# Phase 6: Testing, Documentation & Examples
**Status**: 🔵 Not Started
**Estimated Duration**: 3-4 days
**Prerequisites**: Phases 1-4 Complete (Phase 5 optional enhancements can be done before or after)
---
## Quick Reference
**What you'll build**: Comprehensive test suite, API documentation, example workflows, and production deployment setup
**Tasks**: 40+ across 5 modules
- Module 5.1: Test Suite (18+ test files)
- Module 5.2: Documentation (10+ docs)
- Module 5.3: Example Workflows (6 examples)
- Module 5.4: Performance Optimization (4 tasks)
- Module 5.5: Production Readiness (5 tasks)
**Success criteria**: >80% test coverage, all integration tests pass, complete documentation, 6 working examples, Docker deployment ready
**Verification**:
```bash
./verify_phase5.sh # Final comprehensive verification
pytest --cov=gazebo_mcp --cov-report=html # Coverage report
```
**Key deliverables**:
- ✅ Comprehensive test suite (unit + integration)
- ✅ Complete API documentation
- ✅ 6+ example workflows
- ✅ Performance optimization report
- ✅ Docker production deployment
- ✅ v0.1.0 release ready
---
## Learning Objectives
By completing this phase, you will understand:
1. **Test Strategy**
- How to structure unit vs. integration tests
- When to mock vs. use real Gazebo
- How to achieve high test coverage
- Pytest fixtures and conftest patterns
2. **Integration Testing**
- How to manage Gazebo lifecycle in tests
- Test isolation and cleanup
- Timeout handling in async tests
- CI/CD integration patterns
3. **API Documentation**
- How to document MCP tools effectively
- Writing clear usage examples
- Documenting data formats and schemas
- Creating user-friendly guides
4. **Performance Optimization**
- How to profile Python code
- Identifying bottlenecks
- Caching strategies
- Async/await optimization
5. **Production Deployment**
- Docker containerization for ROS2
- Configuration management
- Health checks and monitoring
- Deployment best practices
---
## Core Principles for This Phase
### 1. Test Pyramid
Follow test distribution:
```
/\
/ \ E2E Integration (10%)
/----\
/ \ Integration Tests (30%)
/--------\
/ \ Unit Tests (60%)
/____________\
```
**Unit tests** (fast, isolated):
- Test individual functions
- Mock external dependencies
- Run in milliseconds
- >80% code coverage target
**Integration tests** (slower, real Gazebo):
- Test full workflows
- Use real ROS2 and Gazebo
- Run in seconds
- Cover critical paths
**E2E tests** (slowest, complete scenarios):
- Test entire user workflows
- Real environment setup
- Run in minutes
- Cover main use cases
### 2. Write Integration Test Patterns
**Standard integration test structure**:
```python
import pytest
import asyncio
from gazebo_mcp.server import GazeboMCPServer
@pytest.fixture(scope="module")
async def gazebo_instance():
"""Start Gazebo for integration tests"""
# Check if already running
if not is_gazebo_running():
proc = subprocess.Popen(['gz', 'sim', '-s'])
await wait_for_gazebo_ready(timeout=10.0)
yield proc
proc.terminate()
proc.wait()
else:
yield None
@pytest.fixture
async def mcp_server(gazebo_instance):
"""Provide clean MCP server instance"""
server = GazeboMCPServer()
await server.start()
yield server
await server.stop()
# Clean up any spawned models
await cleanup_all_models()
@pytest.mark.integration
@pytest.mark.asyncio
async def test_full_workflow(mcp_server):
"""Test complete workflow"""
# Arrange: Set up world
await mcp_server.call_tool('create_empty_world', {})
# Act: Spawn robot and control
spawn_result = await mcp_server.call_tool('spawn_model', {
'model_name': 'test_robot',
'model_type': 'turtlebot3_burger'
})
# Assert: Verify success
assert spawn_result['success'] == True
# Cleanup: Fixture handles cleanup
```
### 3. Document for Users, Not Developers
**Good documentation**:
- Starts with "what" and "why"
- Shows examples first
- Explains parameters clearly
- Provides troubleshooting
**Example**:
```markdown
## spawn_model
Spawns a robot or object model in the Gazebo simulation.
### Use Case
Use this to add TurtleBot3 robots or custom objects to your simulation.
### Example
```python
result = await use_mcp_tool("spawn_model", {
"model_name": "my_robot",
"model_type": "turtlebot3_burger",
"x": 0.0,
"y": 0.0,
"z": 0.1
})
```
### Parameters
- `model_name` (string, required): Unique identifier for this instance
- `model_type` (string): Type of model (default: "turtlebot3_burger")
- `x`, `y`, `z` (float): Position in meters (default: 0, 0, 0)
...
