# Quality Assurance Standards

## Document Information
- **Version**: 1.0
- **Date**: 2025-09-09
- **Status**: Active
- **Classification**: Internal

## 1. Quality Principles

### 1.1 Core Quality Principles
- **Prevention Over Detection**: Build quality in from the start rather than test quality in
- **Continuous Quality**: Quality validation at every stage of development
- **Risk-Based Testing**: Focus testing efforts on high-risk areas
- **AI-Enhanced Validation**: Leverage AI tools for comprehensive quality assessment
- **Customer-Centric Quality**: Quality measured by user satisfaction and business value
- **Data-Driven Decisions**: Use metrics and analytics to guide quality improvements
- **Collaborative Quality**: Quality is everyone's responsibility, not just QA

### 1.2 Quality Standards Framework
```python
from enum import Enum
from typing import Dict, Any, List, Optional
from dataclasses import dataclass

class QualityLevel(Enum):
    CRITICAL = "critical"
    HIGH = "high" 
    MEDIUM = "medium"
    LOW = "low"

@dataclass
class QualityStandard:
    """Definition of a quality standard."""
    name: str
    description: str
    level: QualityLevel
    acceptance_criteria: List[str]
    measurement_method: str
    target_value: Any
    validation_rules: List[str]

class QualityStandardsRegistry:
    """Registry for all quality standards."""
    
    def __init__(self):
        self.standards: Dict[str, QualityStandard] = {}
        self.load_default_standards()
    
    def load_default_standards(self):
        """Load default quality standards for the Chat App."""
        
        # Performance Standards
        self.register_standard(QualityStandard(
            name="response_time",
            description="LLM response time performance",
            level=QualityLevel.HIGH,
            acceptance_criteria=[
                "95% of LLM requests complete within 5 seconds",
                "99% of LLM requests complete within 10 seconds",
                "Terminal rendering maintains 20 FPS minimum"
            ],
            measurement_method="Performance monitoring and load testing",
            target_value={"p95": 5.0, "p99": 10.0, "fps": 20},
            validation_rules=[
                "Automated performance tests must pass",
                "Load testing with 100 concurrent users",
                "Memory usage under 100MB during normal operation"
            ]
        ))
        
        # Reliability Standards
        self.register_standard(QualityStandard(
            name="system_reliability",
            description="System uptime and error rates",
            level=QualityLevel.CRITICAL,
            acceptance_criteria=[
                "99.9% uptime during business hours",
                "Error rate below 0.1% for all operations",
                "Zero data loss tolerance",
                "Graceful degradation on component failure"
            ],
            measurement_method="Monitoring and alerting systems",
            target_value={"uptime": 99.9, "error_rate": 0.001},
            validation_rules=[
                "Chaos engineering tests pass",
                "Failure scenarios tested and documented",
                "Recovery procedures validated"
            ]
        ))
        
        # Security Standards
        self.register_standard(QualityStandard(
            name="security_compliance",
            description="Security vulnerability and compliance",
            level=QualityLevel.CRITICAL,
            acceptance_criteria=[
                "Zero high-severity security vulnerabilities",
                "All data encrypted in transit and at rest",
                "Authentication and authorization implemented",
                "Security audit trail maintained"
            ],
            measurement_method="Security scanning and penetration testing",
            target_value={"high_vulnerabilities": 0, "medium_vulnerabilities": 0},
            validation_rules=[
                "OWASP Top 10 compliance verified",
                "Dependency vulnerability scanning clean",
                "Penetration testing passed"
            ]
        ))
    
    def register_standard(self, standard: QualityStandard):
        """Register a new quality standard."""
        self.standards[standard.name] = standard
    
    def get_standard(self, name: str) -> Optional[QualityStandard]:
        """Retrieve a quality standard by name.""" 
        return self.standards.get(name)
```

## 2. Testing Standards

### 2.1 Test Strategy Framework
```python
import unittest
from unittest.mock import Mock, patch, AsyncMock
from typing import Dict, List, Any, Callable
import asyncio
import time

class TestLevel(Enum):
    UNIT = "unit"
    INTEGRATION = "integration"
    SYSTEM = "system"
    ACCEPTANCE = "acceptance"

class TestCategory(Enum):
    FUNCTIONAL = "functional"
    PERFORMANCE = "performance"
    SECURITY = "security"
    USABILITY = "usability"
    RELIABILITY = "reliability"

@dataclass
class TestSpecification:
    """Specification for a test case."""
    test_id: str
    name: str
    description: str
    level: TestLevel
    category: TestCategory
    priority: QualityLevel
    preconditions: List[str]
    test_steps: List[str]
    expected_results: List[str]
    acceptance_criteria: List[str]

class ComprehensiveTestSuite:
    """Base class for comprehensive test suites."""
    
    def __init__(self):
        self.test_specifications: Dict[str, TestSpecification] = {}
        self.test_results: Dict[str, Any] = {}
        self.ai_assistant = None  # Injected AI assistant for validation
    
    def register_test_specification(self, spec: TestSpecification):
        """Register a test specification."""
        self.test_specifications[spec.test_id] = spec
    
    async def run_comprehensive_test_suite(self) -> Dict[str, Any]:
        """Run all registered tests with comprehensive reporting."""
        
        suite_results = {
            "start_time": time.time(),
            "total_tests": len(self.test_specifications),
            "passed": 0,
            "failed": 0,
            "skipped": 0,
            "test_results": {},
            "quality_gates": {},
            "recommendations": []
        }
        
        # Run tests by priority (CRITICAL first)
        for priority in [QualityLevel.CRITICAL, QualityLevel.HIGH, QualityLevel.MEDIUM, QualityLevel.LOW]:
            priority_tests = [
                spec for spec in self.test_specifications.values() 
                if spec.priority == priority
            ]
            
            for test_spec in priority_tests:
                result = await self.execute_test_specification(test_spec)
                suite_results["test_results"][test_spec.test_id] = result
                
                if result["passed"]:
                    suite_results["passed"] += 1
                elif result["skipped"]:
                    suite_results["skipped"] += 1
                else:
                    suite_results["failed"] += 1
                    
                    # Critical test failure stops execution
                    if test_spec.priority == QualityLevel.CRITICAL and not result["passed"]:
                        suite_results["early_termination"] = True
                        break
        
        suite_results["end_time"] = time.time()
        suite_results["duration"] = suite_results["end_time"] - suite_results["start_time"]
        
        # Generate quality gates assessment
        suite_results["quality_gates"] = await self.assess_quality_gates(suite_results)
        
        # Generate AI-powered recommendations
        if self.ai_assistant:
            suite_results["recommendations"] = await self.generate_ai_recommendations(suite_results)
        
        return suite_results
    
    async def execute_test_specification(self, spec: TestSpecification) -> Dict[str, Any]:
        """Execute a single test specification."""
        
        result = {
            "test_id": spec.test_id,
            "name": spec.name,
            "start_time": time.time(),
            "passed": False,
            "skipped": False,
            "error_message": None,
            "performance_metrics": {},
            "coverage_data": {},
            "validation_results": []
        }
        
        try:
            # Check preconditions
            precondition_check = await self.validate_preconditions(spec.preconditions)
            if not precondition_check["valid"]:
                result["skipped"] = True
                result["skip_reason"] = f"Preconditions not met: {precondition_check['failures']}"
                return result
            
            # Execute test steps
            test_execution_result = await self.execute_test_steps(spec)
            result["passed"] = test_execution_result["success"]
            result["performance_metrics"] = test_execution_result.get("metrics", {})
            result["validation_results"] = test_execution_result.get("validations", [])
            
            if not result["passed"]:
                result["error_message"] = test_execution_result.get("error", "Test failed")
        
        except Exception as e:
            result["passed"] = False
            result["error_message"] = str(e)
            result["exception_details"] = {
                "type": type(e).__name__,
                "traceback": traceback.format_exc()
            }
        
        result["end_time"] = time.time()
        result["duration"] = result["end_time"] - result["start_time"]
        
        return result
```

