By the time you reach Module 6, the project has progressed from high-level vision → detailed use cases → refined relationships → structured specifications → rich behavioral (Activity & Sequence), structural (Class), and data (ERD) models. At this point, the models are comprehensive and visually expressive—but they still live in the world of design intent. The critical question now becomes:
“Does the system actually work correctly under all realistic combinations of conditions, inputs, and events?”
This is the purpose of Scenario Analysis and Logic Validation: systematically breaking down complex use cases into concrete, testable scenarios; mapping every meaningful combination of conditions and decisions; and verifying that the specified behavior, edge cases, exceptions, and business rules are complete, consistent, and free of logical gaps or contradictions.
This module focuses on two tightly integrated techniques:
- Scenario Breakdown — Converting narrative flows (especially those with multiple decisions, loops, or alternatives) into a clear set of distinct, named scenarios that cover happy paths, variations, and error conditions.
- Decision Matrix / Decision Table Analysis — Creating tabular representations that systematically enumerate all unique combinations of conditions (inputs, states, guards) against the corresponding actions/outcomes, ensuring 100% coverage of decision logic.
These activities are not optional polish—they are essential quality gates. They expose hidden assumptions, missing cases, inconsistent rules, and ambiguous specifications before implementation begins, dramatically reducing costly rework during development and testing.
Visual Paradigm’s AI-Powered Use Case Modeling Studio supports this phase by:
- Automatically suggesting key decision points and branches from Activity & Sequence Diagrams
- Generating initial scenario lists and decision tables from use case flows
- Highlighting uncovered combinations or contradictory outcomes
- Allowing you to refine tables interactively (add conditions, merge equivalent rules, annotate business rationale)
- Exporting decision tables as test-case foundations (directly feeding into Module 7)
Practical Examples
Example 1: GourmetReserve – Use Case: Book a Table
Key Decision Points Identified by AI:
- Is the diner authenticated?
- Are tables available for the requested time & party size?
- Is a deposit required (party size, time slot, loyalty status)?
- Does the diner provide a valid promo code?
- Does payment succeed?
AI-Suggested Scenario Breakdown (partial list):
- Happy path – authenticated, tables available, deposit waived (Gold loyalty), no promo, payment succeeds → Reservation confirmed
- Happy path with promo – valid code applied → Reduced deposit
- No tables available → Offered waitlist → Diner joins waitlist
- Deposit required but payment fails (card declined) → Booking aborted, error shown
- Late cancellation attempt (within 2 hours) → No-show fee applied on cancellation
Decision Table Generated by AI (simplified):
| Scenario | Authenticated? | Tables Available? | Deposit Required? | Valid Promo? | Payment Succeeds? | Outcome |
|---|---|---|---|---|---|---|
| 1 | Yes | Yes | No | No | — | Confirmed (no deposit) |
| 2 | Yes | Yes | Yes | Yes | Yes | Confirmed (discounted deposit) |
| 3 | Yes | No | — | — | — | Waitlist offered |
| 4 | Yes | Yes | Yes | No | No | Error: Payment failed |
| 5 | No | — | — | — | — | Redirect to login |
Your refinement:
- Add column: “Peak hours (Fri/Sat 7–9 pm)?” → Influences deposit requirement
- Add rule: If party size ≥ 12 → Manual approval required (new outcome: “Pending manager review”)
Example 2: SecureATM – Use Case: Withdraw Cash
Decision Table (AI-generated excerpt):
| Scenario | Authenticated? | Amount ≤ Daily Limit? | Sufficient Funds? | Sufficient Cash in ATM? | Outcome |
|---|---|---|---|---|---|
| 1 | Yes | Yes | Yes | Yes | Cash dispensed, balance updated |
| 2 | Yes | No | Yes | Yes | Error: Exceeds daily limit |
| 3 | Yes | Yes | No | Yes | Error: Insufficient funds |
| 4 | Yes | Yes | Yes | No | Error: ATM temporarily unavailable |
| 5 | No | — | — | — | Prompt for PIN/card retry |
Refinement added:
- New column: “High-value transaction (> $1,000)?” → Triggers biometric check (extra condition)
- Outcome variant: Biometric fails → Transaction aborted, card retained, fraud alert sent
Example 3: CorpLearn – Use Case: Take Final Assessment
Decision Table:
| Scenario | All modules completed? | Time not expired? | Compliance acknowledgments done? | Score ≥ 80%? | Retakes remaining? | Final Outcome |
|---|---|---|---|---|---|---|
| 1 | Yes | Yes | Yes | Yes | — | Passed → Certificate issued |
| 2 | Yes | Yes | Yes | No | Yes | Failed → Retake offered |
| 3 | Yes | Yes | Yes | No | No | Failed → Course marked incomplete |
| 4 | Yes | No | — | — | — | Auto-submitted → Score calculated |
| 5 | No | — | — | — | — | Cannot start assessment |
Your refinement:
- Add condition: “Mandatory data privacy question answered correctly?” → If No → Auto-fail regardless of score
- Merge equivalent rows where possible to keep table compact
Why This Step Is Critical (Even with AI Models)
AI-generated diagrams and specifications are excellent at following patterns, but they can miss:
- Domain-specific combinations (e.g., regulatory thresholds)
- Rare but catastrophic edge cases
- Contradictory business rules hidden across different flows
- Performance or concurrency implications of certain paths
Scenario analysis and decision tables force explicit enumeration—turning implicit assumptions into visible, reviewable logic. They directly become the foundation for comprehensive test cases (Module 7) and provide strong evidence of due diligence for audits or compliance reviews.
By the end of Module 6, you will have:
- A curated set of named, prioritized scenarios covering normal, alternative, and exceptional behavior
- Decision tables that prove complete coverage of every meaningful condition combination
- Early detection (and resolution) of logical gaps, ambiguities, or inconsistencies
This rigorous validation step ensures that the system doesn’t just look good on paper—it actually behaves correctly when subjected to real-world variability. With logic fully validated, you’re ready to generate high-quality, traceable test cases in the final quality assurance phase.