/aqe-optimize
Optimize test suites using sublinear algorithms to maximize coverage while minimizing test count and execution time
> /plugin marketplace add proffesor-for-testing/agentic-qe > /plugin install agentic-qe-fleet@agentic-qe
How it fires
How this command gets triggered: by you, by Claude, or both.
- Fires itselfClaude auto-loads it when your prompt matches the work.
- You can call itInvoke it directly when you want it.
- Slash command
/aqe-optimize
Context preview
What this command does when you run it.
Optimize test suites using sublinear algorithms to maximize coverage while minimizing test count and execution time
Command definition
aqe-optimize.mdname: aqe-optimize
description: Optimize test suites using sublinear algorithms to maximize coverage while minimizing test count and execution time
AQE Optimize Test Suite
Optimize test suites using sublinear algorithms to maximize coverage while minimizing test count and execution time.
Usage
aqe optimize <target> [options]
# or
/aqe-optimize <target> [options]
Options
| Option | Type | Default | Description | |--------|------|---------|-------------| | `target` | string | **required** | Optimization target: suite, coverage, performance, flakiness | | `--path` | path | `./tests` | Test suite path | | `--algorithm` | string | `sublinear` | Algorithm: sublinear, genetic, greedy, heuristic | | `--objective` | string | `coverage-per-test` | Objective: coverage-per-test, execution-time, reliability | | `--budget` | number | - | Time/test budget constraint (seconds) | | `--aggressive` | boolean | `false` | Aggressive optimization (may remove tests) | | `--dry-run` | boolean | `false` | Preview optimization without applying |
Examples
Optimize Test Suite
aqe optimize suite --path tests/unit --algorithm sublinear
Optimizes test suite using sublinear algorithms for maximum efficiency.
Maximize Coverage Efficiency
aqe optimize coverage --objective coverage-per-test --aggressive
Aggressively optimizes for maximum coverage per test ratio.
Reduce Execution Time
aqe optimize performance --budget 300 --algorithm genetic
Optimizes to meet 300-second execution budget using genetic algorithm.
Remove Flaky Tests
aqe optimize flakiness --dry-run
Identifies and previews removal of flaky tests without modifying suite.
Multi-Objective Optimization
aqe optimize suite --objective coverage-per-test --budget 180
Optimizes for both coverage efficiency and time budget.
Integration with Claude Code
Spawning Optimizer Agent
// Use Claude Code's Task tool to spawn the optimizer agent
Task("Optimize test suite for efficiency", `
Perform comprehensive test suite optimization:
- Use sublinear algorithms for O(log n) performance
- Maximize coverage while minimizing test count
- Target: Reduce execution time by 30%
- Maintain 95% coverage threshold
Store optimization results: aqe/optimization/results
Coordinate with test executor to validate optimized suite.
`, "qe-coverage-analyzer")Coordinated Optimization Workflow
// Optimize suite and validate results
[Single Message]:
Task("Optimize test suite", "Use sublinear algorithms to reduce redundancy", "qe-coverage-analyzer")
Task("Validate optimized suite", "Ensure coverage maintained after optimization", "qe-test-executor")
TodoWrite({ todos: [
{content: "Analyze test redundancy", status: "in_progress", activeForm: "Analyzing redundancy"},
{content: "Apply sublinear optimization", status: "in_progress", activeForm: "Optimizing suite"},
{content: "Validate optimized suite", status: "pending", activeForm: "Validating optimization"},
{content: "Measure performance improvement", status: "pending", activeForm: "Measuring improvement"}
]})Agent Coordination
Primary Agent
- **qe-coverage-analyzer**: Main agent with optimization module
Supporting Agents
- **qe-test-executor**: Validates optimized suite
- **qe-performance-tester**: Measures performance impact
Coordination Flow
1. Pre-Task Hook
├─> Retrieve test suite metadata
├─> Retrieve coverage matrix
