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Model-Based Risk Assessment in
Multi-Disciplinary Systems Engineering
Euromicro Conference series on Software Engineering and
Advanced Applications (SEAA) 2015
Arndt Lueder, Nicole SchmidtStefan Biffl, Luca Berardinelli,
Emanuel Maetzler, Manuel Wimmer
Introduction
2
Multi-Disciplinary Engineering
 Multidisciplinary Domain
 Mechanical Engineering
 e.g., design, production, and operation of
machinery (powered tools)
 Electrical Engineering
 e.g., design of complex power system and
electronic circuits
 Software Engineering
 e.g., design, implementation, testing,
validation of software for machinery
• Heterogeneous document/tool landscape
 Mechanical Engineering
 Matlab, CAD tools…
 Electrical Engineering
 EPLAN
 Software Engineering
 Programming IDEs, Modeling Tools…
= domain = tool = doc
overall system design
mechanical
engineering
electrical
engineering
software
engineering
Introduction: Industry 4.0 and its principles
3
 Industry 4.0: computerization of
manufacturing. Driving principles
1. Interoperability among mechatronic
systems (a.k.a. cyber physical systems
CPS), humans and factories
2. Virtualization: a virtual copy of the
factory with sensed data
3. Decentralization: the ability of CPSs to
make decisions on their own
4. Real-Time Capability: monitoring,
analysis, planning, execution
5. Service Orientation: OPC Unified
Architecture (SOA)
6. Modularity: flexible adaptation to
changing requirements
= domain = tool = doc
overall system design
mechanical
engineering
electrical
engineering
software
engineering
Industry 4.0
Introduction: Engineering of Industrial Production Systems
4
 AutomationML (AML) standard for tool
data exchange
 AML docs are XML-based artifacts
 AML as pivotal language: Tool-
specific docs can be transformed in
AML docs
overall system design
mechanical
engineering
electrical
engineering
software
engineering
Industry 4.0
= domain = tool = doc
XML-based
artifacts
CAEX.xsd
Introduction: Engineering of Industrial Production Systems
5
Mechanical/Eletrical/Software
Components Library
«cloned»
Production System Model
«represents»
Lab-sized Production System
“Equipment Center for Distributed Systems,”
http://www.iafbg.ovgu.de/en/technische ausstattung cvs.html,
Institute of Ergonomics, Manufacturing Systems and
Automation at Otto-v.-Guericke University Magdeburg.
Introduction: Engineering of Industrial Production Systems
6
 Risk: the probability of an
occurring event which can have a
negative impact on system overall
quality
 Risk Assessment: collection of
adequate metrics to feed analysis
processes including the
identification of countermeasures
throughout the system engineering
process
 Model-Based Risk Assessment:
collection of metrics on (machine
readable) models (e.g., AML ones)
Introduction: Engineering of Industrial Production Systems
7
 Risk: the probability of an
occurring event which can have a
negative impact on system overall
quality
 Risk Assessment: collection of
adequate metrics to feed analysis
processes including the
identification of countermeasures
throughout the system engineering
process
 Model-Based Risk Assessment:
collection of metrics on (machine
readable) models (e.g., AML ones)
Problem Description
8
Risk management is an error prone
and cumbersome task for industrial
production systems especially when
having distributed models in different
variants and versions
 Needs for metrics for AML artifacts
 Needs for linking and versioning
support for AML artifacts
 Lack of Model-Based foundation for
risk assessment based on AML
= domain = tool
overall system design
mechanical
engineering
electrical
engineering
software
engineering
= doc
Contribution: Model-Based Measurement Process for AML
9
Jacquet et al. proposed a generic measurement process model to calculate metrics for
software engineering projects.
Contribution: Model-Based Suite for AutomationML
10
Jacquet et al. proposed a generic measurement process model to calculate metrics for
software engineering projects. We contextualized inputs/outputs for each step.
Contribution: Model-Based Measurement Process for AML
11
• Our objective
Risk assessment
 in multidisciplinary systems engineering projects
 based on AML and linked AML artefacts
 to reason on a set of distributed engineering artifacts
and their relationships
<<metamodel>>
AttributedGraph
<<metamodel>>
AMLMetamodel
<<model>>
AMLModel
<<model>>
AMLLibrary
conformsTo
Relationship
Legend
<<metamodel>>
LinkMetamodel
<<model>>
LinkModel
connects
Relationship
Contribution: Model-Based Measurement Process for AML
12
 We need models for...
