The Tool Desk
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What “spec-first” meant for Blast Radius
Burgholzer describes a sequence of requirements, design, task breakdown, and then code. He says he decided the canonical data format, analysis pipeline, and dependency-injection testing strategy on paper before implementation. The practical value, in his account, was making tests part of the planned work and reducing early refactoring of structural choices.
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Blast Radius is intended to show the potential impact of infrastructure-as-code changes before deployment. Adapters for CDK, CloudFormation, and Terraform normalize proposed changes into a shared ResourceChange representation. Examples include mapping create, update, and delete operations to Add, Modify, and Remove, and mapping provider-specific replacement forms to Replace.
How the project was divided
The project uses npm workspaces in a TypeScript monorepo, rather than Nx, Turborepo, or Lerna. The author describes a one-way internal dependency pattern: core has no internal package dependencies, and the other workspaces consume its shared types.
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| Workspace | Role described by the author |
|---|---|
@blast-radius/core |
Shared models, validation, caching, retry logic, verdicts, and authorization scoping. |
@blast-radius/lambdas |
Lambda handlers used in the analysis pipeline. |
@blast-radius/frontend |
React, Vite, and Cytoscape.js single-page application. |
@blast-radius/cli |
Command-line integration for CI/CD workflows. |
@blast-radius/infra |
CDK deployment stack. |
The frontend keeps its own API type definitions instead of sharing core’s types directly. Burgholzer identifies this as a potential source of drift: duplicated contracts need deliberate maintenance as the API evolves.
Why the tests avoid module mocks
The reported total is 349 passing tests across 29 files, with a search for vi.mock( returning no results. That does not mean the tests use no test doubles. Burgholzer distinguishes module mocking from vi.fn() stubs: handlers receive fake AWS clients through explicit dependency arguments, rather than having imported modules replaced.
This design makes a test call the handler with the same general shape as production while supplying controlled dependencies. It also makes dependencies visible at the function boundary, rather than hiding them in module-level behavior. The account is specific to this project and does not establish that avoiding module mocks is always preferable.
Where property-based tests fit
For deterministic logic, the project uses fast-check to generate input cases and check invariants. The author lists scoring, validation, filtering, sorting, and caching among the targets. Examples span core validation and cache behavior, Lambda scoring and dependency-chain logic, and frontend filtering, sorting, and JSON export.
One representative property checks that sorting produces non-increasing impact scores for generated resource lists of up to 100 items. Generated cases can expose edge conditions that a handful of hand-picked examples might miss, but they do not prove correctness for every possible input.
What local tests did not catch
Burgholzer says two problems emerged only after deployment to AWS, illustrating the boundary between application-level tests and the environment that invokes the application.
Lambda handler behavior under Node.js 22
He reports that synchronous adapter handlers returned null in the runtime and that declaring the handlers async resolved the issue in this project. Treat this as a project-specific observation, not a universal rule for Node.js 22 Lambda handlers; runtime behavior can depend on the handler and its invocation contract.
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Lambda invokes a handler with event and context arguments. The author says a fallback that selected injected dependencies using null-coalescing could therefore accept the truthy Context object as though it were a dependency container. His reported fix was to verify that the injected object contained an expected client key before using it, and otherwise construct default dependencies.
Other deployment adjustments
The article also mentions tuning an API Gateway timeout and correcting Bedrock model configuration after the initial expectation proved wrong. It does not quantify either adjustment, so these are useful reminders to validate deployed service configuration, not values to copy into another system.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How changes move through the analysis workflow
The adapters convert infrastructure changes into the canonical model. A DynamoDB-backed adapter registry maps formats to Lambda ARNs, and the article says the CDK deployment seeds the default registry rows. In the CLI, the main entry point is blast-radius analyze, which can generate input from CDK, Terraform, or CloudFormation.
For CloudFormation, the described workflow creates and inspects a changeset, then deletes it rather than executing it. The CLI polls for status every three seconds, up to a 90-second ceiling, and treats five unchanged polls as stale status. The author calls that ceiling soft: the Step Functions timeout is 120 seconds, and a large dependency graph could outlast the CLI’s wait window. A version-tagged release workflow bundles the CLI into a single Node-targeted file.
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- Analysis duration: Large dependency graphs may exceed the CLI’s 90-second polling ceiling, even though the described Step Functions timeout is 120 seconds.
- Coverage labels: The
full,partial, andunknownlabels summarize coverage coarsely; they do not say which relationships failed to resolve. - Risk scoring: The scoring weights are hand-tuned constants. The author raises team configuration or learning from incident outcomes as possible future directions, not existing capabilities.
- Repository and deployment shape: A single repo and deploy model may become awkward if the frontend and backend need different release cadences.
These are Burgholzer’s reflections on the version he describes, rather than claims about the project’s current state.
What the example does—and does not—show
Burgholzer writes that “The Kiro spec workflow genuinely changed how I work.” The reported test count and architecture provide a concrete example of planning test seams and runtime concerns before implementation. They do not show that Kiro alone caused the result, that 349 tests guarantee safe infrastructure deployments, or that spec-first development will produce the same outcome on another project. The article is best read as one builder’s account of how requirements-first planning, explicit dependencies, generated property checks, and deployment feedback fit together.
Source: Scott Burgholzer, “349 Tests, Zero Module Mocks: Building Blast Radius Spec-First with Kiro”, DEV Community / AWS Community Builders, September 30, 2026.
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