### 2.2 Unit Testing Standards
```python
class ChatAppUnitTestCase(unittest.TestCase):
    """Base class for Chat App unit tests with enhanced validation."""
    
    def setUp(self):
        """Enhanced setup with AI validation support."""
        self.mock_event_bus = Mock()
        self.mock_state_manager = Mock()
        self.mock_config = Mock()
        self.mock_renderer = Mock()
        self.test_data = self.load_test_data()
        self.ai_validator = AITestValidator() if hasattr(self, 'ai_validator') else None
    
    def assertEventBusUsage(self, component, expected_events: List[str]):
        """Assert that component properly uses EventBus for expected events."""
        
        # Verify EventBus dependency
        self.assertTrue(hasattr(component, 'event_bus'), 
                       "Component must have event_bus attribute")
        
        # Check hook registration
        self.assertTrue(hasattr(component, 'register_hooks') and 
                       callable(component.register_hooks),
                       "Component must implement register_hooks method")
        
        # Verify expected events are handled
        for event_type in expected_events:
            hooks = self.mock_event_bus.hooks.get(event_type, [])
            self.assertTrue(len(hooks) > 0,
                           f"Component should register hooks for {event_type}")
    
    def assertAsyncCompliance(self, method):
        """Assert that method follows async/await patterns."""
        self.assertTrue(asyncio.iscoroutinefunction(method),
                       f"Method {method.__name__} should be async")
    
    def assertErrorHandling(self, method, exception_type: type):
        """Assert that method properly handles specific exception types."""
        with self.assertRaises(exception_type):
            if asyncio.iscoroutinefunction(method):
                asyncio.run(method())
            else:
                method()
    
    async def assertPerformanceRequirement(self, operation: Callable, max_duration: float):
        """Assert that operation completes within performance requirements."""
        start_time = time.time()
        
        if asyncio.iscoroutinefunction(operation):
            await operation()
        else:
            operation()
        
        duration = time.time() - start_time
        self.assertLess(duration, max_duration,
                       f"Operation took {duration:.3f}s, expected < {max_duration}s")
    
    async def assertMemoryEfficiency(self, operation: Callable, max_memory_mb: float):
        """Assert that operation uses memory efficiently."""
        import psutil
        import gc
        
        process = psutil.Process()
        
        # Baseline memory
        gc.collect()
        baseline_memory = process.memory_info().rss / 1024 / 1024
        
        # Execute operation
        if asyncio.iscoroutinefunction(operation):
            await operation()
        else:
            operation()
        
        # Check memory usage
        gc.collect()
        current_memory = process.memory_info().rss / 1024 / 1024
        memory_increase = current_memory - baseline_memory
        
        self.assertLess(memory_increase, max_memory_mb,
                       f"Memory increase {memory_increase:.2f}MB, expected < {max_memory_mb}MB")

# Example comprehensive unit test
class TestLLMPlugin(ChatAppUnitTestCase):
    """Comprehensive unit tests for LLM Plugin."""
    
    def setUp(self):
        super().setUp()
        self.llm_plugin = LLMPlugin(
            self.mock_state_manager,
            self.mock_event_bus,
            self.mock_renderer,
            self.mock_config
        )
    
    def test_plugin_interface_compliance(self):
        """Test that LLM Plugin implements required interface."""
        required_methods = ['initialize', 'register_hooks', 'get_status_line', 'shutdown']
        
        for method_name in required_methods:
            self.assertTrue(hasattr(self.llm_plugin, method_name),
                           f"Plugin must implement {method_name}")
            self.assertTrue(callable(getattr(self.llm_plugin, method_name)),
                           f"{method_name} must be callable")
    
    async def test_llm_request_processing_performance(self):
        """Test LLM request processing meets performance requirements."""
        
        test_request = {
            "model": "test-model",
            "messages": [{"role": "user", "content": "Test message"}]
        }
        
        # Mock HTTP response
        mock_response = Mock()
        mock_response.json.return_value = {
            "choices": [{"message": {"content": "Test response"}}]
        }
        
        with patch('aiohttp.ClientSession.post', return_value=mock_response):
            # Assert performance requirement (5 second max)
            await self.assertPerformanceRequirement(
                lambda: self.llm_plugin.process_llm_request(test_request),
                max_duration=5.0
            )
    
    async def test_memory_efficiency_during_processing(self):
        """Test memory efficiency during LLM processing."""
        
        large_test_data = {
            "messages": [{"role": "user", "content": "x" * 10000}] * 100
        }
        
        # Assert memory efficiency (10MB max increase)
        await self.assertMemoryEfficiency(
            lambda: self.llm_plugin.process_large_request(large_test_data),
            max_memory_mb=10.0
        )
    
    def test_thinking_tags_processing(self):
        """Test processing of <think> tags in LLM responses."""
        
        test_response = """
        <think>
        This is internal reasoning that should be displayed with special formatting.
        It should be processed and shown to the user differently than final response.
        </think>
        
        This is the final response that should be displayed normally.
        """
        
        result = self.llm_plugin.process_thinking_tags(test_response)
        
        # Verify thinking content is extracted
        self.assertIn("thinking_content", result)
        self.assertIn("internal reasoning", result["thinking_content"])
        
        # Verify final response is clean
        self.assertIn("final_response", result)
        self.assertNotIn("<think>", result["final_response"])
        self.assertIn("final response", result["final_response"])
    
    def test_error_handling_network_failures(self):
        """Test error handling for network failures."""
        
        # Test various network error scenarios
        network_errors = [
            aiohttp.ClientError("Connection failed"),
            aiohttp.TimeoutError("Request timeout"),
            aiohttp.ClientResponseError(None, None, status=500)
        ]
        
        for error in network_errors:
            with self.subTest(error=error):
                with patch('aiohttp.ClientSession.post', side_effect=error):
                    result = asyncio.run(
                        self.llm_plugin.process_llm_request({"test": "data"})
                    )
                    
                    # Should handle error gracefully
                    self.assertIsNotNone(result)
                    self.assertIn("error", result)
                    self.assertIsInstance(result["error"], str)
```