1. the production system,
2. links among heterogenous, versioned artifacts
3. the metrics definitions
4. metric results
from
Model-Based Co-Evolution of Production Systems
and their Libraries with AutomationML
Berardinelli, Biffl, Maetzler, Mayerhofer, Wimmer
IEEE International Conference on Emerging Technologies
and Factory Automation (ETFA 2015)
AML Metamodel
defines the concepts
and their relationships
for production system
modeling.
Implemented using
Eclipse Ecore
previous work
Contribution: Model-Based Measurement Process for AML
13
 We need models for...
1. the production system,
2. links among heterogenous, versioned artifacts
3. the metrics definitions
4. metric results
from
Linking and Versioning Support for AutomationML:
A Model-Driven Engineering Perspective
Biffl, Maetzler, Wimmer, Lueder, Schmidt
IEEE International Conference on Industrial Informatics
(INDIN 2015)
Linking Metamodel
defines the concepts and
their relationships for
representing links among
artifacts conforming to
different metamodels.
Implemented using
Eclipse Ecore
previous work
Contribution: Model-Based Measurement Process for AML
14
 We need models for...
1. the production system,
2. links among heterogenous, versioned artifacts
3. the metrics definitions
4. metric results
from
Structured Metrics Metamodel (SMM) by OMG.
http://www.omg.org/spec/SMM/
“The SMM is a specification for
the definition of measures
and the representation of
their measurement results.
The measure definitions make
up the library of measures and
that serves to establish the
specification upon which all of
the measurements will be
based.” from
http://www.omg.org/spec/SMM/
1.0/
Implemented using Eclipse Ecore
Contribution: Model-Based Measurement Process for AML
From textual specification (not a model in MDE)…
Contribution: Model-Based Measurement Process for AML
…to SMM Model
«represents»
Contribution: Model-Based Measurement Process for AML
 Definition of the numerical assignment rules
Example Groovy Code:
Contribution: Model-Based Measurement Process for AML
 AML Models and Linking Models among different versions
Different Versions of
v1 v2
«links»
Contribution: Model-Based Measurement Process for AML
 Queries are executed on the system models
 Example: Lab-size production system result:
1. Length Metrics:
2. Max. Depth: 8
3. Max. Width: 10
4. 2378 : nodes
5. 2731 : edges
6. 1.148 : edgeToNodeRatio
7. Usage Metrics:
8. ***** Class Usage
9. 2 : FabrikModell/Bauteile/Motor
10. 2 : FabrikModell/Bauteile/Endlagenschalter
11. 1 : FabrikModell/Bauteile/Untergestell_Turntable
12. 1 : FabrikModell/Bauteile/Obergestell_Turntable
13. 1 : FabrikModell/Bauteile/Drehkranz
14. 1 : FabrikModell/Bauteile/Band_Turntable
15. ***** Role Usage
16. 2 : Modell RoleClassLib/Motor
17. 3 : Modell RoleClassLib/Sensor
18. 1 : Modell RoleClassLib/Untergestell_Turntable
19. 1 : Modell RoleClassLib/Obergestell_Turntable
20. 1 : AutomationMLBaseRole/Structure/ResourceStructure/MechanicalAssembly
21. 1 : Modell RoleClassLib/Band
22. 7 : Modell RoleClassLib/Kabel
23. ***** Interface Usage
24. 4 : ModellInterfaceClassLib/Verzahnung
25. 12 : ModellInterfaceClassLib/Verschraubung
26. 7 : ModellInterfaceClassLib/Stromanschlussbuchse
27. 3 : AutomationMLBaseInterface/ExternalDataConnector/COLLADAInterface
28. 7 : ModellInterfaceClassLib/PLCopenXMLInterface/LogicInterface
29. 6 : ModellInterfaceClassLib/COLLADAInterface
30. 4 : ModellInterfaceClassLib/Lagerung
31. 14 : ModellInterfaceClassLib/Stromanschlussstecker
Contribution: Model-Based Measurement Process for AML
 Measurement Results are part of the SMM Model
Contribution: Model-Based Measurement Process for AML
 Future work: exploitation of result, e.g., for impact analysis
 May be done with reporting tools such as Eclipse Birt
 Connector to EMF models required
http://www.eclipse.org/birt/
Conclusion
 We discussed foundations for risk assessment in multi-disciplinary
software and systems engineering projects and proposed a model-
based metrics suite for AML models and their inter-model links.
