1. “Models Are a Thinking Tool, Not a Rule”
Jim keeps repeating this key point:
Models aren’t paperwork to satisfy a process.
They’re thinking tools that help you:
Have better conversations,
Spot missing scenarios,
And design better experiments.
As you watch, keep asking:
“If I drew this for my system, what would I suddenly see that I’m missing today?”
This mindset is crucial when you’re handing context to AI tools or trying to generate tests automatically—good mental models make everything else sharper.
2. Functional Flow Diagrams (FFDs): Mapping User Paths
Jim’s first traditional model: the functional flow diagram.
What it gives you:
Left-to-right flow of functionality.
Decision points (do this / do that).
End-to-end paths, plus pieces that can be tested in isolation.
Why testers should care:
Great for discovering happy path vs alternate path scenarios.
Helps you see which steps can be:
UI tests,
API-level tests,
or even unit-level checks.
Direct input into your automation framework structure (which actions you need as reusable functions/page methods).
As you watch, think about one system you own and how an FFD would immediately clarify “What are the real flows we need to automate?”
3. Data Flow Diagrams (DFDs): Testing Data, Not Just Screens
Next model: data flow diagrams—how data moves and transforms.
What you get:
Where data is created, validated, enriched, or written.
The states of the data as it moves (e.g., “raw transaction → validated → paycheck generated”).
Automation impact:
Drives test data design:
Which values truly matter?
Where can you inject test data?
Which transformations need verification?
Lets you plan valid vs invalid data scenarios logically instead of guessing.
This is gold for testing APIs, ETL, event-driven systems, and AI pipelines where data shape/quality is everything.
4. State Transition Diagrams: Finite State Machines for Real
Jim’s state-transition diagrams (finite state machines) focus on:
States (e.g., Draft, Submitted, Approved, Rejected)
Events/actions that move you between states
All the paths: primary and alternative
Why this matters:
Perfect for designing scenario-based tests and model-based testing (MBT).
Makes it obvious where:
Illegal transitions should be blocked,
Edge cases live,
And where AI or agents might get confused.
Even if you never build a full MBT framework, having a state model keeps you from missing critical transitions in your automation suite.
5. System Architecture Diagrams: Choosing the Right Tool & Layer
Jim then zooms out to system architecture:
Front-end (web/mobile/desktop)
Middle tier (web/app servers)
Back-end (databases, services)
Underlying technologies (Apache vs IIS, .NET vs Java, Oracle vs SQL Server, etc.)
What to listen for:
He uses architecture diagrams to decide:
Which layer to target (UI, API, DB, messaging),
Which tools to use at each layer,
And where performance testing or service-level tests should live.
In an AI and microservices world, this is how you avoid “all UI, all the time” and instead build a layered, maintainable automation strategy.
6. Story Mapping & Mind Maps: Usage & Decomposition
Jim’s “modern models” start with two very approachable tools:
Story Mapping / Storyboarding
Maps features by usage frequency and criticality.
Helps you see:
“What do users do most often?”
“What flows are business-critical?”
Mind Maps
Decompose features into sub-features and data variations.
Great for:
Visualizing edge cases,
Listing test ideas,
Identifying data dimensions (e.g., one vs many attachments, file types, limits).
What’s in it for you:
Clear input for risk-based testing and automation priority.
Easy to use in a workshop with devs, PMs, and testers (even non-technical folks can follow).
Ideal pre-work before asking an AI assistant to “generate tests for X”—you’re giving it a structured view, not just a vague requirement.
7. UCML, VTA & Behavior-Based Testing: Test Design with Depth
Jim covers three advanced but very practical modeling methods:
a) UCML (User Community Modeling Language)
Originally for performance testing, but:
Maps user types, usage percentages, and paths.
Perfect for:
Deciding which flows to automate first.
Applying the 80/20 rule: automate the 20% of flows that get 80% of real usage.
b) Visual Task Analysis (VTA)
Created by Richard Bradshaw & Mark Winteringham.
Visualizes the tech stack layers and where each process lives:
UI, API, services, database, etc.
Shows how many “checkpoints” exist below the UI.
Why it’s powerful:
Helps you choose which layer to test at (and which tool to use).
Avoids over-testing at the UI layer when a cheaper API or component test would do.
c) Behavior-Based Testing (BBT)
Created by Tim Harrison.
A visual way to design BDD-style tests:
Map contexts, events, and outcomes first,
Then derive Gherkin (Given/When/Then) steps.
Encourages:
Reusable step “snippets,”
Data-driven combinations.
This keeps your BDD suite from turning into hundreds of almost-identical, brittle scenarios.
8. How These Models Help Your Automation (and AI) Strategy
Throughout the talk, Jim keeps tying modeling back to automation:
Models help you:
Decide what’s worth automating (and at which layer),
Design modular frameworks aligned with flows and data,
Identify reusable building blocks (actions, components, helpers),
Prioritize based on usage and risk, not gut feel.
In the age of AI:
These models become inputs and constraints for AI agents/tools:
Use them as context for AI-generated test suites.
Use UCML/story maps to steer AI toward the most impactful scenarios.
Use state diagrams and DFDs to define “allowed behavior” and detect anomalies.
If You Only Take Away Three Things…
Draw before you code. A simple model (flow, data, state, workload) will make your automation more focused, modular, and maintainable.
Not all tests belong at the UI. Architecture and VTA-style models help you pick the right layer and tool for each check.
Usage and risk should drive automation priorities. Story maps, UCML, and mind maps give you a concrete way to choose what to automate first—and what to leave manual or to AI/agents.
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