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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Clear out junk files and repair common Windows errorsFree Scan →A dependency graph shows what currently depends on what; it does not, by itself, say whether those dependencies are deliberate, permitted, or meant to continue. To decide whether a proposed dependency fits a codebase, pair the graph’s evidence with explicit architectural rules, recorded exceptions, and checks that catch violations.
What does a dependency graph tell you?
A graph represents observed relationships in a codebase: for example, which service imports a repository, which route accesses a database, or which modules depend on one another. That makes it useful for investigating questions such as “What is this project actually doing?” and “What could a change affect?”
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It can also help reveal cycles, highly connected modules, and dependencies on unstable components. A 2019 paper from the IEEE International Conference on Software Architecture Companion discusses dependency graphs as a way to represent module views and investigate architectural smells, including cyclic dependencies, hub-like nodes, and nodes with unstable dependencies. Read the paper.
But an edge records a relationship; it does not explain why that relationship exists. A direct path from a route to a database could be an accepted design, an accidental shortcut, a legacy compromise, or a pattern the team plans to remove. The graph alone cannot distinguish those cases.
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What does the graph leave unanswered?
Architecture decisions express intent: which boundaries matter, which dependencies are allowed, and which are out of bounds. For example, a team might decide that payment-provider access must pass through an adapter, persistence code belongs only on the server, one module must not depend on another, or a particular service owns authorization.
Without such a rule, a direct import of a payment provider is simply an observed fact. Once the adapter boundary is explicit, that same import can be judged against the project’s intended design. The practical question becomes: “Which parts of that behavior has the team decided to preserve?”
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History and frequency help explain how a pattern arose and how widely it is used, but neither proves that the team approves of it. A long-standing dependency may still be a violation; a common one may still be slated for change.
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| Information | Question it answers |
|---|---|
| Observed dependency graph | What currently depends on what, and what might a change affect? |
| Explicit project policy | Which dependencies are allowed, forbidden, or permitted as named exceptions? |
| History and frequency | How did this pattern arise, and how common is it? |
| Verification | Did a proposed change violate a stated constraint? |
These are complementary kinds of information, not competing tools. A useful way to assess a proposed change is to ask “Am I allowed to make this dependency?” and then look for the project rule that answers it. If no rule exists, the graph can show what is already there, but not supply the missing decision.
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Turn architectural intent into checks
For a rule to be checked reliably, it needs to be concrete enough to distinguish an allowed dependency from a forbidden one. A team can define its boundaries, identify permitted exceptions, and decide how to handle violations already present in the repository. Where appropriate, it can baseline existing violations and have checks focus on newly introduced ones.
- State the boundary. Describe the dependency the project requires or prohibits, such as requiring payment-provider access to go through an adapter.
- Record exceptions. Name any allowed departures rather than letting them become invisible precedent.
- Account for existing violations. If the codebase already breaks a rule, decide whether to address those cases immediately or baseline them so new violations remain visible.
- Check proposed changes. Run a verification step against the stated policy and review any new violation before it becomes part of the codebase.
Vlad Zoff’s article describes Guard’s core checks as deterministic and local. That is the author’s description of that product, not a general guarantee about architecture tools. The broader point is that a check can only enforce the intent the project has actually expressed.
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When can a graph be part of an architecture model?
The distinction is not that graphs can never represent architecture. A graph with explicit semantics, defined construction rules, and validation can be part of an architectural model. Kora’s documentation describes a typed, compile-time application graph as a model for dependencies, lifecycle, module composition, operational wiring, and tooling. That is a framework-specific example; it does not mean every dependency graph carries those meanings. See Kora’s documentation.
The key difference is whether the graph merely reports observed connections or is built and interpreted according to explicit architectural rules. In the latter case, those rules give the graph meaning beyond its edges.
Keep the distinction clear when reading a codebase
- Use the graph to discover structure, connections, and possible change impact.
- Look for written rules to determine which dependencies the team intends to allow or forbid.
- Treat exceptions as explicit decisions, not as assumptions inferred from existing code.
- Use checks to test proposed changes against those decisions.
For a definition of a related concept, the Open SWE Book describes an architectural pattern as “a reusable, named arrangement of modules and connectors that has repeatedly solved a recurring design problem.” Read Chapter 7, “Architectural Patterns”. A named pattern can help communicate a design choice, but the project still needs to make its own boundaries and exceptions clear.
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