 We plan to extend the AutomationML metrics suite for several aspects
 Further metrics and queries for AML and link model artifacts
 Integrating dynamic aspects of AML model elements specifications through PLCopen
XML and state-machine like notations
 Visualization of results using Birt or graphical and textual modeling editors
22

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Model-Based Risk Assessment for Multi-Disciplinary Systems

  • 1. Model-Based Risk Assessment in Multi-Disciplinary Systems Engineering Euromicro Conference series on Software Engineering and Advanced Applications (SEAA) 2015 Arndt Lueder, Nicole SchmidtStefan Biffl, Luca Berardinelli, Emanuel Maetzler, Manuel Wimmer
  • 2. Introduction 2 Multi-Disciplinary Engineering  Multidisciplinary Domain  Mechanical Engineering  e.g., design, production, and operation of machinery (powered tools)  Electrical Engineering  e.g., design of complex power system and electronic circuits  Software Engineering  e.g., design, implementation, testing, validation of software for machinery • Heterogeneous document/tool landscape  Mechanical Engineering  Matlab, CAD tools…  Electrical Engineering  EPLAN  Software Engineering  Programming IDEs, Modeling Tools… = domain = tool = doc overall system design mechanical engineering electrical engineering software engineering
  • 3. Introduction: Industry 4.0 and its principles 3  Industry 4.0: computerization of manufacturing. Driving principles 1. Interoperability among mechatronic systems (a.k.a. cyber physical systems CPS), humans and factories 2. Virtualization: a virtual copy of the factory with sensed data 3. Decentralization: the ability of CPSs to make decisions on their own 4. Real-Time Capability: monitoring, analysis, planning, execution 5. Service Orientation: OPC Unified Architecture (SOA) 6. Modularity: flexible adaptation to changing requirements = domain = tool = doc overall system design mechanical engineering electrical engineering software engineering Industry 4.0
  • 4. Introduction: Engineering of Industrial Production Systems 4  AutomationML (AML) standard for tool data exchange  AML docs are XML-based artifacts  AML as pivotal language: Tool- specific docs can be transformed in AML docs overall system design mechanical engineering electrical engineering software engineering Industry 4.0 = domain = tool = doc XML-based artifacts CAEX.xsd
  • 5. Introduction: Engineering of Industrial Production Systems 5 Mechanical/Eletrical/Software Components Library «cloned» Production System Model «represents» Lab-sized Production System “Equipment Center for Distributed Systems,” http://www.iafbg.ovgu.de/en/technische ausstattung cvs.html, Institute of Ergonomics, Manufacturing Systems and Automation at Otto-v.-Guericke University Magdeburg.
  • 6. Introduction: Engineering of Industrial Production Systems 6  Risk: the probability of an occurring event which can have a negative impact on system overall quality  Risk Assessment: collection of adequate metrics to feed analysis processes including the identification of countermeasures throughout the system engineering process  Model-Based Risk Assessment: collection of metrics on (machine readable) models (e.g., AML ones)
  • 7. Introduction: Engineering of Industrial Production Systems 7  Risk: the probability of an occurring event which can have a negative impact on system overall quality  Risk Assessment: collection of adequate metrics to feed analysis processes including the identification of countermeasures throughout the system engineering process  Model-Based Risk Assessment: collection of metrics on (machine readable) models (e.g., AML ones)
  • 8. Problem Description 8 Risk management is an error prone and cumbersome task for industrial production systems especially when having distributed models in different variants and versions  Needs for metrics for AML artifacts  Needs for linking and versioning support for AML artifacts  Lack of Model-Based foundation for risk assessment based on AML = domain = tool overall system design mechanical engineering electrical engineering software engineering = doc
  • 9. Contribution: Model-Based Measurement Process for AML 9 Jacquet et al. proposed a generic measurement process model to calculate metrics for software engineering projects.
  • 10. Contribution: Model-Based Suite for AutomationML 10 Jacquet et al. proposed a generic measurement process model to calculate metrics for software engineering projects. We contextualized inputs/outputs for each step.
