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Grischa Liebel
grischa@chalmers.se
(grischa.liebel@gmail.com)
Modelling your Requirements for
Communication, Coordination
(and Analytics)

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Agenda
• What?
• Why?
• Connection to the WS?

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• Lack of Interdisciplinary Understanding
• Insufficient Communication and Feedback Channels
• Unclear Responsibilities and Borders
(Liebel et al., “Organisation and communication problems in automotive
requirements engineering”, Requirements Engineering 23 (1), 145–167, 2018)
RE Problems

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• Use model-based systems engineering data (traces and person
information) to create and visualise coordination networks.
“Use the project memory” – Herbsleb
Idea
Req 1
(Peter)
Issue X
(Lisa)
HW Design
(Alice)
Issue Y
(Alice)
Peter
Lisa
Alice2
1
1
We are not modelling the
requirements
-
we interpret existing
requirements as a model!

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• Not restricted to Software Engineering
• Clarification, no elicitation
(i.e., mainly company internal)
• People authoring reqs/design have the knowledge
“An engineer is trying to find experts for clarification of requirements or design”
Main Use Case

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• Design science, three cycles
• At Volvo Trucks
• Mainly with one tool (SystemWeaver)
• Proposal for improvements wrt. interoperability, design
• 15 interviews, several focus groups, survey
• Concerns:
• Possible?
• Useful?
• General?
Method
• Information management solution
• Everything in a single model
• Flexible meta model
• Entities and relationships

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• Yes!
• Social data needs to exist and be up-to-date
• (Pre-GDPR)
Possible?
Systems Engineering
Models
(e.g. Requirements, Design)
Social Models

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• Practitioners see potential:
• For “new” employees
• For spotting isolated nodes (people)
• For additional use cases (e.g., team composition)
• Awareness!
• Ethical issues
• Blame
• Performance evaluation
Useful?

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• Implemented adapters to the OSLC standard
• Tool interoperability for a variety of domains
(e.g., requirements mgmt, change mgmt)
• Evaluated based on IBM Jazz data and an industrial survey
• (Customised version of Gephi)
• (Open source on GitHub)
Follow-up study
General?

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• Early communication regarding requirements
(vs. development-specific discussions)
• Little data (requirements)
(vs. lots of data (code, tests))
• Mirror of the actual data (there is value in "wrong" nodes)
(vs. abstraction)
• Multi-disciplinary approach
(vs. single-discipline tool)
Why?

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Connection to the Workshop?
• ”Big Software”
• Lots of stakeholders
• Across organisations
• Across interfaces/systems
• Statistical methods, AI, ML, etc., are needed to handle large
amounts of data
• But: How about models on a high level of abstraction?
• Requirements level
• Interfaces between organisations
• Inform human decisions

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Req 1
(Peter)
Issue X
(Lisa)
HW Design
(Alice)
Issue Y
(Alice)
Peter
Lisa
Alice2
1
1
- Useful for several use
cases
- Data quality is an issue
- Potential ethical issues
Collaboration?
grischa.liebel@gmail.com