Presentation adapted from the ProSTEP symposium to present the concept and advances in the digitalization of the lifecyle with focus on task automation and reuse.

1. Sailing the V:
Engineering digitalization through task
automation and reuse in the development
lifecycle
Jose María Alvarez & Juan Llorens | UC3M & TRC | {josemaria.alvarez, llorens}@uc3m.es

2. Introduction
The lifecycle

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Lifecycle management: the Future of Systems Engineering
Source: https://www.researchgate.net/publication/340649785_AI4SE_and_SE4AI_A_Research_Roadmap

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Mats Berglund (Ericsson)
http://www.ices.kth.se/upload/events/13/84404189f85d41a6a7d1cafd0d
b4ee80.pdf
Engineering (and corporate) environment
Lifecycle processes
ISO 15288:2015
Digitalization of the lifecycle: Internet of Tools
Source: https://www.nist.gov/system/files/documents/2019/04/05/14_delp.pdf

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Source: Boeing
Sailing the V: engineering digitalization
Lifecycle evolution

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Potential needs to digitalize the V
Automation
Requirement identification and generation
Model population
Documentation and compliance
Traceability
Recovery traces
Consistency checking
Management
MBSE
Integration and exchange
Link logical (descriptive) →physical (analytical)
Reuse
Simulation
Configuration
Orchestration
Link
V&V
Quality (CCC)
Information sharing with providers
Configuration Management
Evolution and information sharing

7. The approach
Knowledge-Centric
Systems Engineering

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Concept: a knowledge management strategy

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Sailing V: defining the ground truth
01 Controlled Organizational and
Project Vocabulary for a common
understanding among stakeholders
Vocabulary / Terminology
02 Relate the terms in different
way representing semantic
relationships:
- Relationships between terms
(Thesaurus)
- Clusters of Terms
Terms Relationships
04 Information about how can
the text being matched by
the patterns be represented
using graphs
Formalization
03 Represent text structures in a
way it is possible to do Pattern
Matching within the text
Textual Patterns
05 A combination of rules,
tasks and groups to infer
information from existing
text
Reasoning Info

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E.g. Support smart artifact authoring (requirements)

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Sailing the V: domain artifacts management (hub & gateway) and exploitation
Input
artifact/operation
(and tool)
Tool j
Transformation
rules
System
Knowledge
Base
SRL
(engineering
knowledge graph)
Linking: data, information &
knowledge
Text
SysML
Modelica
Simulink
…
Transformation
rules
Text
SysML
Modelica
Simulink
…
System
Knowledge
Base
Tool k
System Assets
Store
(Knowledge graph)
Output
artifact/operation
(and tool)

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TRC ecosystem: capabilities and tools within the H2020-AHTOOLs project

13. User stories
5 user stories in Action

14. “That's one small step for a man, one giant leap for engineering”
Requirements
Engineering
As requirements engineer
I want to identify and
extract requirements
from legacy documents.
So that I can automate
requirements population.
MBSE &
Requirements
As domain engineer
I want to populate models
from requirements.
So that I can keep
consistency over time and
make my system artifacts
executable.
Keep data links alive and
consistent.
Quality: V&V
As domain engineer
I want to check quality of
my system artifacts:
models, requirements, etc.
So that I can ensure high-
quality artifacts from
scratch reaching the CCC
objectives.
Reuse
As domain engineer
I want to exchange
information between
tools, find similar system
artifacts (e.g. models)
and recover traces.
So that I can reuse
existing knowledge
embedded in system
artifacts.
Digitalization of Engineering
As systems engineer
I want to have a human friendly
environment for the engineering
process.
So that I can share all information
and data with my colleagues in
different disciplines.

15. Identify and extract requirements from legacy documents
VIDEO-1

16. Model generation and exploitation
VIDEO-2, VIDEO-2B, VIDEO-2C, VIDEO-2D, VIDEO-2E

17. Quality: V & V
VIDEO-3, VIDEO-3B & VIDEO-3C

18. Reuse: finding models and recovering traces
VIDEO-4

19. Integration of system artifacts & document generation
VIDEO-5

20. Closing the stage
Conclusions
&
Future Directions

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Collaborative engineering: unleashing data & knowledge
Formal
ontologies
Main use:
• To create a knowledge base of the system:
knowledge creation (collaborative)
• To perform reasoning processes for
knowledge inference
How to use:
• Local and/or distributed reasoning
• Not all ontologies are formal ontologies
Warning:
• Do NOT use ontologies to perform data
validation (consistency checking,
etc.)→time consuming process
• Make ontologies “runnable” not just a
document
• Avoid transformations from different
paradigms but boost cooperation
between paradigms
• e.g. SysMLTransformation or
cooperation?→OWL
Data
Shapes
Main use:
• Data representation, exchange and
consistency.
• Lightweight semantics→”The Shape”
How to use:
• Data as a Service: create standard-based
APIs (technology is NOT relevant,
FOUNDATIONS ARE)
• OSLC
• Swagger (Open API Specification)
• REST architectural style (JSON format)
Warning:
• Define your URIs and methods properly
• Expose both: data and operations
• Document the use of the API
→Swagger a good example

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Technology: main applications of the presented approach
• “Shared database”
• Common data model (representation)
• Federated data & knowledge
• Query language
• Logical view (graph) vs Physical view (?)
• Ready for providing functionalities (e.g.
quality, traceability, etc.)
Technology as a Data hub
Process integration
• Connection & access to system artifacts
• Common data model (representation)
• Transformation
• Round-trip between tools
• No indexing, storage, etc.→gateway
• Not only exchange data but functionalities
on top of data
• Consume functionalities provided by tools to
integrate results
• Provide new functionalities having a data
hub
Functionality as a Service
Technology as a Data gateway
• “Message bus, broker etc.”, “Hub-Spoke”
• Collaboration between tools to implement a
more complex process
• Communication and orchestration
architecture
• Orchestration (e.g. simulation, verification,
etc.)

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Interoperability as a key enabler of the lifecycle management

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Conclusions and Future work
Focus on data integration,
semantics, AI/ML
-Understanding of the knowledge
embedded in the system artifacts
FUSE
Automate
Trace
Models
Simulation
&
Quality
Key
Enablers
Focus on innovation
-Avoid manual tasks
-SMART tools for engineers
Focus on linking (knowledge graph)
-Recover
-Manage
-Exploit
Focus on integration
-Model management & population
-Model exchange & execution
-Link different types of models
-SysML V2 API implementation
Focus on reuse and continuous
quality:
-Link simulations (SysPHS and SSP)
-Ensure quality over time
-Reuse system artifacts
-Standardization (interoperability)
-Configuration Management
-Tools and APIs (e.g. OpenAPI)
-Enhanced engineering methods:
AI/ML

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Acknowledgements
The research leading to these results has received funding from the H2020-ECSEL Joint Undertaking (JU) under grant agreement No 826452-
“Arrowhead Tools for Engineering of Digitalisation Solutions” and from specific national programs and/or funding authorities.
Learn more: https://www.amass-ecsel.eu/

26. Thank you for
your attention!
Jose María Álvarez-Rodríguez
Josemaria.alvarez@uc3m.es
@chema_ar
Take a seat and
comment with us!
Juan Llorens
llorens@inf.uc3m.es
https://www.reusecompany.com/ http://www.kr.inf.uc3m.es/