Gwendal Jouneaux, Olivier Barais, Benoit Combemale, Gunter Mussbacher. SLE 2021.
29. Take Away Messages
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Breakthroughs require innovative thinking and
collective intelligence
▸ Socio-technical coordination
▸ Modeling is key!
Smartness comes from human beings
▸ Model/data integration in time and space
▸ From modeling environment to virtual labs, to digital twin
▸ Live, exploratory and collaborative (meta)modeling
New challenges for software languages
▸ language specification should abstract new concerns
(coordination/integration, feedback loop, approximation…)
▸ language specification should support the development of new tools
(for reliability, trade-off analysis…)