### 2.3 Integration Testing Standards
```python
class ChatAppIntegrationTest(unittest.TestCase):
    """Integration testing base class with comprehensive validation."""
    
    def setUp(self):
        """Set up integration test environment."""
        self.test_environment = self.create_test_environment()
        self.event_bus = EventBus()
        self.state_manager = StateManager(":memory:")  # In-memory database for testing
        self.config_manager = ConfigManager()
        self.plugin_registry = PluginRegistry()
        
    def create_test_environment(self) -> Dict[str, Any]:
        """Create isolated test environment."""
        return {
            "temp_dir": tempfile.mkdtemp(),
            "test_config": self.load_test_config(),
            "mock_services": {},
            "test_data": self.load_integration_test_data()
        }
    
    async def test_complete_event_flow(self):
        """Test complete event flow through the system."""
        
        # Initialize components
        await self.initialize_test_components()
        
        # Create test event
        test_event = Event(
            event_type="USER_INPUT",
            data={"message": "Hello, world!", "user_id": "test_user"},
            source="integration_test"
        )
        
        # Track event processing
        event_tracker = EventTracker()
        self.event_bus.add_event_listener(event_tracker.track_event)
        
        # Emit event and wait for processing
        result = await self.event_bus.emit_event(test_event)
        
        # Verify event flow
        self.assertTrue(result.success, "Event processing should succeed")
        
        # Verify all expected hooks were called
        expected_hook_types = ["PRE", "MAIN", "POST"]
        for hook_type in expected_hook_types:
            self.assertTrue(
                event_tracker.hook_called(hook_type),
                f"{hook_type} hooks should be called"
            )
        
        # Verify final state
        final_state = await self.state_manager.get_current_state()
        self.assertIsNotNone(final_state.get("last_user_input"))
        self.assertEqual(final_state["last_user_input"]["message"], "Hello, world!")
    
    async def test_plugin_system_integration(self):
        """Test plugin discovery, loading, and integration."""
        
        # Create test plugin
        test_plugin_code = """
class TestIntegrationPlugin:
    def __init__(self, state_manager, event_bus, renderer, config):
        self.state_manager = state_manager
        self.event_bus = event_bus
        self.renderer = renderer
        self.config = config
        self.initialized = False
    
    async def initialize(self):
        self.initialized = True
    
    async def register_hooks(self):
        await self.event_bus.register_hook(
            "TEST_EVENT", self.handle_test_event, priority=100
        )
    
    async def handle_test_event(self, event):
        event.data["processed_by"] = "TestIntegrationPlugin"
        return {"status": "processed"}
    
    def get_status_line(self):
        return "Integration Test Plugin: Active" if self.initialized else "Not Initialized"
    
    async def shutdown(self):
        self.initialized = False
    
    @staticmethod
    def get_default_config():
        return {"test_setting": "test_value"}
"""
        
        # Write test plugin to temporary file
        plugin_path = os.path.join(self.test_environment["temp_dir"], "test_integration_plugin.py")
        with open(plugin_path, 'w') as f:
            f.write(test_plugin_code)
        
        # Add temp directory to plugin search path
        self.plugin_registry.add_plugin_directory(self.test_environment["temp_dir"])
        
        # Discover and load plugin
        discovered_plugins = await self.plugin_registry.discover_plugins()
        self.assertIn("test_integration_plugin", discovered_plugins)
        
        # Load and initialize plugin
        load_success = await self.plugin_registry.load_plugin("test_integration_plugin")
        self.assertTrue(load_success, "Plugin should load successfully")
        
        # Verify plugin is active
        plugin = self.plugin_registry.get_plugin("test_integration_plugin")
        self.assertIsNotNone(plugin)
        self.assertTrue(plugin.initialized)
        
        # Test plugin hook processing
        test_event = Event("TEST_EVENT", {"test": "data"}, "integration_test")
        result = await self.event_bus.emit_event(test_event)
        
        self.assertTrue(result.success)
        self.assertEqual(test_event.data["processed_by"], "TestIntegrationPlugin")
    
    async def test_error_propagation_and_recovery(self):
        """Test error propagation and system recovery."""
        
        # Create failing plugin
        class FailingPlugin:
            def __init__(self, state_manager, event_bus, renderer, config):
                pass
            
            async def initialize(self):
                pass
            
            async def register_hooks(self):
                await self.event_bus.register_hook(
                    "ERROR_TEST_EVENT", self.failing_handler, priority=100
                )
            
            async def failing_handler(self, event):
                raise RuntimeError("Intentional test failure")
            
            def get_status_line(self):
                return "Failing Plugin"
            
            async def shutdown(self):
                pass
        
        # Register failing plugin
        failing_plugin = FailingPlugin(
            self.state_manager, self.event_bus, self.renderer, self.config_manager
        )
        await failing_plugin.register_hooks()
        
        # Test error handling
        error_event = Event("ERROR_TEST_EVENT", {"test": "error"}, "error_test")
        result = await self.event_bus.emit_event(error_event)
        
        # System should continue operating despite plugin failure
        self.assertIsNotNone(result)
        # Error should be logged but not crash the system
        
        # Verify system recovery
        recovery_event = Event("USER_INPUT", {"message": "recovery test"}, "recovery_test")
        recovery_result = await self.event_bus.emit_event(recovery_event)
        self.assertTrue(recovery_result.success, "System should recover from plugin failures")
```

