Systematic test design with boundary value analysis, equivalence partitioning, decision tables, state transition testing, and combinatorial testing. Use when designing comprehensive test cases, reducing redundant tests, or ensuring systematic coverage.
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<default_to_action> When designing test cases, select technique by input type:
// Auto-generate BVA tests
await Task("Generate BVA Tests", {
field: 'age',
dataType: 'integer',
constraints: { min: 18, max: 120 }
}, "qe-test-generator");
// Returns: 6 boundary test cases
// Auto-generate pairwise tests
await Task("Generate Pairwise Tests", {
parameters: {
browser: ['Chrome', 'Firefox', 'Safari'],
os: ['Windows', 'Mac', 'Linux'],
screen: ['Desktop', 'Tablet', 'Mobile']
}
}, "qe-test-generator");
// Returns: 9-12 tests (vs 27 full combination)
aqe/test-design/
├── bva-analysis/* - Boundary value tests
├── partitions/* - Equivalence partitions
├── decision-tables/* - Decision table tests
└── pairwise/* - Combinatorial reduction
const designFleet = await FleetManager.coordinate({
strategy: 'systematic-test-design',
agents: [
'qe-test-generator', // Apply design techniques
'qe-coverage-analyzer', // Analyze coverage
'qe-quality-analyzer' // Assess test quality
],
topology: 'sequential'
});
With Agents: qe-test-generator applies these techniques automatically, generating optimal test suites with maximum coverage and minimum redundancy. Agents identify boundaries, partitions, and combinations from code analysis.
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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.
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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.
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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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