1. Ebrahim Bagheri E.Bagheri@unb.ca
The Integration of Para-consistent Conceptual Models Influenced by Uncertainty: A Belief-theoretic Approach
Ebrahim Bagheri
Supervisor: Dr. Ali A. Ghorbani
11/22/2018

2. Agenda Introduction Background and Related Work Challenges Motivations
Proposed Model
Contributions to Knowledge
Evaluation Methods
Thesis Timeline
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3. Introduction Software development is a problem solving process
The dimensions of the problem are:
Context
Environment
Situation
Users
etc.
Solving a problem requires suitable tools!
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4. Introduction (cntd.) Conceptual models are those tools
Conceptual modeling is also known as problem analysis, or conceptualization
Useful for the formalization of experts understanding of a problem
Also useful for the systematic classification of knowledge and procedures
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5. Why Conceptualize? Make real-world concepts tangible
Support communication and collaboration
Detect missing information, errors, or even misinterpretations
Specify software behavior
Provide an orientation on software performance
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6. The Process Early Late Problem Domain Conceptualization Product
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7. Famous Models Software Engineering: Knowledge Engineering:
Classical: Telos, DFD, SADT, ERD, …
Advanced: UML, EM, KAOS, i*, …
Knowledge Engineering:
Model, CML, KARL, CommonKADS
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8. Viewpoint-Based Models
Different areas and amount of knowledge helps better analyze a problem
Employing more information sources for getting a better insight
High complexity of a software system requires collaborative design effort
Sources of information (participants) are known as viewpoints
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9. 11/22/2018

10. Related Work
Viewpoint-Oriented Requirement Definition (VORD – Preview):
Employ any notation
Formal definition of Viewpoint:
viewpoint name
viewpoint focus (boundary and scope)
viewpoint concern (e.g. organizational goals, business objectives, etc.)
viewpoint information sources
viewpoint requirement definitions
viewpoint activity history
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11. Related Work (cntd.) Nuseibeh et al. define a viewpoint as:
Representation style
Domain of interest (area of concern)
Requirement specification
Work plan (requirement engineering strategy)
A work record
Inflexible due to strict declaration of:
Work plan
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12. Related Work (cntd.) Controlled Requirement Expression (CORE):
divide the problem domain into disjoint areas of concern
Consistent and complete model when sub-models are merged into a single model
How can we clearly split the problem domain?
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13. Challenges to VP-based Models
Defining a unique representation style
Use of common terminology (vocabulary)
Identifying specification overlaps
Detecting model discrepancies
Merging different models
Evaluating the final product
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14. Challenges (cntd.) Humans make conception errors due to:
Risk aversion
Short term memory
Perceptual problems
Epistemic uncertainty (aka partial ignorance)
Not all information sources are equally reliable
Different problem statement strategies
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15. Current Solutions Shared ontology: Human expert inspection:
Common application vocabulary
Thesauri
Human expert inspection:
Conflict forms
Evaluating conflict metrics
Category theory
Belnap’s knowledge order
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16. Current Solutions (cntd.)
Formal Methods
Convert viewpoint specifications into
VWPI and use static analyzer
First-order logic and use backward and forward chaining
Goal regression in KAOS
Incrementally elicited ranked structures
Make use of epistemic states
Apply preference orderings
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17. Motivations Lack of attention to the following issues:
Need to capture uncertainty
Unify model integration for different CM schemas
Address information source reliability
Real-time model development, negotiation, and integration
Evaluation of integration effectiveness
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18. Uncertainty Dempster-Shafer theory of evidence:
A tool for numerically quantifying and reasoning under uncertainty
An extension to Probability theory where power set elements receive probability (belief) mass
A rich area of research with tools for
Belief function combination (e.g. Dempster’s rule of combination)
Belief propagation
Belief entropy measures (e.g. generalized entropy)
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19. Proposed Model Conceptual Modeling Layer Underlying Construct Layer
Develop Individual
Conceptual Models
(Use Linguistic Terms)
Calculate Integration Efficiency
Conceptual Model Integration
Conceptual Modeling Layer
Proposed Model
Convert Model to the Underlying Construct Format
Integrated Construct Instance Base
Underlying Construct Layer
Process Viewpoint Beliefs (Opinions)
Calculate Pre-Consensus Belief Recommendations
Belief Merge
Calculate Belief Mass Merge Effectiveness
Recommend Belief Mass for Consensus Formation
Viewpoint Reliability Assessment
Belief Layer
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20. Schematic View Discount Discount Viewpoint Viewpoint Discount
Update
Update
Shared
Understanding
(Consensus)
Viewpoint
Viewpoint
Update
Discount
Viewpoint
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21. Sample Process Mary Bob John Car Road Parking FA/AC SA/SC Tire Door
Brand
FA/SC
EW/SC
Bob
SA/SC: (0.5, 0.15, 0.35)
Slight Agreement/Slightly Certain
FA/AC: (0.85, 0.05, 0.1)
Firm Agreement/Absolutely Certain
EW/SC: (0.33, 0.33, 0.33)
Either Way/Slightly Certain
FA/SC: (0.65, 0.05, 0.3)
Firm Agreement/Slightly Certain
Sample Process
11/22/2018

22. Belief Space (Subjective Logic)
Uncertainty 1
Uncertain
Highly
Uncertain
Very
Slightly
Certain
Highly
Certain 1 1
Belief
Firm
Disagreement
Disbelief
Slight
Disagreement
Either Way
Slight
Agreement
Firm
Agreement
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23. Sample Process (ctnd.) Car Tire Door Brand Road Parking SA/VU EW/AU
Integrated View
(Before Pruning)
Car
Tire
Door
Brand
Road
Parking
SA/VU
EW/AU
SA/SC
FA/SC
Integrated View
(After Pruning)
Car
Tire
Door
Road
Parking
Brand
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24. Contributions to Knowledge
Employing linguistic terms
Addressing uncertainty
Creating an underlying modeling construct
Consensus building and negotiation
Pre-consensus belief recommendation
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25. Contributions to Knowledge (cntd.)
Merging conceptual models
Evaluating merging effectiveness
Information source reliability assessment
Tool support for the proposed model
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26. Evaluation Methods Formally prove the correctness of the:
Merge operators
Belief recommendation algorithms
Analyze the behavior of the operators under extreme conditions
Conduct several case studies using
A number of Computer Science graduate students
A tool that will support the proposed solution
The preliminary tool is being developed in Eclipse (EMF)
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27. Thank you 
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28. Goal regression
The idea of goal regression is linked to planning via plan modification...i.e. in order to achieve P and Q, construct a plan F that achieves P, and then modify F so that it achieves Q while still achieving P. The idea is to protect P so that the choice of where to place the steps for achieving Q is determined relative to the plan for P.
11/22/2018