  • 11. Contribution: Model-Based Measurement Process for AML 11 • Our objective Risk assessment  in multidisciplinary systems engineering projects  based on AML and linked AML artefacts  to reason on a set of distributed engineering artifacts and their relationships <<metamodel>> AttributedGraph <<metamodel>> AMLMetamodel <<model>> AMLModel <<model>> AMLLibrary conformsTo Relationship Legend <<metamodel>> LinkMetamodel <<model>> LinkModel connects Relationship
  • 12. Contribution: Model-Based Measurement Process for AML 12  We need models for... 1. the production system, 2. links among heterogenous, versioned artifacts 3. the metrics definitions 4. metric results from Model-Based Co-Evolution of Production Systems and their Libraries with AutomationML Berardinelli, Biffl, Maetzler, Mayerhofer, Wimmer IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2015) AML Metamodel defines the concepts and their relationships for production system modeling. Implemented using Eclipse Ecore previous work
  • 13. Contribution: Model-Based Measurement Process for AML 13  We need models for... 1. the production system, 2. links among heterogenous, versioned artifacts 3. the metrics definitions 4. metric results from Linking and Versioning Support for AutomationML: A Model-Driven Engineering Perspective Biffl, Maetzler, Wimmer, Lueder, Schmidt IEEE International Conference on Industrial Informatics (INDIN 2015) Linking Metamodel defines the concepts and their relationships for representing links among artifacts conforming to different metamodels. Implemented using Eclipse Ecore previous work
  • 14. Contribution: Model-Based Measurement Process for AML 14  We need models for... 1. the production system, 2. links among heterogenous, versioned artifacts 3. the metrics definitions 4. metric results from Structured Metrics Metamodel (SMM) by OMG. http://www.omg.org/spec/SMM/ “The SMM is a specification for the definition of measures and the representation of their measurement results. The measure definitions make up the library of measures and that serves to establish the specification upon which all of the measurements will be based.” from http://www.omg.org/spec/SMM/ 1.0/ Implemented using Eclipse Ecore
  • 15. Contribution: Model-Based Measurement Process for AML From textual specification (not a model in MDE)…
  • 16. Contribution: Model-Based Measurement Process for AML …to SMM Model «represents»
  • 17. Contribution: Model-Based Measurement Process for AML  Definition of the numerical assignment rules Example Groovy Code:
  • 18. Contribution: Model-Based Measurement Process for AML  AML Models and Linking Models among different versions Different Versions of v1 v2 «links»
  • 19. Contribution: Model-Based Measurement Process for AML  Queries are executed on the system models  Example: Lab-size production system result: 1. Length Metrics: 2. Max. Depth: 8 3. Max. Width: 10 4. 2378 : nodes 5. 2731 : edges 6. 1.148 : edgeToNodeRatio 7. Usage Metrics: 8. ***** Class Usage 9. 2 : FabrikModell/Bauteile/Motor 10. 2 : FabrikModell/Bauteile/Endlagenschalter 11. 1 : FabrikModell/Bauteile/Untergestell_Turntable 12. 1 : FabrikModell/Bauteile/Obergestell_Turntable 13. 1 : FabrikModell/Bauteile/Drehkranz 14. 1 : FabrikModell/Bauteile/Band_Turntable 15. ***** Role Usage 16. 2 : Modell RoleClassLib/Motor 17. 3 : Modell RoleClassLib/Sensor 18. 1 : Modell RoleClassLib/Untergestell_Turntable 19. 1 : Modell RoleClassLib/Obergestell_Turntable 20. 1 : AutomationMLBaseRole/Structure/ResourceStructure/MechanicalAssembly 21. 1 : Modell RoleClassLib/Band 22. 7 : Modell RoleClassLib/Kabel 23. ***** Interface Usage 24. 4 : ModellInterfaceClassLib/Verzahnung 25. 12 : ModellInterfaceClassLib/Verschraubung 26. 7 : ModellInterfaceClassLib/Stromanschlussbuchse 27. 3 : AutomationMLBaseInterface/ExternalDataConnector/COLLADAInterface 28. 7 : ModellInterfaceClassLib/PLCopenXMLInterface/LogicInterface 29. 6 : ModellInterfaceClassLib/COLLADAInterface 30. 4 : ModellInterfaceClassLib/Lagerung 31. 14 : ModellInterfaceClassLib/Stromanschlussstecker
  • 20. Contribution: Model-Based Measurement Process for AML  Measurement Results are part of the SMM Model
  • 21. Contribution: Model-Based Measurement Process for AML  Future work: exploitation of result, e.g., for impact analysis  May be done with reporting tools such as Eclipse Birt  Connector to EMF models required http://www.eclipse.org/birt/
  • 22. Conclusion  We discussed foundations for risk assessment in multi-disciplinary software and systems engineering projects and proposed a model- based metrics suite for AML models and their inter-model links.  We plan to extend the AutomationML metrics suite for several aspects  Further metrics and queries for AML and link model artifacts  Integrating dynamic aspects of AML model elements specifications through PLCopen XML and state-machine like notations  Visualization of results using Birt or graphical and textual modeling editors 22