Scott Burgholzer says he built Blast Radius by defining its requirements, architecture, and implementation tasks in Kiro before writing code. In his September 30, 2026 account, the project had 349 passing tests across 29 files and no vi.mock( calls. The approach centers on explicit dependency injection for AWS-facing handlers and property-based tests for pure logic—not on avoiding all test doubles.
What spec-first meant for Blast Radius
Blast Radius is an open-source infrastructure-as-code change impact analysis project. Burgholzer describes using Kiro’s spec workflow in this order: requirements, design, task breakdown, then implementation. He says he settled key structural decisions on paper first: a canonical format for resource changes, a Step Functions pipeline, and a dependency-injection strategy for testing.
His stated reason for deciding these early was to avoid refactoring foundational choices later and to make tests part of the implementation plan. He describes the workflow as changing how he works, which is his personal assessment rather than evidence that spec-first development produces the same results for every project. Burgholzer’s project account was posted September 30, 2026.
How the monorepo is organized
The project uses TypeScript and npm workspaces. Burgholzer says he chose this arrangement rather than Nx, Turborepo, or Lerna. Its five workspaces have distinct roles:
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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. |
@blast-radius/infra |
A CDK deployment stack. |
The intended dependency direction is one-way: core has no internal package dependencies, and the other packages consume its shared types. The frontend maintains its own API type definitions instead of using that shared source, a choice Burgholzer identifies as a potential type-drift point.
Why the tests have no module mocks
The reported count is 349 passing tests in 29 test files. Burgholzer also says a search of the repository found no vi.mock( calls. That does not mean the tests contain no doubles: he distinguishes module replacement from vi.fn() stubs and fake AWS clients.
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Pass dependencies into handlers
In the described design, Lambda handlers accept dependencies explicitly. Tests can construct fake clients and pass them into the handler rather than replacing the module that creates or imports those clients. The test therefore exercises the handler through the same call shape used in production, while controlling its external dependencies.
Use property tests for pure logic
For logic that can run without AWS—such as scoring, validation, filtering, sorting, and caching—the author describes using fast-check property-based tests. Examples cover core validation and cache behavior, Lambda scoring and dependency chains, and frontend filtering, sorting, and JSON export. One property checks that sorting produces non-increasing impact scores for generated resource lists of up to 100 items. This tests an invariant across generated cases; it does not prove correctness for every possible input.
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How changes move through the system
Blast Radius adapters for CDK, CloudFormation, and Terraform normalize infrastructure changes into a canonical ResourceChange format. The article describes mapping create, update, and delete operations to Add, Modify, and Remove, and mapping replacement variants from the different tools to a common Replace concept. A DynamoDB-backed adapter registry associates formats with Lambda ARNs; the author says CDK deployment seeds its default rows.
The CLI’s main workflow is blast-radius analyze. Burgholzer says it can generate input from CDK, Terraform, or CloudFormation. For CloudFormation, it creates and inspects a changeset, then deletes it rather than executing it. The account describes status polling every three seconds, up to a 90-second ceiling, with stale status detected after five polls without a change. The CLI release workflow bundles a single Node-targeted file when version tags are published.
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Runtime problems local tests did not catch
Burgholzer says two issues passed local tests and surfaced only in AWS. These are lessons from this project’s deployment, not general guarantees about Lambda behavior.
Handler behavior in the Node.js 22 runtime
He reports that synchronous adapter handlers returned null in the runtime and that declaring the handlers async fixed the issue. Treat this as a project-specific observation: the account does not establish that synchronous handlers universally behave this way in Node.js 22.
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Lambda Context mistaken for injected dependencies
Lambda invokes a handler with an event and a context argument. The author says a simple null-coalescing fallback for optional dependencies could treat the truthy Context object as the dependency bundle. His reported fix was to check for an expected client key before accepting an injected object; otherwise, the handler constructs its defaults.
Other deployment seams
The author also mentions an API Gateway timeout that needed tuning and Bedrock model configuration that differed from his initial expectation. His account provides no quantified settings or further detail for either issue.
Limits Burgholzer identified
These are retrospective caveats about the version described in the article, not confirmation of the project’s current state.
- Analysis duration: The CLI’s 90-second ceiling is described as a soft limit alongside a 120-second Step Functions timeout. Large dependency graphs could exceed the available time.
- Coverage labels: The
full,partial, andunknownlabels are coarse; they do not identify which relationships failed to resolve. - Risk scoring: The scoring constants were hand-tuned. The author says team-specific configuration or learning from incident outcomes might be needed later.
- Release coordination: A single repository and deploy model could become awkward if frontend and backend release schedules diverge.
What this example shows—and what it does not
Burgholzer’s account illustrates a practical combination: write down architecture and tasks before implementation, keep domain logic in a dependency-light core, inject external clients at handler boundaries, and test pure logic with generated inputs. It also shows why passing tests cannot substitute for exercising deployment runtimes and service boundaries.
The 349-test figure and zero-module-mock count are the author’s report; they have not been independently verified here. The article does not provide a comparative study showing that Kiro or spec-first development caused the counts, nor does it establish a general testing benchmark. The useful takeaway is the described design and the specific gaps it exposed after deployment.
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