├─> Retrieve execution history
└─> Load optimization parameters
2. Optimization Process
├─> Build test-coverage matrix
├─> Apply sublinear optimization algorithm
├─> Calculate minimal test set for target coverage
├─> Identify redundant tests
└─> Generate optimized suite
3. Validation Phase
├─> Execute optimized suite
├─> Verify coverage maintained
├─> Measure performance improvement
└─> Compare metrics before/after
4. Post-Task Hook
├─> Store optimization results
├─> Update test suite metadata
├─> Train neural patterns
└─> Notify fleet of improvements
Memory Operations
Input Memory Keys
# Retrieve test suite metadata
npx claude-flow@alpha memory retrieve --key "aqe/test-suite/${suite}"
# Retrieve coverage matrix
npx claude-flow@alpha memory retrieve --key "aqe/coverage-matrix"
# Retrieve execution history
npx claude-flow@alpha memory retrieve --key "aqe/execution-history"Output Memory Keys
# Store optimization results
npx claude-flow@alpha memory store \
--key "aqe/optimization/results" \
--value '{"testsBefore": 120, "testsAfter": 85, "coverageDelta": 0.2}'
# Store optimized suite
npx claude-flow@alpha memory store \
--key "aqe/optimized-suite" \
--value '{"tests": ["test1.ts", "test2.ts"], "metadata": {}}'
# Store optimization metrics
npx claude-flow@alpha memory store \
--key "aqe/optimization-metrics" \
--value '{"timeSaved": 45, "testsRemoved": 35}'Hooks and Coordination
Pre-Task Hook
npx claude-flow@alpha hooks pre-task \
--description "Optimize ${target} with ${algorithm}" \
--agent "qe-coverage-analyzer"Post-Task Hook
npx claude-flow@alpha hooks post-task \
--task-id "${OPT_ID}" \
--results "${OPT_RESULTS}"Notify Fleet
npx claude-flow@alpha hooks notify \
--message "Optimization: ${TEST_REDUCTION}% fewer tests, ${TIME_SAVED}s saved"Expected Outputs
Success Output
⚡ Optimizing suite...
🧮 Running sublinear optimization algorithm...
📊 Optimization Results:
Algorithm: sublinear
Objective: coverage-per-test
Tests Before: 120
Tests After: 85
Reduction: 29.2%
Coverage Before: 93.5%
Coverage After: 93.7%
Delta: +0.2%
Time Saved: 45s per run
✅ Optimization successful! Fewer tests with improved coverage.
Dry-Run Output
🔍 Optimization Preview (D
Read more
name: aqe-optimize description: Optimize test suites using sublinear algorithms to maximize coverage while minimizing test count and execution time
AQE Optimize Test Suite
Optimize test suites using sublinear algorithms to maximize coverage while minimizing test count and execution time.
Usage
aqe optimize <target> [options] # or /aqe-optimize <target> [options]
Options
| Option | Type | Default | Description | |--------|------|---------|-------------| | `target` | string | **required** | Optimization target: suite, coverage, performance, flakiness | | `--path` | path | `./tests` | Test suite path | | `--algorithm` | string | `sublinear` | Algorithm: sublinear, genetic, greedy, heuristic | | `--objective` | string | `coverage-per-test` | Objective: coverage-per-test, execution-time, reliability | | `--budget` | number | - | Time/test budget constraint (seconds) | | `--aggressive` | boolean | `false` | Aggressive optimization (may remove tests) | | `--dry-run` | boolean | `false` | Preview optimization without applying |
Examples
Optimize Test Suite
aqe optimize suite --path tests/unit --algorithm sublinear
Optimizes test suite using sublinear algorithms for maximum efficiency.
Maximize Coverage Efficiency
aqe optimize coverage --objective coverage-per-test --aggressive
Aggressively optimizes for maximum coverage per test ratio.
Reduce Execution Time
aqe optimize performance --budget 300 --algorithm genetic
Optimizes to meet 300-second execution budget using genetic algorithm.
Remove Flaky Tests
aqe optimize flakiness --dry-run
Identifies and previews removal of flaky tests without modifying suite.
Multi-Objective Optimization
aqe optimize suite --objective coverage-per-test --budget 180
Optimizes for both coverage efficiency and time budget.
