AI Regression Testing

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.

Setup (Vitest + Next.js App Router)

// vitest.config.ts
import { defineConfig } from 'vitest/config';
import path from 'path';

export default defineConfig({
  test: {
    environment: 'node',
    globals: true,
    include: ['__tests__/**/*.test.ts'],
    setupFiles: ['__tests__/setup.ts'],
  },
  resolve: {
    alias: {
      '@': path.resolve(__dirname, '.'),
    },
  },
});

// __tests__/setup.ts
// Force sandbox mode — no database needed
process.env.SANDBOX_MODE = 'true';
process.env.NEXT_PUBLIC_SUPABASE_URL = '';
process.env.NEXT_PUBLIC_SUPABASE_ANON_KEY = '';

Test Helper for Next.js API Routes

// __tests__/helpers.ts
import { NextRequest } from 'next/server';

export function createTestRequest(
  url: string,
  options?: {
    method?: string;
    body?: Record<string, unknown>;
    headers?: Record<string, string>;
    sandboxUserId?: string;
  }
): NextRequest {
  const { method = 'GET', body, headers = {}, sandboxUserId } = options || {};
  const fullUrl = url.startsWith('http') ? url : `http://localhost:3000${url}`;
  const reqHeaders: Record<string, string> = { ...headers };

  if (sandboxUserId) {
    reqHeaders['x-sandbox-user-id'] = sandboxUserId;
  }

  const init: { method: string; headers: Record<string, string>; body?: string } = {
    method,
    headers: reqHeaders,
  };

  if (body) {
    init.body = JSON.stringify(body);
    reqHeaders['content-type'] = 'application/json';
  }

  return new NextRequest(fullUrl, init);
}

export async function parseResponse(response: Response) {
  const json = await response.json();
  return { status: response.status, json };
}

Writing Regression Tests

The key principle: write tests for bugs that were found, not for code that works.

// __tests__/api/user/profile.test.ts
import { 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 response
const REQUIRED_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 = await GET(req);
    const { status, json } = await parseResponse(res);

    expect(status).toBe(200);
    for (const field of REQUIRED_FIELDS) {
      expect(json.data).toHaveProperty(field);
    }
  });

  // Regression test — this exact bug was introduced by AI 4 times
  it('notification_settings is not undefined (BUG-R1 regression)', async () => {
    const req = createTestRequest('/api/user/profile');
    const res = await GET(req);
    const { json } = await parseResponse(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 contract
describe('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 = await GET(req);
    const { json } = await parseResponse(res);

    // This caught a bug where partner_name was added
    // to production path but not sandbox path
    if (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 only
if (isSandboxMode()) {
  return { data: { id, email, name } }; // Missing new field
}
// Production path
return { data: { id, email, name, notification_settings } };

// PASS: Both paths must return the same shape
if (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 ON
  const res = await GET(createTestRequest('/api/user/profile'));
  const { json } = await parseResponse(res);

  for (const field of REQUIRED_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 SELECT
const { 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 columns
const { 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 cleared
catch (err) {
  setError("Failed to load");
  // reservations still shows data from previous tab!
}

// PASS: Clear related state on error
catch (err) {
  setReservations([]);  // Clear stale data
  setError("Failed to load");
}

Pattern 4: Optimistic Update Without Proper Rollback

// FAIL: No rollback on failure
const handleRemove = async (id: string) => {
  setItems((prev) => prev.filter((i) => i.id !== id));
  await fetch(`/api/items/${id}`, { method: 'DELETE' });
  // If API fails, item is gone from UI but still in DB
};

// PASS: Capture previous state and rollback on failure
const handleRemove = async (id: string) => {
  const prevItems = [...items];
  setItems((prev) => prev.filter((i) => i.id !== id));
  try {
    const res = await fetch(`/api/items/${id}`, { method: 'DELETE' });
    if (!res.ok) throw new Error('API error');
  } catch {
    setItems(prevItems); // Rollback
    alert('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:

1. AI tends to make the same category of mistake repeatedly
2. Bugs cluster in complex areas (auth, multi-path logic, state management)
3. Once tested, that exact regression cannot happen again
4. Test count grows organically with bug fixes — no wasted effort

Quick Reference

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