1. Support for Managing Design-Time Decisions
By: Dan Garubba
Based on a Paper by Alexander Egyed and David S. Wile

2. Scenario You are at a party and everyone wants to eat pizza.
However, not everyone wants the same kind of pizza. Some want different toppings; some want different kinds of cuts.
The configuration of the pizzas must be determined before placing and order.
Any changes after placing the order requires further pizzas to be ordered

3. Topics Formalizing Constraints, Ambiguities, and Design Choices
Algorithmically Eliminating Infeasible Choices
Computational Complexity

4. Formalizing Design Choices, Ambiguities, Constraints
CSP: Constraint Satisfaction Problem
CST: CSP Solution Techniques
Ambiguity: a variable
Choice: elements that determine the domain of an ambiguity
Assignment = Ambiguity -> Choice
Constraint: Assignment -> Boolean

5. Focus of CSP example
Ambiguities
Choices

6. Evaluate constraint 2 based on the Constraint Chart

7. The Algorithm Eliminate Choices, One Constraint at a time
Determine Mandatory Ambiguities
Promoting Optional Ambiguities to Mandatory Ambiguities

8. Choice Elimination by Explicit Constraints
Collect All Positive Assignments
Model Profiling: Create assignments from ambiguities which may produce more assignments

9. Mandatory Ambiguities
The ambiguities that are that are referenced in every positive assignment
Removing invalid choices:

10. Optional Ambiguities
Ambiguities that are referenced in some, but not all positive assignments
Conditionally dependent on other ambiguities

11. Promoting Optional Ambiguities to Mandatory Ambiguities
Optional ambiguities maybe promoted to mandatory, which makes them eliminatable
Constraint Propagation: If a choice is eliminated, it should be removed in all positive assignments of all constraints.

12. Feedback Loop

13. Computational Complexity
Positive Assignments: O(C# * Csize * ChPAAPC)
Choice Elimination: O(CPA * (ChPAAPC-1 + APC * ChPA))
Though it appears that the order is high, Csize, CPA, and APC are small, even in big models.
Further more, tests on small models reveal that optimal (most choices eliminated), and consistency verified solutions were verified.

14. Real Life Give an example or real life anecdote
Sympathize with the audience’s situation if appropriate

15. Conclusion
Automated tools cannot derive the best design, that is up to the designer
However the authors guarantee:
No valid choice is ever eliminated from the designer’s consideration
Now choice reported to the designer as inconsistent could possibly be consistent with the decisions made to that point
Determining what choices should be removed is near optimal
The algorithm is computationally scalable
The algorithms guarantees all of the above properties despite the fact that it is not able to evaluate all of the constraints.

16. Source
Support for Managing Design Time Decisions, Egyed & Wile. IEEE Transactions on Sovtare Engineering, Vol32., No. 5, May <