### 2.4 AI-Enhanced Testing
```python
class AITestValidator:
    """AI-powered test validation and enhancement."""
    
    def __init__(self, ai_assistant):
        self.ai_assistant = ai_assistant
        self.test_patterns = self.load_test_patterns()
    
    async def validate_test_coverage(self, test_suite: str, source_code: str) -> Dict[str, Any]:
        """Use AI to validate test coverage completeness."""
        
        validation_prompt = f"""
        Analyze this test suite and source code for comprehensive test coverage:
        
        SOURCE CODE:
        {source_code}
        
        TEST SUITE:
        {test_suite}
        
        Evaluate:
        1. Functional coverage - are all methods tested?
        2. Edge case coverage - are boundary conditions tested?
        3. Error path coverage - are error conditions tested?
        4. Integration coverage - are component interactions tested?
        5. Performance coverage - are performance requirements validated?
        6. Security coverage - are security aspects tested?
        
        Provide specific gaps and recommendations for improvement.
        Return results as JSON with coverage percentage and missing areas.
        """
        
        result = await self.ai_assistant.analyze(validation_prompt)
        return self.parse_coverage_analysis(result)
    
    async def generate_test_cases(self, component_specification: Dict) -> List[Dict]:
        """Generate comprehensive test cases using AI."""
        
        generation_prompt = f"""
        Generate comprehensive test cases for this component:
        
        COMPONENT SPECIFICATION:
        {json.dumps(component_specification, indent=2)}
        
        Generate test cases covering:
        1. Happy path scenarios
        2. Edge cases and boundary conditions
        3. Error conditions and exception handling
        4. Performance scenarios
        5. Security test cases
        6. Integration scenarios
        
        For each test case provide:
        - Test name and description
        - Preconditions
        - Test steps
        - Expected results
        - Assertions to verify
        - Test data requirements
        
        Return as JSON array of test case specifications.
        """
        
        result = await self.ai_assistant.analyze(generation_prompt)
        return self.parse_generated_test_cases(result)
    
    async def analyze_test_failures(self, test_results: Dict) -> Dict[str, Any]:
        """Analyze test failures and provide improvement recommendations."""
        
        analysis_prompt = f"""
        Analyze these test failure results and provide recommendations:
        
        TEST RESULTS:
        {json.dumps(test_results, indent=2)}
        
        For each failure, analyze:
        1. Root cause of the failure
        2. Whether it's a test issue or code issue
        3. Impact on overall system quality
        4. Recommended fixes
        5. Prevention strategies
        
        Also provide:
        - Overall test suite health assessment
        - Patterns in failures
        - Recommendations for test improvement
        - Risk assessment for production deployment
        
        Return analysis as structured JSON.
        """
        
        result = await self.ai_assistant.analyze(analysis_prompt)
        return self.parse_failure_analysis(result)
    
    async def optimize_test_suite(self, test_performance_data: Dict) -> Dict[str, Any]:
        """Optimize test suite performance using AI analysis."""
        
        optimization_prompt = f"""
        Analyze this test performance data and suggest optimizations:
        
        PERFORMANCE DATA:
        {json.dumps(test_performance_data, indent=2)}
        
        Identify:
        1. Slow-running tests and optimization opportunities
        2. Redundant test cases that could be consolidated
        3. Parallel execution opportunities
        4. Test data optimization possibilities
        5. Infrastructure improvements
        
        Provide specific recommendations with expected performance improvements.
        """
        
        result = await self.ai_assistant.analyze(optimization_prompt)
        return self.parse_optimization_recommendations(result)
```

