Regression testing strategies for AI-assisted development: sandbox-mode API testing without database dependencies, automated bug-check workflows, and patterns to catch blind spots when the same model writes and reviews code. USE WHEN guarding AI-generated changes against regressions.
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Regression testing strategies for AI-assisted development: sandbox-mode API testing without database dependencies, automated bug-check workflows, and patterns to catch blind spots when the same model writes and reviews code. USE WHEN guarding AI-generated changes against regressions.
origin
ECC
cluster
quality-eval
version
1.0.0
AI Regression Testing
Testing patterns specifically designed for AI-assisted development, where the same model writes code and reviews it — creating systematic blind spots that only automated tests can catch.
When to Activate
AI agent (Claude Code, Cursor, Codex) has modified API routes or backend logic
A bug was found and fixed — need to prevent re-introduction
Project has a sandbox/mock mode that can be leveraged for DB-free testing
Running /bug-check or similar review commands after code changes
Multiple code paths exist (sandbox vs production, feature flags, etc.)
The Core Problem
When an AI writes code and then reviews its own work, it carries the same assumptions into both steps. This creates a predictable failure pattern:
AI writes fix → AI reviews fix → AI says "looks correct" → Bug still exists
Real-world example (observed in production):
Fix 1: Added notification_settings to API response
→ Forgot to add it to the SELECT query
→ AI reviewed and missed it (same blind spot)
Fix 2: Added it to SELECT query
→ TypeScript build error (column not in generated types)
→ AI reviewed Fix 1 but didn't catch the SELECT issue
Fix 3: Changed to SELECT *
→ Fixed production path, forgot sandbox path
→ AI reviewed and missed it AGAIN (4th occurrence)
Fix 4: Test caught it instantly on first run PASS:
The pattern: sandbox/production path inconsistency is the #1 AI-introduced regression.
Sandbox-Mode API Testing
Most projects with AI-friendly architecture have a sandbox/mock mode. This is the key to fast, DB-free API testing.
The key principle: write tests for bugs that were found, not for code that works.
// __tests__/api/user/profile.test.tsimport { describe, it, expect } from"vitest";
import { createTestRequest, parseResponse } from"../../helpers";
import { GET, PATCH } from"@/app/api/user/profile/route";
// Define the contract — what fields MUST be in the responseconstREQUIRED_FIELDS = [
"id",
"email",
"full_name",
"phone",
"role",
"created_at",
"avatar_url",
"notification_settings", // ← Added after bug found it missing
];
describe("GET /api/user/profile", () => {
it("returns all required fields", async () => {
const req = createTestRequest("/api/user/profile");
const res = awaitGET(req);
const { status, json } = awaitparseResponse(res);
expect(status).toBe(200);
for (const field ofREQUIRED_FIELDS) {
expect(json.data).toHaveProperty(field);
}
});
// Regression test — this exact bug was introduced by AI 4 timesit("notification_settings is not undefined (BUG-R1 regression)", async () => {
const req = createTestRequest("/api/user/profile");
const res = awaitGET(req);
const { json } = awaitparseResponse(res);
expect("notification_settings"in json.data).toBe(true);
const ns = json.data.notification_settings;
expect(ns === null || typeof ns === "object").toBe(true);
});
});
Testing Sandbox/Production Parity
The most common AI regression: fixing production path but forgetting sandbox path (or vice versa).