```
### 4. Optimize Measurably
**Always benchmark before and after**:
```python
import time
import pytest
@pytest.mark.benchmark
def test_spawn_performance():
"""Benchmark model spawning"""
times = []
for i in range(100):
start = time.time()
result = spawn_model(f"robot_{i}", "turtlebot3_burger")
elapsed = time.time() - start
times.append(elapsed)
avg_time = sum(times) / len(times)
assert avg_time < 0.5, f"Spawn too slow: {avg_time:.3f}s"
print(f"Average spawn time: {avg_time:.3f}s")
print(f"Min: {min(times):.3f}s, Max: {max(times):.3f}s")
```
### 5. Prepare for Production
**Production checklist**:
- [ ] Configuration via environment variables
- [ ] Health check endpoints
- [ ] Logging to stdout (for Docker)
- [ ] Graceful shutdown handling
- [ ] Resource limits documented
- [ ] Security considerations addressed
---
## Overview
Create comprehensive test suite, documentation, and example workflows to ensure quality and usability of the Gazebo MCP Server.
## Objectives
1. Achieve >80% test coverage with unit and integration tests
2. Create comprehensive API documentation
3. Build 6+ example workflows demonstrating capabilities
4. Optimize performance bottlenecks
5. Prepare for production deployment
---
## Module 5.1: Test Suite Development
### Unit Tests (12 test files)
#### Core Tests
- [ ] `tests/test_server.py` - MCP server initialization and lifecycle
- [ ] `tests/test_connection_manager.py` - ROS2 connection management
- [ ] `tests/test_bridge_node.py` - Bridge node communication
- [ ] `tests/test_exceptions.py` - Exception handling
- [ ] `tests/test_logger.py` - Logging functionality
#### Utility Tests
- [ ] `tests/test_validators.py` - Input validation
- [ ] `tests/test_converters.py` - Message conversions
- [ ] `tests/test_geometry.py` - Geometric calculations
- [ ] `tests/test_sdf_generator.py` - SDF generation
#### Tool Tests
- [ ] `tests/test_simulation_control.py` - Simulation tools
- [ ] `tests/test_model_management.py` - Model management tools
- [ ] `tests/test_sensor_tools.py` - Sensor access tools
- [ ] `tests/test_world_generation.py` - World generation tools
- [ ] `tests/test_lighting_tools.py` - Lighting control tools
- [ ] `tests/test_terrain_tools.py` - Terrain tools
- [ ] `tests/test_live_updates.py` - Live update tools
### Integration Tests (6 test files)
- [ ] `tests/integration/test_turtlebot3_spawn.py` - Spawn and control TurtleBot3
- [ ] `tests/integration/test_obstacle_course.py` - Generate and navigate obstacles
- [ ] `tests/integration/test_sensor_access.py` - Read all sensor types
- [ ] `tests/integration/test_world_creation.py` - Complete world generation
- [ ] `tests/integration/test_day_night_cycle.py` - Lighting animation
- [ ] `tests/integration/test_live_manipulation.py` - Real-time world updates
### Test Infrastructure
**File**: `tests/conftest.py`
```python
"""Pytest configuration and fixtures"""
import pytest
from gazebo_mcp.bridge.connection_manager import ConnectionManager
from gazebo_mcp.server import GazeboMCPServer
@pytest.fixture
async def mcp_server():
"""Provide MCP server instance for tests"""
server = GazeboMCPServer()
await server.start()
yield server
await server.stop()
@pytest.fixture
def mock_bridge():
"""Provide mock bridge for unit tests"""
# Mock implementation
pass
@pytest.fixture(scope="session")
def gazebo_running():
"""Check if Gazebo is running for integration tests"""
# Check and skip if not available
pass
```
### Coverage Requirements
- **Target**: >80% code coverage
- **Goal**: >90% code coverage
- **Tools**: pytest-cov
- **Report**: HTML and XML formats
**Command**:
```bash
pytest --cov=gazebo_mcp --cov-report=html --cov-report=xml
```
---
## Module 5.2: Documentation
### API Documentation
**Location**: `docs/api/`
- [ ] **API_REFERENCE.md** - Complete tool reference
- All MCP tools with signatures
- Parameter descriptions
- Return value formats
- Usage examples
- Error codes
- [ ] **SENSOR_DATA_FORMATS.md** - Sensor data structures
- Camera image formats
- LiDAR point cloud format
- IMU data format
