Use when running comprehensive WCAG accessibility audits with axe-core + pa11y + Lighthouse, generating context-aware remediation, or testing video accessibility. Supports 3-tier browser cascade with graceful degradation.
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Database schema validation, data integrity testing, migration testing, transaction isolation, and query performance. Use when testing data persistence, ensuring referential integrity, or validating database migrations.
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<default_to_action> When testing database operations:
Quick DB Testing Checklist:
Critical Success Factors:
| Type | Focus | When |
|---|---|---|
| Schema | Structure correct | Table creation |
| Integrity | Constraints work | Data operations |
| Migration | Up/down work | Schema changes |
| Transaction | ACID properties | Concurrent access |
| Performance | Query speed | Optimization |
| Property | Test Focus |
|---|---|
| Atomicity | All or nothing (rollback on error) |
| Consistency | Constraints always valid |
| Isolation | Concurrent transactions don't interfere |
| Durability | Committed data persists |
test('users table has correct schema', async () => {
const schema = await db.raw(`
SELECT column_name, data_type, is_nullable
FROM information_schema.columns
WHERE table_name = 'users'
`);
expect(schema).toContainEqual({
column_name: 'id',
data_type: 'integer',
is_nullable: 'NO'
});
expect(schema).toContainEqual({
column_name: 'email',
data_type: 'character varying',
is_nullable: 'NO'
});
});
test('email must be unique', async () => {
await db.users.create({ email: 'test@example.com' });
await expect(
db.users.create({ email: 'test@example.com' })
).rejects.toThrow('unique constraint violation');
});
test('foreign key prevents orphaned records', async () => {
const user = await db.users.create({ email: 'test@example.com' });
await db.orders.create({ userId: user.id, total: 100 });
await expect(
db.users.delete({ id: user.id })
).rejects.toThrow('foreign key constraint');
});
test('migration is reversible', async () => {
await migrate('add-users-table');
// Table exists after migration
const tables = await db.raw(`SELECT table_name FROM information_schema.tables`);
expect(tables.map(t => t.table_name)).toContain('users');
await rollback('add-users-table');
// Table gone after rollback
const tablesAfter = await db.raw(`SELECT table_name FROM information_schema.tables`);
expect(tablesAfter.map(t => t.table_name)).not.toContain('users');
});
test('migration preserves existing data', async () => {
await db.users.create({ email: 'test@example.com' });
await migrate('add-age-column');
const user = await db.users.findOne({ email: 'test@example.com' });
expect(user).toBeDefined();
expect(user.age).toBeNull(); // New column, null default
});
test('transaction rolls back on error', async () => {
const initialCount = await db.users.count();
try {
await db.transaction(async (trx) => {
await trx('users').insert({ email: 'user1@example.com' });
await trx('users').insert({ email: 'user2@example.com' });
throw new Error('Rollback test');
});
} catch (error) { /* Expected */ }
expect(await db.users.count()).toBe(initialCount);
});
test('concurrent transactions isolated', async () => {
const user = await db.users.create({ balance: 100 });
// Two concurrent withdrawals (race condition test)
await Promise.all([
db.transaction(async (trx) => {
const current = await trx('users').where({ id: user.id }).first();
await trx('users').update({ balance: current.balance - 50 });
}),
db.transaction(async (trx) => {
const current = await trx('users').where({ id: user.id }).first();
await trx('users').update({ balance: current.balance - 50 });
})
]);
const final = await db.users.findOne({ id: user.id });
expect(final.balance).toBe(0); // Proper isolation
});
// Generate test data with integrity
await Task("Generate Test Data", {
schema: 'ecommerce',
tables: ['users', 'products', 'orders'],
count: { users: 1000, products: 500, orders: 5000 },
preserveReferentialIntegrity: true
}, "qe-test-data-architect");
// Test migration safety
await Task("Migration Test", {
migration: 'add-payment-status-column',
tests: ['forward', 'rollback', 'data-preservation'],
environment: 'staging'
}, "qe-test-executor");
aqe/database-testing/
├── schema-snapshots/* - Current schema state
├── migrations/* - Migration test results
├── integrity/* - Constraint validation
└── performance/* - Query benchmarks
const dbFleet = await FleetManager.coordinate({
strategy: 'database-testing',
agents: [
'qe-test-data-architect', // Generate test data
'qe-test-executor', // Run DB tests
'qe-performance-tester' // Query performance
],
topology: 'sequential'
});
Database bugs are catastrophic. Data loss is unrecoverable. Corruption spreads silently. Performance issues compound.
Test migrations before production: Forward works, rollback works, data preserved, performance acceptable. Never deploy untested migrations.
With Agents: qe-test-data-architect generates realistic test data with referential integrity. qe-test-executor validates migrations automatically in CI/CD.
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Use when running comprehensive WCAG accessibility audits with axe-core + pa11y + Lighthouse, generating context-aware remediation, or testing video accessibility. Supports 3-tier browser cascade with graceful degradation.
中文简介正在准备中。当前显示原文说明。
WCAG 2.2 compliance testing, screen reader validation, and inclusive design verification. Use when ensuring legal compliance (ADA, Section 508), testing for disabilities, or building accessible applications for 1 billion disabled users globally.
中文简介正在准备中。当前显示原文说明。
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
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Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
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Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
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Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.
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