## 3. Code Quality Standards

### 3.1 Code Quality Metrics
```python
import ast
from typing import Dict, Any, List
import radon.complexity as radon_complexity
import radon.metrics as radon_metrics

class CodeQualityAnalyzer:
    """Comprehensive code quality analysis."""
    
    def __init__(self):
        self.quality_thresholds = {
            "cyclomatic_complexity": 10,
            "cognitive_complexity": 15,
            "maintainability_index": 20,
            "lines_of_code_per_function": 50,
            "function_parameters": 5,
            "class_methods": 20,
            "inheritance_depth": 5,
            "code_duplication": 0.05  # 5% max
        }
    
    def analyze_code_quality(self, file_path: str) -> Dict[str, Any]:
        """Analyze code quality for a Python file."""
        
        with open(file_path, 'r') as file:
            code = file.read()
        
        # Parse AST
        try:
            tree = ast.parse(code)
        except SyntaxError as e:
            return {
                "error": f"Syntax error: {str(e)}",
                "quality_score": 0,
                "violations": [{"type": "syntax_error", "message": str(e)}]
            }
        
        analysis_result = {
            "file_path": file_path,
            "metrics": {},
            "violations": [],
            "quality_score": 100,
            "recommendations": []
        }
        
        # Calculate various metrics
        analysis_result["metrics"] = {
            "cyclomatic_complexity": self.calculate_cyclomatic_complexity(code),
            "maintainability_index": self.calculate_maintainability_index(code),
            "lines_of_code": len(code.splitlines()),
            "function_metrics": self.analyze_functions(tree),
            "class_metrics": self.analyze_classes(tree),
            "import_analysis": self.analyze_imports(tree)
        }
        
        # Check violations
        analysis_result["violations"] = self.check_quality_violations(analysis_result["metrics"])
        
        # Calculate quality score
        analysis_result["quality_score"] = self.calculate_quality_score(
            analysis_result["metrics"], 
            analysis_result["violations"]
        )
        
        # Generate recommendations
        analysis_result["recommendations"] = self.generate_quality_recommendations(
            analysis_result["violations"]
        )
        
        return analysis_result
    
    def calculate_cyclomatic_complexity(self, code: str) -> Dict[str, Any]:
        """Calculate cyclomatic complexity for all functions."""
        try:
            complexity_data = radon_complexity.cc_visit(code)
            return {
                "average": sum(item.complexity for item in complexity_data) / len(complexity_data) if complexity_data else 0,
                "max": max((item.complexity for item in complexity_data), default=0),
                "functions": [
                    {
                        "name": item.name,
                        "complexity": item.complexity,
                        "line": item.lineno
                    }
                    for item in complexity_data
                ]
            }
        except Exception:
            return {"average": 0, "max": 0, "functions": []}
    
    def analyze_functions(self, tree: ast.AST) -> Dict[str, Any]:
        """Analyze function-level metrics."""
        functions = []
        
        class FunctionVisitor(ast.NodeVisitor):
            def visit_FunctionDef(self, node):
                function_info = {
                    "name": node.name,
                    "line": node.lineno,
                    "parameters": len(node.args.args),
                    "lines_of_code": self.count_lines(node),
                    "docstring": ast.get_docstring(node) is not None,
                    "async": isinstance(node, ast.AsyncFunctionDef),
                    "decorators": len(node.decorator_list),
                    "complexity_indicators": self.analyze_function_complexity(node)
                }
                functions.append(function_info)
                self.generic_visit(node)
            
            def visit_AsyncFunctionDef(self, node):
                self.visit_FunctionDef(node)
            
            def count_lines(self, node):
                return len(ast.dump(node).splitlines())
            
            def analyze_function_complexity(self, node):
                """Analyze complexity indicators within function."""
                indicators = {
                    "nested_loops": 0,
                    "conditional_statements": 0,
                    "exception_handlers": 0
                }
                
                class ComplexityVisitor(ast.NodeVisitor):
                    def visit_For(self, n):
                        indicators["nested_loops"] += 1
                        self.generic_visit(n)
                    
                    def visit_While(self, n):
                        indicators["nested_loops"] += 1
                        self.generic_visit(n)
                    
                    def visit_If(self, n):
                        indicators["conditional_statements"] += 1
                        self.generic_visit(n)
                    
                    def visit_ExceptHandler(self, n):
                        indicators["exception_handlers"] += 1
                        self.generic_visit(n)
                
                ComplexityVisitor().visit(node)
                return indicators
        
        visitor = FunctionVisitor()
        visitor.visit(tree)
        
        return {
            "total_functions": len(functions),
            "average_parameters": sum(f["parameters"] for f in functions) / len(functions) if functions else 0,
            "average_loc": sum(f["lines_of_code"] for f in functions) / len(functions) if functions else 0,
            "functions_with_docstrings": sum(1 for f in functions if f["docstring"]),
            "async_functions": sum(1 for f in functions if f["async"]),
            "functions": functions
        }
    
    def check_quality_violations(self, metrics: Dict[str, Any]) -> List[Dict[str, Any]]:
        """Check for quality standard violations."""
        violations = []
        
        # Cyclomatic complexity violations
        if metrics["cyclomatic_complexity"]["max"] > self.quality_thresholds["cyclomatic_complexity"]:
            violations.append({
                "type": "high_cyclomatic_complexity",
                "severity": "high",
                "message": f"Maximum cyclomatic complexity {metrics['cyclomatic_complexity']['max']} exceeds threshold {self.quality_thresholds['cyclomatic_complexity']}",
                "affected_functions": [
                    f["name"] for f in metrics["cyclomatic_complexity"]["functions"]
                    if f["complexity"] > self.quality_thresholds["cyclomatic_complexity"]
                ]
            })
        
        # Function parameter violations
        for function in metrics["function_metrics"]["functions"]:
            if function["parameters"] > self.quality_thresholds["function_parameters"]:
                violations.append({
                    "type": "too_many_parameters",
                    "severity": "medium",
                    "message": f"Function '{function['name']}' has {function['parameters']} parameters, exceeds threshold {self.quality_thresholds['function_parameters']}",
                    "function": function["name"],
                    "line": function["line"]
                })
        
        # Function length violations
        for function in metrics["function_metrics"]["functions"]:
            if function["lines_of_code"] > self.quality_thresholds["lines_of_code_per_function"]:
                violations.append({
                    "type": "function_too_long",
                    "severity": "medium", 
                    "message": f"Function '{function['name']}' has {function['lines_of_code']} lines, exceeds threshold {self.quality_thresholds['lines_of_code_per_function']}",
                    "function": function["name"],
                    "line": function["line"]
                })
        
        # Documentation violations
        functions_without_docs = [
            f for f in metrics["function_metrics"]["functions"] 
            if not f["docstring"] and not f["name"].startswith("_")
        ]
        if functions_without_docs:
            violations.append({
                "type": "missing_documentation",
                "severity": "low",
                "message": "Public functions missing docstrings",
                "affected_functions": [f["name"] for f in functions_without_docs]
            })
        
        return violations
    
    def calculate_quality_score(self, metrics: Dict[str, Any], violations: List[Dict]) -> float:
        """Calculate overall quality score (0-100)."""
        base_score = 100
        
        # Deduct points based on violations
        for violation in violations:
            if violation["severity"] == "high":
                base_score -= 15
            elif violation["severity"] == "medium":
                base_score -= 10
            elif violation["severity"] == "low":
                base_score -= 5
        
        # Bonus points for good practices
        if metrics["function_metrics"]["functions_with_docstrings"] > 0:
            documentation_ratio = (
                metrics["function_metrics"]["functions_with_docstrings"] / 
                metrics["function_metrics"]["total_functions"]
            )
            base_score += min(10, documentation_ratio * 10)
        
        return max(0, min(100, base_score))
```