- GPS data format
- [ ] **SDF_TEMPLATES.md** - SDF/URDF templates
- Primitive shapes
- TurtleBot3 models
- Custom model guidelines
### User Guides
**Location**: `docs/guides/`
- [ ] **INSTALLATION.md** - Complete setup guide
- ROS2 installation
- Gazebo installation
- Python dependencies
- TurtleBot3 setup
- Troubleshooting
- [ ] **QUICK_START.md** - Getting started tutorial
- First simulation
- Spawn robot
- Basic control
- Read sensors
- [ ] **TURTLEBOT3_GUIDE.md** - TurtleBot3 specific guide
- Model variants
- Sensor configuration
- Navigation setup
- Common patterns
- [ ] **WORLD_BUILDING.md** - World creation guide
- Basic world structure
- Adding objects
- Terrain creation
- Lighting setup
- Best practices
### Development Documentation
- [ ] **CONTRIBUTING.md** - Contribution guidelines
- [ ] **DEVELOPMENT.md** - Developer setup and practices
- [ ] **TESTING.md** - Testing guidelines
- [ ] **CHANGELOG.md** - Version history
---
## Module 5.3: Example Workflows
**Location**: `examples/`
### Example 1: Basic Simulation ✨
**File**: `examples/01_basic_simulation.py`
```python
"""
Example 1: Basic Simulation
Demonstrates:
- Starting Gazebo
- Creating empty world
- Adding ground plane
- Basic lighting
"""
async def main():
# Start simulation
await use_mcp_tool("start_simulation", {
"world_name": "empty_world"
})
print("✓ Simulation started with empty world")
```
- [ ] Create example script
- [ ] Add detailed comments
- [ ] Include expected output
- [ ] Add to documentation
### Example 2: TurtleBot3 Spawn & Control ✨
**File**: `examples/02_turtlebot3_spawn.py`
```python
"""
Example 2: TurtleBot3 Spawn and Control
Demonstrates:
- Spawning TurtleBot3 Burger
- Sending velocity commands
- Reading sensor data (LiDAR)
- Basic navigation
"""
async def main():
# Spawn robot
result = await use_mcp_tool("spawn_model", {
"model_name": "my_robot",
"model_type": "turtlebot3_burger",
"x": 0.0, "y": 0.0, "z": 0.1
})
# Move forward
await use_mcp_tool("send_velocity_command", {
"model_name": "my_robot",
"linear_x": 0.2,
"angular_z": 0.0
})
# Read LiDAR
lidar = await use_mcp_tool("get_sensor_data", {
"model_name": "my_robot",
"sensor_type": "lidar"
})
print(f"✓ LiDAR detected obstacles: {lidar['ranges'][:10]}")
```
- [ ] Create example script
- [ ] Add movement patterns
- [ ] Include sensor visualization
- [ ] Document expected behavior
### Example 3: Obstacle Course Generation ✨
**File**: `examples/03_obstacle_course.py`
- [ ] Create world with obstacles
- [ ] Spawn TurtleBot3
- [ ] Navigate through course
- [ ] Demonstrate collision avoidance
### Example 4: Multi-Terrain Testing ✨
**File**: `examples/04_multi_terrain.py`
- [ ] Create heightmap terrain
- [ ] Set different surface types
- [ ] Test robot on various surfaces
- [ ] Compare sensor readings
### Example 5: Day/Night Cycle ✨
**File**: `examples/05_day_night_cycle.py`
- [ ] Configure lighting cycle
- [ ] Capture camera images at different times
- [ ] Demonstrate lighting effects
- [ ] Show sensor adaptation
### Example 6: Live World Updates ✨
**File**: `examples/06_live_updates.py`
- [ ] Start with basic world
- [ ] Add objects during simulation
- [ ] Apply forces to objects
- [ ] Modify lighting in real-time
- [ ] Demonstrate dynamic scenarios
---
## Module 5.4: Performance Optimization
### Profiling Tasks
- [ ] Profile MCP tool call latency
- [ ] Profile ROS2 message overhead
- [ ] Profile sensor data serialization
- [ ] Identify bottlenecks with py-spy or cProfile
### Optimization Tasks
- [ ] Implement caching for repeated queries
- Model state cache (with TTL)
- Sensor data buffering
- World state cache
- [ ] Optimize message conversions
- Reduce allocations
- Use efficient serialization
- [ ] Add batching for bulk operations
- Spawn multiple models at once
- Bulk object placement
- [ ] Optimize SDF generation
- Template caching
- Pre-generated common models
### Performance Targets
| Operation | Target Latency |
|-----------|----------------|
| Tool call (cached) | < 100ms |
| Spawn model | < 500ms |
