Good architecture isn’t about patterns, frameworks, or layered diagrams. It’s about how well your system absorbs change and how clearly it expresses its core rules.
Two signals reveal this better than anything else: testability and information flow.
Testability exposes design quality
Testability isn’t a “testing” problem — it’s a design problem.
When a piece of code is hard to test, that’s your architecture showing friction. Something depends on something it shouldn’t.
- If you need to set up databases, APIs, or ten mocks just to test a single class, your boundaries are misplaced.
- If you can test logic with no external setup, dependencies are likely pointing in the right direction.
- The easier it is to test something in isolation, the cleaner the design.
Think of tests as the quickest design feedback loop. When you struggle to write a small, focused test, that’s a sign your module doesn’t actually have clear input/output boundaries.
If a rule can’t be tested without involving infrastructure, the rule is in the wrong place.
Information flow shows where your system’s control lives
The direction of information in your system determines who’s in charge — the business logic or the tools.
- Data should flow inward from volatile edges (UI, APIs, integrations) to the stable core (domain logic).
- The core should make decisions and send commands outward to those edges.
- The core should never depend on frameworks, UI logic, or network responses.
When this flow is reversed — when the domain waits on framework details or data models from APIs — your architecture becomes fragile. Changing an API shouldn’t require touching your business rules.
A healthy system lets the domain logic stay still while the edges shift freely.
Boundaries reveal how your system evolves
Boundaries exist to separate different reasons for change. If two parts of your codebase change for the same reason, they belong together. If they change for different reasons, they need a boundary.
Two common issues show up here:
- Hidden coupling: modules appear separate but still share assumptions or data shapes.
- Boundary mismatch: a boundary exists, but the code on both sides still changes for the same reasons — it isn’t isolating change.
Boundaries are there to protect stability — not to look neat on a diagram.
Every boundary should reduce the blast radius of change. If an update in one place forces edits everywhere, the boundary is wrong.
Conclusion
Forget patterns and frameworks for a minute.
If you want to evaluate architecture quality, check these two things:
- Testability: Can you test each part without pulling in the whole system?
- Information flow: Does data move from the edges toward the core, not the reverse?
If both hold true, you’re looking at a design that’s stable, scalable, and easy to change. If not, the system will fight every attempt at evolution — no matter how clever the patterns look.