### 3.2 Automated Code Quality Gates
```python
class QualityGate:
    """Automated quality gate for CI/CD pipeline."""
    
    def __init__(self):
        self.quality_analyzer = CodeQualityAnalyzer()
        self.test_runner = ComprehensiveTestSuite()
        self.security_scanner = SecurityScanner()
        self.performance_analyzer = PerformanceAnalyzer()
    
    async def evaluate_quality_gate(self, changed_files: List[str]) -> Dict[str, Any]:
        """Evaluate all quality criteria for changed files."""
        
        gate_results = {
            "overall_passed": True,
            "gate_score": 100,
            "evaluations": {
                "code_quality": {},
                "test_coverage": {},
                "security_scan": {},
                "performance_check": {}
            },
            "blocking_issues": [],
            "warnings": [],
            "recommendations": []
        }
        
        # Code Quality Analysis
        code_quality_results = await self.evaluate_code_quality(changed_files)
        gate_results["evaluations"]["code_quality"] = code_quality_results
        
        if code_quality_results["average_score"] < 80:
            gate_results["overall_passed"] = False
            gate_results["blocking_issues"].append({
                "category": "code_quality",
                "message": f"Code quality score {code_quality_results['average_score']:.1f} below threshold 80",
                "details": code_quality_results["violations"]
            })
        
        # Test Coverage Analysis
        test_coverage_results = await self.evaluate_test_coverage(changed_files)
        gate_results["evaluations"]["test_coverage"] = test_coverage_results
        
        if test_coverage_results["coverage_percentage"] < 90:
            gate_results["overall_passed"] = False
            gate_results["blocking_issues"].append({
                "category": "test_coverage",
                "message": f"Test coverage {test_coverage_results['coverage_percentage']:.1f}% below threshold 90%",
                "details": test_coverage_results["uncovered_lines"]
            })
        
        # Security Scan
        security_results = await self.evaluate_security(changed_files)
        gate_results["evaluations"]["security_scan"] = security_results
        
        if security_results["high_severity_issues"] > 0:
            gate_results["overall_passed"] = False
            gate_results["blocking_issues"].append({
                "category": "security",
                "message": f"Found {security_results['high_severity_issues']} high-severity security issues",
                "details": security_results["issues"]
            })
        
        # Performance Check
        performance_results = await self.evaluate_performance(changed_files)
        gate_results["evaluations"]["performance_check"] = performance_results
        
        if performance_results["performance_regression"]:
            gate_results["warnings"].append({
                "category": "performance",
                "message": "Performance regression detected",
                "details": performance_results["regression_details"]
            })
        
        # Calculate overall gate score
        gate_results["gate_score"] = self.calculate_gate_score(gate_results["evaluations"])
        
        # Generate recommendations
        gate_results["recommendations"] = self.generate_gate_recommendations(gate_results)
        
        return gate_results
    
    async def evaluate_code_quality(self, files: List[str]) -> Dict[str, Any]:
        """Evaluate code quality for all files."""
        
        file_results = []
        total_score = 0
        
        for file_path in files:
            if file_path.endswith('.py'):
                result = self.quality_analyzer.analyze_code_quality(file_path)
                file_results.append(result)
                total_score += result["quality_score"]
        
        return {
            "files_analyzed": len(file_results),
            "average_score": total_score / len(file_results) if file_results else 100,
            "file_results": file_results,
            "violations": [v for result in file_results for v in result["violations"]],
            "recommendations": [r for result in file_results for r in result["recommendations"]]
        }
    
    async def evaluate_test_coverage(self, files: List[str]) -> Dict[str, Any]:
        """Evaluate test coverage for changed files."""
        
        # Run tests with coverage
        coverage_result = await self.run_tests_with_coverage(files)
        
        return {
            "coverage_percentage": coverage_result["coverage_percentage"],
            "lines_covered": coverage_result["lines_covered"],
            "lines_total": coverage_result["lines_total"],
            "uncovered_lines": coverage_result["uncovered_lines"],
            "branch_coverage": coverage_result.get("branch_coverage", 0),
            "test_results": coverage_result["test_results"]
        }
    
    def generate_quality_report(self, gate_results: Dict[str, Any]) -> str:
        """Generate comprehensive quality report."""
        
        report = f"""
# Quality Gate Report

## Overall Result: {'✅ PASSED' if gate_results['overall_passed'] else '❌ FAILED'}
**Gate Score: {gate_results['gate_score']:.1f}/100**

## Code Quality Analysis
- Average Score: {gate_results['evaluations']['code_quality']['average_score']:.1f}/100
- Files Analyzed: {gate_results['evaluations']['code_quality']['files_analyzed']}
- Violations: {len(gate_results['evaluations']['code_quality']['violations'])}

## Test Coverage
- Coverage: {gate_results['evaluations']['test_coverage']['coverage_percentage']:.1f}%
- Lines Covered: {gate_results['evaluations']['test_coverage']['lines_covered']}
- Total Lines: {gate_results['evaluations']['test_coverage']['lines_total']}

## Security Scan
- High Severity Issues: {gate_results['evaluations']['security_scan']['high_severity_issues']}
- Medium Severity Issues: {gate_results['evaluations']['security_scan']['medium_severity_issues']}
- Low Severity Issues: {gate_results['evaluations']['security_scan']['low_severity_issues']}

## Performance Check
- Regression Detected: {'Yes' if gate_results['evaluations']['performance_check']['performance_regression'] else 'No'}
- Performance Score: {gate_results['evaluations']['performance_check']['performance_score']:.1f}

## Blocking Issues
"""
        
        for issue in gate_results["blocking_issues"]:
            report += f"\n### {issue['category'].title()}\n"
            report += f"**{issue['message']}**\n\n"
            if issue["details"]:
                for detail in issue["details"][:5]:  # Limit to first 5 details
                    report += f"- {detail}\n"
        
        if gate_results["recommendations"]:
            report += "\n## Recommendations\n"
            for rec in gate_results["recommendations"][:10]:  # Limit to top 10
                report += f"- {rec}\n"
        
        return report
```

