Testing, Debugging and Devtools Phase 25 - Optimistic Updates
What to test Optimistic code should be tested as a state machine, not only as a successful click.
Core matrix • Mutation starts: predicted value appears immediately.
• In-flight refetch cannot overwrite the prediction.
• Successful response replaces prediction with server value.
• 400/403/409/network failure restores previous cache value.
• List queries invalidate after settlement.
• Two updates to the same Task preserve intended ordering when scoped serially.
• Two different Tasks can mutate independently.
• Pending create rows disappear after success and are replaced by server-backed query data.
• Filtered pages reconcile correctly after invalidation.
Devtools clues • Watch the Task detail query before, during, and after onMutate.
• Inspect mutation status, variables, submittedAt, and mutationKey.
• Confirm invalidation marks only the intended query families stale.
• Look for duplicate refetches caused by both onSuccess and onSettled performing the same work.
Debugging rule If the UI ends in the wrong state, write the timeline: previous cache -> onMutate -> request A/B -> callback order -> invalidation -> refetch. Optimistic bugs are usually ordering bugs.
Tests are executable behavioral contracts. Good tests protect meaningful invariants and failure cases, not implementation trivia.
Restate the goal of “Testing Debugging and Devtools” 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: Optimistic task update/create flow with rollback and authoritative server reconciliation.. 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 “Testing Debugging and Devtools” to a different TaskFlow resource or input. Write the acceptance criteria first, implement it, and provide the evidence that each criterion is satisfied.
On success/settle, reconcile with authoritative server data rather than trusting the optimistic object forever.
Use the smallest change that demonstrates this lesson. Stop after it works and capture evidence.Older mutation response overwriting newer user intent
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.