| Get sensor data | < 50ms |
| Apply force | < 100ms |
| World generation | 1-5s |
---
## Module 5.5: Production Readiness
### Deployment Tasks
- [ ] Create Docker image
```dockerfile
FROM ros:humble
RUN apt-get update && apt-get install -y \
ros-humble-gazebo-ros-pkgs \
python3-pip
COPY . /workspace
RUN pip install -e /workspace
CMD ["gazebo-mcp-server"]
```
- [ ] Create docker-compose.yaml
- [ ] Create systemd service file
- [ ] Add health check endpoints
- [ ] Document deployment options
### Configuration Management
- [ ] Environment variable support
- [ ] Config file validation
- [ ] Default config templates
- [ ] Configuration documentation
### Monitoring & Logging
- [ ] Add performance metrics logging
- [ ] Add error rate tracking
- [ ] Add usage statistics
- [ ] Create monitoring dashboard (optional)
---
## Documentation Checklist
### Complete Documentation Set
- [x] README.md (Phase 1)
- [x] ARCHITECTURE.md (Phase 1)
- [x] IMPLEMENTATION_PLAN.md (Current)
- [ ] API_REFERENCE.md
- [ ] INSTALLATION.md
- [ ] QUICK_START.md
- [ ] TURTLEBOT3_GUIDE.md
- [ ] WORLD_BUILDING.md
- [ ] CONTRIBUTING.md
- [ ] DEVELOPMENT.md
- [ ] TESTING.md
- [ ] CHANGELOG.md
- [ ] TROUBLESHOOTING.md
---
## Success Criteria
### Automated Verification ✅
Run final verification:
```bash
./verify_phase5.sh # Complete project verification
```
This runs ALL checks:
- [ ] All phases 1-5 tests pass
- [ ] >80% total code coverage (goal: >90%)
- [ ] Type checking passes (mypy --strict)
- [ ] Linting passes (ruff, black)
- [ ] Security scan passes
- [ ] Documentation builds without errors
- [ ] All examples run successfully
### Test Coverage ✅
```bash
pytest --cov=gazebo_mcp --cov-report=html --cov-report=term-missing
```
**Requirements**:
- [ ] Overall coverage >80% (target >90%)
- [ ] Core modules >90% coverage:
- server.py
- connection_manager.py
- bridge_node.py
- [ ] Tool modules >80% coverage:
- simulation_control.py
- model_management.py
- sensor_tools.py
- world_generation.py
- lighting_tools.py
- [ ] Utility modules >85% coverage
- [ ] No critical paths untested
### Integration Tests ✅
All integration tests must pass with real Gazebo:
```bash
# Start Gazebo
gz sim -s &
# Run all integration tests
pytest tests/integration/ -v --tb=short
```
**Must pass**:
- [ ] `test_turtlebot3_spawn.py` - TurtleBot3 workflows
- [ ] `test_obstacle_course.py` - Obstacle generation and navigation
- [ ] `test_sensor_access.py` - All sensor types
- [ ] `test_world_creation.py` - Complete world generation
- [ ] `test_day_night_cycle.py` - Lighting animation
- [ ] `test_live_manipulation.py` - Real-time updates
### Documentation Completeness ✅
All documentation must be complete and accurate:
**API Documentation** (docs/api/):
- [ ] API_REFERENCE.md - All tools documented
- [ ] SENSOR_DATA_FORMATS.md - All formats explained
- [ ] SDF_TEMPLATES.md - Templates with examples
**User Guides** (docs/guides/):
- [ ] INSTALLATION.md - Step-by-step setup
- [ ] QUICK_START.md - Getting started tutorial
- [ ] TURTLEBOT3_GUIDE.md - TurtleBot3 specifics
- [ ] WORLD_BUILDING.md - World creation guide
**Development Docs**:
- [ ] CONTRIBUTING.md - Contribution guidelines
- [ ] DEVELOPMENT.md - Developer setup
- [ ] TESTING.md - Testing guidelines
- [ ] CHANGELOG.md - Version history
**Supporting Docs**:
- [x] TROUBLESHOOTING.md - Common issues (Phase 1)
- [x] ARCHITECTURE.md - System design (Phase 1)
- [x] README.md - Project overview (Phase 1)
### Example Workflows ✅
All 6 examples must run successfully:
```bash
# Test each example
python examples/01_basic_simulation.py
python examples/02_turtlebot3_spawn.py
python examples/03_obstacle_course.py
python examples/04_multi_terrain.py
python examples/05_day_night_cycle.py
python examples/06_live_updates.py
```
**Verification**:
- [ ] Example 1: Basic simulation starts
- [ ] Example 2: TurtleBot3 spawns and moves
- [ ] Example 3: Obstacle course generates and navigates
- [ ] Example 4: Multiple terrain types work
- [ ] Example 5: Day/night cycle animates
- [ ] Example 6: Live updates work during sim