Integration with Claude Code
Spawning Optimizer Agent
// Use Claude Code's Task tool to spawn the optimizer agent
Task("Optimize test suite for efficiency", `
Perform comprehensive test suite optimization:
- Use sublinear algorithms for O(log n) performance
- Maximize coverage while minimizing test count
- Target: Reduce execution time by 30%
- Maintain 95% coverage threshold
Store optimization results: aqe/optimization/results
Coordinate with test executor to validate optimized suite.
`, "qe-coverage-analyzer")Coordinated Optimization Workflow
// Optimize suite and validate results
[Single Message]:
Task("Optimize test suite", "Use sublinear algorithms to reduce redundancy", "qe-coverage-analyzer")
Task("Validate optimized suite", "Ensure coverage maintained after optimization", "qe-test-executor")
TodoWrite({ todos: [
{content: "Analyze test redundancy", status: "in_progress", activeForm: "Analyzing redundancy"},
{content: "Apply sublinear optimization", status: "in_progress", activeForm: "Optimizing suite"},
{content: "Validate optimized suite", status: "pending", activeForm: "Validating optimization"},
{content: "Measure performance improvement", status: "pending", activeForm: "Measuring improvement"}
]})Agent Coordination
Primary Agent
- **qe-coverage-analyzer**: Main agent with optimization module
Supporting Agents
- **qe-test-executor**: Validates optimized suite
- **qe-performance-tester**: Measures performance impact
Coordination Flow
1. Pre-Task Hook ├─> Retrieve test suite metadata ├─> Retrieve coverage matrix ├─> Retrieve execution history └─> Load optimization parameters 2. Optimization Process ├─> Build test-coverage matrix ├─> Apply sublinear optimization algorithm ├─> Calculate minimal test set for target coverage ├─> Identify redundant tests └─> Generate optimized suite 3. Validation Phase ├─> Execute optimized suite ├─> Verify coverage maintained ├─> Measure performance improvement └─> Compare metrics before/after 4. Post-Task Hook ├─> Store optimization results ├─> Update test suite metadata ├─> Train neural patterns └─> Notify fleet of improvements
Memory Operations
Input Memory Keys
# Retrieve test suite metadata
npx claude-flow@alpha memory retrieve --key "aqe/test-suite/${suite}"
# Retrieve coverage matrix
npx claude-flow@alpha memory retrieve --key "aqe/coverage-matrix"
# Retrieve execution history
npx claude-flow@alpha memory retrieve --key "aqe/execution-history"Output Memory Keys
# Store optimization results
npx claude-flow@alpha memory store \
--key "aqe/optimization/results" \
--value '{"testsBefore": 120, "testsAfter": 85, "coverageDelta": 0.2}'
# Store optimized suite
npx claude-flow@alpha memory store \
--key "aqe/optimized-suite" \
--value '{"tests": ["test1.ts", "test2.ts"], "metadata": {}}'
# Store optimization metrics
npx claude-flow@alpha memory store \
--key "aqe/optimization-metrics" \
--value '{"timeSaved": 45, "testsRemoved": 35}'Hooks and Coordination
Pre-Task Hook
npx claude-flow@alpha hooks pre-task \
--description "Optimize ${target} with ${algorithm}" \
--agent "qe-coverage-analyzer"Post-Task Hook
npx claude-flow@alpha hooks post-task \
--task-id "${OPT_ID}" \
--results "${OPT_RESULTS}"Notify Fleet
npx claude-flow@alpha hooks notify \
--message "Optimization: ${TEST_REDUCTION}% fewer tests, ${TIME_SAVED}s saved"Expected Outputs
Success Output
⚡ Optimizing suite... 🧮 Running sublinear optimization algorithm... 📊 Optimization Results: Algorithm: sublinear Objective: coverage-per-test Tests Before: 120 Tests After: 85 Reduction: 29.2% Coverage Before: 93.5% Coverage After: 93.7% Delta: +0.2% Time Saved: 45s per run ✅ Optimization successful! Fewer tests with improved coverage.
Dry-Run Output
🔍 Optimization Preview (D
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Repo: proffesor-for-testing/agentic-qe
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