// Test that sandbox responses match the expected contractdescribe("GET /api/user/messages (conversation list)", () => {
it("includes partner_name in sandbox mode", async () => {
const req = createTestRequest("/api/user/messages", {
sandboxUserId: "user-001",
});
const res = awaitGET(req);
const { json } = awaitparseResponse(res);
// This caught a bug where partner_name was added// to production path but not sandbox pathif (json.data.length > 0) {
for (const conv of json.data) {
expect("partner_name"in conv).toBe(true);
}
}
});
});
Integrating Tests into Bug-Check Workflow
Custom Command Definition
<!-- .claude/commands/bug-check.md -->
# Bug Check## Step 1: Automated Tests (mandatory, cannot skip)
Run these commands FIRST before any code review:
npm run test # Vitest test suite
npm run build # TypeScript type check + build
- If tests fail → report as highest priority bug
- If build fails → report type errors as highest priority
- Only proceed to Step 2 if both pass
## Step 2: Code Review (AI review)1. Sandbox / production path consistency
2. API response shape matches frontend expectations
3. SELECT clause completeness
4. Error handling with rollback
5. Optimistic update race conditions
## Step 3: For each bug fixed, propose a regression test
The Workflow
User: "バグチェックして" (or "/bug-check")
│
├─ Step 1: npm run test
│ ├─ FAIL → Bug found mechanically (no AI judgment needed)
│ └─ PASS → Continue
│
├─ Step 2: npm run build
│ ├─ FAIL → Type error found mechanically
│ └─ PASS → Continue
│
├─ Step 3: AI code review (with known blind spots in mind)
│ └─ Findings reported
│
└─ Step 4: For each fix, write a regression test
└─ Next bug-check catches if fix breaks
Common AI Regression Patterns
Pattern 1: Sandbox/Production Path Mismatch
Frequency: Most common (observed in 3 out of 4 regressions)
// FAIL: AI adds field to production path onlyif (isSandboxMode()) {
return { data: { id, email, name } }; // Missing new field
}
// Production pathreturn { data: { id, email, name, notification_settings } };
// PASS: Both paths must return the same shapeif (isSandboxMode()) {
return { data: { id, email, name, notification_settings: null } };
}
return { data: { id, email, name, notification_settings } };
Test to catch it:
it("sandbox and production return same fields", async () => {
// In test env, sandbox mode is forced ONconst res = awaitGET(createTestRequest("/api/user/profile"));
const { json } = awaitparseResponse(res);
for (const field ofREQUIRED_FIELDS) {
expect(json.data).toHaveProperty(field);
}
});
Pattern 2: SELECT Clause Omission
Frequency: Common with Supabase/Prisma when adding new columns
// FAIL: New column added to response but not to SELECTconst { data } = await supabase
.from("users")
.select("id, email, name") // notification_settings not here
.single();
return { data: { ...data, notification_settings: data.notification_settings } };
// → notification_settings is always undefined// PASS: Use SELECT * or explicitly include new columnsconst { data } = await supabase
.from("users")
.select("*")
.single();
Pattern 3: Error State Leakage
Frequency: Moderate — when adding error handling to existing components
// FAIL: Error state set but old data not clearedcatch (err) {
setError("Failed to load");
// reservations still shows data from previous tab!
}
// PASS: Clear related state on errorcatch (err) {
setReservations([]); // Clear stale datasetError("Failed to load");
}
Pattern 4: Optimistic Update Without Proper Rollback
// FAIL: No rollback on failureconsthandleRemove = async (id: string) => {
setItems(prev => prev.filter(i => i.id !== id));
awaitfetch(`/api/items/${id}`, { method: "DELETE" });
// If API fails, item is gone from UI but still in DB
};
// PASS: Capture previous state and rollback on failureconsthandleRemove = async (id: string) => {
const prevItems = [...items];
setItems(prev => prev.filter(i => i.id !== id));
try {
const res = awaitfetch(`/api/items/${id}`, { method: "DELETE" });
if (!res.ok) thrownewError("API error");
} catch {
setItems(prevItems); // Rollbackalert("削除に失敗しました");
}
};
Strategy: Test Where Bugs Were Found
Don't aim for 100% coverage. Instead:
Bug found in /api/user/profile → Write test for profile API
Bug found in /api/user/messages → Write test for messages API
Bug found in /api/user/favorites → Write test for favorites API
No bug in /api/user/notifications → Don't write test (yet)
Why this works with AI development:
AI tends to make the same category of mistake repeatedly
Bugs cluster in complex areas (auth, multi-path logic, state management)
Once tested, that exact regression cannot happen again
Test count grows organically with bug fixes — no wasted effort
Quick Reference
AI Regression Pattern
Test Strategy
Priority
Sandbox/production mismatch
Assert same response shape in sandbox mode
High
SELECT clause omission
Assert all required fields in response
High
Error state leakage
Assert state cleanup on error
Medium
Missing rollback
Assert state restored on API failure
Medium
Type cast masking null
Assert field is not undefined
Medium
DO / DON'T
DO:
Write tests immediately after finding a bug (before fixing it if possible)
Test the API response shape, not the implementation
Run tests as the first step of every bug-check
Keep tests fast (< 1 second total with sandbox mode)
Name tests after the bug they prevent (e.g., "BUG-R1 regression")
DON'T:
Write tests for code that has never had a bug
Trust AI self-review as a substitute for automated tests
Skip sandbox path testing because "it's just mock data"
Write integration tests when unit tests suffice
Aim for coverage percentage — aim for regression prevention