## 4. Performance Quality Standards

### 4.1 Performance Testing Framework
```python
import time
import asyncio
import psutil
from typing import Dict, Any, List, Callable
from dataclasses import dataclass

@dataclass
class PerformanceRequirement:
    """Performance requirement specification."""
    name: str
    description: str
    metric_type: str  # "response_time", "throughput", "memory", "cpu"
    target_value: float
    threshold_value: float  # Failing threshold
    measurement_unit: str
    test_duration: int  # seconds
    load_pattern: str  # "constant", "ramp", "spike"

class PerformanceTestSuite:
    """Comprehensive performance testing framework."""
    
    def __init__(self):
        self.requirements = {}
        self.load_default_requirements()
    
    def load_default_requirements(self):
        """Load default performance requirements for Chat App."""
        
        # LLM Response Time
        self.add_requirement(PerformanceRequirement(
            name="llm_response_time",
            description="LLM API response time",
            metric_type="response_time",
            target_value=3.0,  # 3 seconds target
            threshold_value=10.0,  # 10 seconds max
            measurement_unit="seconds",
            test_duration=60,
            load_pattern="constant"
        ))
        
        # Terminal Rendering Performance
        self.add_requirement(PerformanceRequirement(
            name="terminal_fps",
            description="Terminal rendering frame rate",
            metric_type="throughput",
            target_value=20.0,  # 20 FPS target
            threshold_value=10.0,  # 10 FPS minimum
            measurement_unit="fps",
            test_duration=30,
            load_pattern="constant"
        ))
        
        # Memory Usage
        self.add_requirement(PerformanceRequirement(
            name="memory_usage",
            description="Application memory consumption",
            metric_type="memory",
            target_value=50.0,  # 50MB target
            threshold_value=100.0,  # 100MB max
            measurement_unit="MB",
            test_duration=300,  # 5 minutes
            load_pattern="ramp"
        ))
    
    def add_requirement(self, requirement: PerformanceRequirement):
        """Add a performance requirement."""
        self.requirements[requirement.name] = requirement
    
    async def run_performance_test_suite(self) -> Dict[str, Any]:
        """Run comprehensive performance test suite."""
        
        suite_results = {
            "start_time": time.time(),
            "requirements_tested": len(self.requirements),
            "passed": 0,
            "failed": 0,
            "test_results": {},
            "overall_performance_score": 0,
            "recommendations": []
        }
        
        for req_name, requirement in self.requirements.items():
            print(f"Running performance test: {requirement.description}")
            
            test_result = await self.run_performance_test(requirement)
            suite_results["test_results"][req_name] = test_result
            
            if test_result["passed"]:
                suite_results["passed"] += 1
            else:
                suite_results["failed"] += 1
        
        suite_results["end_time"] = time.time()
        suite_results["total_duration"] = suite_results["end_time"] - suite_results["start_time"]
        
        # Calculate overall performance score
        suite_results["overall_performance_score"] = self.calculate_performance_score(suite_results)
        
        # Generate recommendations
        suite_results["recommendations"] = self.generate_performance_recommendations(suite_results)
        
        return suite_results
    
    async def run_performance_test(self, requirement: PerformanceRequirement) -> Dict[str, Any]:
        """Run a single performance test."""
        
        test_result = {
            "requirement_name": requirement.name,
            "start_time": time.time(),
            "measurements": [],
            "statistics": {},
            "passed": False,
            "performance_score": 0
        }
        
        if requirement.metric_type == "response_time":
            test_result = await self.test_response_time(requirement, test_result)
        elif requirement.metric_type == "throughput":
            test_result = await self.test_throughput(requirement, test_result)
        elif requirement.metric_type == "memory":
            test_result = await self.test_memory_usage(requirement, test_result)
        elif requirement.metric_type == "cpu":
            test_result = await self.test_cpu_usage(requirement, test_result)
        
        test_result["end_time"] = time.time()
        test_result["test_duration"] = test_result["end_time"] - test_result["start_time"]
        
        return test_result
    
    async def test_response_time(self, requirement: PerformanceRequirement, test_result: Dict) -> Dict:
        """Test response time performance."""
        
        measurements = []
        start_time = time.time()
        
        while time.time() - start_time < requirement.test_duration:
            # Simulate LLM request
            request_start = time.time()
            
            # Actual test operation would go here
            await self.simulate_llm_request()
            
            response_time = time.time() - request_start
            measurements.append(response_time)
            
            # Small delay between requests
            await asyncio.sleep(0.1)
        
        # Calculate statistics
        test_result["measurements"] = measurements
        test_result["statistics"] = {
            "count": len(measurements),
            "average": sum(measurements) / len(measurements),
            "min": min(measurements),
            "max": max(measurements),
            "p95": self.calculate_percentile(measurements, 95),
            "p99": self.calculate_percentile(measurements, 99)
        }
        
        # Check if requirement is met
        avg_response_time = test_result["statistics"]["average"]
        test_result["passed"] = avg_response_time <= requirement.threshold_value
        
        # Calculate performance score (0-100)
        if avg_response_time <= requirement.target_value:
            test_result["performance_score"] = 100
        elif avg_response_time <= requirement.threshold_value:
            # Linear scale between target and threshold
            score_range = requirement.threshold_value - requirement.target_value
            actual_over_target = avg_response_time - requirement.target_value
            test_result["performance_score"] = 100 - (actual_over_target / score_range * 50)
        else:
            test_result["performance_score"] = 0
        
        return test_result
    
    async def test_memory_usage(self, requirement: PerformanceRequirement, test_result: Dict) -> Dict:
        """Test memory usage performance."""
        
        process = psutil.Process()
        measurements = []
        start_time = time.time()
        
        while time.time() - start_time < requirement.test_duration:
            memory_info = process.memory_info()
            memory_mb = memory_info.rss / 1024 / 1024
            measurements.append(memory_mb)
            
            # Simulate work that might consume memory
            await self.simulate_memory_intensive_operation()
            
            await asyncio.sleep(1.0)  # Sample every second
        
        test_result["measurements"] = measurements
        test_result["statistics"] = {
            "count": len(measurements),
            "average": sum(measurements) / len(measurements),
            "min": min(measurements),
            "max": max(measurements),
            "final": measurements[-1] if measurements else 0
        }
        
        # Check requirement
        max_memory = test_result["statistics"]["max"]
        test_result["passed"] = max_memory <= requirement.threshold_value
        
        # Performance score
        if max_memory <= requirement.target_value:
            test_result["performance_score"] = 100
        elif max_memory <= requirement.threshold_value:
            score_range = requirement.threshold_value - requirement.target_value
            actual_over_target = max_memory - requirement.target_value
            test_result["performance_score"] = 100 - (actual_over_target / score_range * 50)
        else:
            test_result["performance_score"] = 0
        
        return test_result
    
    async def simulate_llm_request(self):
        """Simulate LLM request for testing."""
        # Simulate network delay and processing
        await asyncio.sleep(random.uniform(0.5, 3.0))
    
    async def simulate_memory_intensive_operation(self):
        """Simulate memory-intensive operation."""
        # Create and release some data
        data = [i for i in range(10000)]
        del data
    
    def calculate_percentile(self, data: List[float], percentile: int) -> float:
        """Calculate percentile value from data."""
        if not data:
            return 0
        
        sorted_data = sorted(data)
        index = int((percentile / 100) * (len(sorted_data) - 1))
        return sorted_data[index]
```

