Performance Regression Testing and CI Budgets
Performance tests become useful when they can detect meaningful regressions without creating flaky gates. TaskFlow separates deterministic budgets from noisy environment-dependent benchmarks.
• Bundle-size reports can be compared in CI because build artifacts are relatively deterministic.
• Query-count assertions can catch accidental N+1 regressions in integration tests.
• EXPLAIN plan snapshots can be reviewed, but avoid brittle exact-cost assertions across environments/data changes.
• Microbenchmarks should run on controlled hardware and are poor generic cloud-CI gates.
• End-to-end latency load tests belong in a stable benchmark environment; compare distributions, not one request. // Example budget metadata - illustrative { "route": "GET /tasks", "scenario": "20 concurrent users / 30s", "p95MsTarget": 300, "unexpected5xxPercentMax": 1 }
Phase 30 observability remains the production regression detector. Synthetic benchmark results explain capacity in a scenario; field telemetry tells you what real users are experiencing. Key idea: A performance gate should fail on a material regression you understand, not on random cloud noise. Otherwise teams learn to ignore or disable it.
Tests are executable behavioral contracts. Good tests protect meaningful invariants and failure cases, not implementation trivia.
Restate the goal of “Performance Regression Testing and CI Budgets” as one observable behavior. Write the success signal before touching code.
Trace the boundary map: behavior contract → arrange → act → assert → regression signal. Mark the one boundary this lesson changes.
Inspect the current repository state and record the exact files, route, table, or runtime involved. Avoid creating an abstraction before you can name the existing owner.
Implement the smallest vertical slice. Add one coherent change, run the narrowest verification command, and read the output before continuing.
Connect the slice to TaskFlow’s phase artifact: Before/after performance report with measured bottleneck, change, and regression guard.. Verify behavior from the outside as a user or API client would.
Create one controlled failure that is safe to reproduce. Predict the symptom first, capture evidence, locate the earliest broken boundary, then fix only that cause.
Close the reference and rebuild or explain the core change from memory. Record what you would monitor or test before trusting the same change in production.
it("rejects deleting another member's task", async () => {
const response = await asUser(bob).delete(`/tasks/${aliceTask.id}`);
expect(response.status).toBe(403);
expect(await taskStillExists(aliceTask.id)).toBe(true);
});Do not copy this as a finished answer. Predict what each boundary does, type the smallest useful slice, run it, then compare the result with your prediction.
Look for shared database state, timers, globals, unordered execution, or missing cleanup before adding retries.
Move the highest-risk boundary to an integration test with the real adapter/database.
Without copying the final source, create a second small scenario that applies “Performance Regression Testing and CI Budgets” to a different TaskFlow resource or input. Write the acceptance criteria first, implement it, and provide the evidence that each criterion is satisfied.
Inspect PostgreSQL EXPLAIN/ANALYZE for a representative slow query and validate index/query changes.
Use the smallest change that demonstrates this lesson. Stop after it works and capture evidence.Caching incorrect data or bypassing authorization
Predict the symptom before causing the failure. Then locate the earliest boundary where reality diverges from expectation.Why does this responsibility live here, what input does it trust, what output proves success, and what would go wrong if another layer owned it?
If your explanation is vague, repeat the boundary trace before moving on.Close the reference. Reproduce the core idea from memory or explain the exact request/data flow aloud. Then check your diff or notes for accidental shortcuts that bypass validation, authorization, error handling, or verification earned in earlier phases.