- [ ] All examples have clear output
- [ ] All examples handle errors gracefully
### Performance Targets ✅
Run performance benchmarks:
```bash
pytest tests/performance/ -v
```
**Targets must be met**:
| Operation | Target | Measured | Pass |
|-----------|--------|----------|------|
| MCP tool call (cached) | < 100ms | ___ | [ ] |
| Spawn model | < 500ms | ___ | [ ] |
| Get sensor data | < 50ms | ___ | [ ] |
| Send velocity command | < 100ms | ___ | [ ] |
| World generation | 1-5s | ___ | [ ] |
| Obstacle course (10 objects) | < 5s | ___ | [ ] |
| Day/night transition | < 200ms | ___ | [ ] |
**If targets not met**:
- Profile and identify bottlenecks
- Implement optimizations
- Re-test until targets achieved
### Production Readiness ✅
**Docker Deployment**:
- [ ] Dockerfile builds successfully
- [ ] Docker image runs without errors
- [ ] docker-compose.yaml works
- [ ] Health check endpoint responds
- [ ] Container logs properly
**Configuration**:
- [ ] Environment variable support works
- [ ] Config file validation works
- [ ] Default configs provided
- [ ] Configuration documented
**Quality Assurance**:
- [ ] No TODO/FIXME in production code
- [ ] All deprecation warnings addressed
- [ ] Security scan passes (no high/critical issues)
- [ ] License headers present
- [ ] Version number correct (0.1.0)
### Code Quality Final Check ✅
**CRITICAL**: Final quality standards:
- [ ] **Type Hints**: 100% of functions typed
- [ ] **Docstrings**: 100% of public functions documented
- [ ] **Tests**: >80% coverage (goal >90%)
- [ ] **Linting**: Zero errors, minimal warnings
- [ ] **Type Checking**: Zero errors in strict mode
- [ ] **Security**: No high/critical vulnerabilities
- [ ] **Performance**: All targets met
- [ ] **Documentation**: Complete and accurate
### Release Readiness ✅
**v0.1.0 Release Checklist**:
- [ ] All success criteria above met
- [ ] CHANGELOG.md updated with release notes
- [ ] Version number updated in all files
- [ ] Git tags created (v0.1.0)
- [ ] Release notes written
- [ ] Installation tested on clean system
- [ ] README.md reflects current state
- [ ] License file present and correct
- [ ] Contributing guide complete
- [ ] Examples tested end-to-end
---
## Final Deliverables
### 1. Test Suite ✅
- 18+ test files (unit + integration)
- >80% code coverage
- All tests passing
- Performance benchmarks included
### 2. Documentation ✅
- Complete API reference
- User guides (installation, quick start, etc.)
- Development documentation
- Troubleshooting guide
### 3. Examples ✅
- 6 working example scripts
- Clear, commented code
- Expected output documented
- Error handling demonstrated
### 4. Performance Report ✅
- Benchmark results
- Optimization recommendations
- Profiling data
- Performance targets met
### 5. Docker Deployment ✅
- Production-ready Dockerfile
- docker-compose.yaml
- Deployment documentation
- Health check implementation
### 6. Release Package ✅
- v0.1.0 tagged in git
- Release notes published
- Installation verified
- All deliverables complete
---
## Best Practices Summary
**DO** ✅:
- Follow test pyramid (60% unit, 30% integration, 10% E2E)
- Write integration test fixtures properly
- Document for users, not developers
- Benchmark before and after optimization
- Test examples end-to-end
- Verify production deployment
- Complete all documentation
**DON'T** ❌:
- Skip integration tests
- Ignore performance targets
- Write documentation without examples
- Skip Docker testing
- Leave TODOs in production code
- Forget to test examples
- Rush the release checklist
---
**Estimated Completion**: 3-4 days
**Priority**: HIGH - Required for v0.1.0 release
---
## Post-Release Tasks (Optional)
After v0.1.0 release:
1. **CI/CD Pipeline** - Set up GitHub Actions
2. **Package Distribution** - Publish to PyPI
3. **Video Tutorials** - Create demonstration videos
4. **Blog Post** - Write announcement post
5. **Community** - Set up discussion forums
6. **Monitoring** - Add telemetry (opt-in)
7. **Future Features** - Plan v0.2.0 roadmap