## 5. Documentation Quality Standards

### 5.1 Documentation Requirements
```python
class DocumentationQualityChecker:
    """Automated documentation quality assessment."""
    
    def __init__(self):
        self.quality_criteria = {
            "docstring_coverage": 90,  # 90% of public functions
            "docstring_quality": 80,   # Quality score out of 100
            "readme_completeness": 95, # README section coverage
            "api_documentation": 100,  # All public APIs documented
            "code_examples": 75        # Functions with examples
        }
    
    def assess_documentation_quality(self, codebase_path: str) -> Dict[str, Any]:
        """Assess overall documentation quality."""
        
        assessment = {
            "overall_score": 0,
            "criteria_results": {},
            "violations": [],
            "recommendations": [],
            "file_analysis": {}
        }
        
        # Analyze all Python files
        python_files = self.discover_python_files(codebase_path)
        
        for file_path in python_files:
            file_analysis = self.analyze_file_documentation(file_path)
            assessment["file_analysis"][file_path] = file_analysis
        
        # Calculate overall metrics
        assessment["criteria_results"] = self.calculate_documentation_metrics(assessment["file_analysis"])
        
        # Check violations
        assessment["violations"] = self.check_documentation_violations(assessment["criteria_results"])
        
        # Calculate overall score
        assessment["overall_score"] = self.calculate_documentation_score(assessment["criteria_results"])
        
        # Generate recommendations
        assessment["recommendations"] = self.generate_documentation_recommendations(assessment)
        
        return assessment
    
    def analyze_file_documentation(self, file_path: str) -> Dict[str, Any]:
        """Analyze documentation quality for a single file."""
        
        with open(file_path, 'r') as file:
            content = file.read()
        
        tree = ast.parse(content)
        
        analysis = {
            "file_path": file_path,
            "functions": [],
            "classes": [],
            "module_docstring": ast.get_docstring(tree) is not None,
            "total_public_functions": 0,
            "documented_functions": 0,
            "quality_scores": []
        }
        
        # Analyze functions
        class DocumentationVisitor(ast.NodeVisitor):
            def visit_FunctionDef(self, node):
                if not node.name.startswith('_'):  # Public function
                    analysis["total_public_functions"] += 1
                    docstring = ast.get_docstring(node)
                    
                    function_info = {
                        "name": node.name,
                        "line": node.lineno,
                        "has_docstring": docstring is not None,
                        "docstring_quality": self.assess_docstring_quality(docstring) if docstring else 0,
                        "parameters_documented": self.check_parameters_documented(node, docstring),
                        "return_documented": self.check_return_documented(node, docstring),
                        "examples_included": self.check_examples_included(docstring)
                    }
                    
                    analysis["functions"].append(function_info)
                    
                    if function_info["has_docstring"]:
                        analysis["documented_functions"] += 1
                        analysis["quality_scores"].append(function_info["docstring_quality"])
                
                self.generic_visit(node)
            
            def assess_docstring_quality(self, docstring: str) -> float:
                """Assess the quality of a docstring."""
                if not docstring:
                    return 0
                
                score = 0
                max_score = 100
                
                # Basic content (40 points)
                if len(docstring.strip()) > 20:
                    score += 20
                if '.' in docstring:  # Proper sentences
                    score += 20
                
                # Structure (30 points)
                if 'Args:' in docstring or 'Parameters:' in docstring:
                    score += 15
                if 'Returns:' in docstring or 'Return:' in docstring:
                    score += 15
                
                # Examples (20 points)
                if 'Example:' in docstring or '>>>' in docstring:
                    score += 20
                
                # Clarity (10 points)
                if len(docstring.split()) > 10:  # Sufficient detail
                    score += 10
                
                return min(score, max_score)
            
            def check_parameters_documented(self, node: ast.FunctionDef, docstring: str) -> bool:
                """Check if all parameters are documented."""
                if not docstring or not node.args.args:
                    return True  # No parameters to document
                
                param_names = [arg.arg for arg in node.args.args if arg.arg != 'self']
                if not param_names:
                    return True
                
                # Check if parameters are mentioned in docstring
                return all(param in docstring for param in param_names[:3])  # Check first 3 params
            
            def check_return_documented(self, node: ast.FunctionDef, docstring: str) -> bool:
                """Check if return value is documented."""
                if not docstring:
                    return False
                
                # Check for return statements in function
                has_return = any(isinstance(n, ast.Return) and n.value for n in ast.walk(node))
                
                if has_return:
                    return any(keyword in docstring.lower() for keyword in ['return', 'returns'])
                
                return True  # No return value to document
            
            def check_examples_included(self, docstring: str) -> bool:
                """Check if examples are included in docstring."""
                if not docstring:
                    return False
                
                return any(keyword in docstring.lower() for keyword in ['example', '>>>', 'usage'])
        
        visitor = DocumentationVisitor()
        visitor.visit(tree)
        
        return analysis
    
    def generate_documentation_improvements(self, analysis: Dict[str, Any]) -> List[str]:
        """Generate specific documentation improvement suggestions."""
        
        improvements = []
        
        # Overall coverage improvements
        if analysis["overall_score"] < 80:
            improvements.append("📚 **Critical**: Overall documentation score is below 80%. Focus on improving docstring coverage and quality.")
        
        # File-specific improvements
        for file_path, file_analysis in analysis["file_analysis"].items():
            if file_analysis["total_public_functions"] > 0:
                coverage = file_analysis["documented_functions"] / file_analysis["total_public_functions"]
                
                if coverage < 0.8:
                    improvements.append(
                        f"📝 **{file_path}**: Document {file_analysis['total_public_functions'] - file_analysis['documented_functions']} "
                        f"missing public functions (currently {coverage:.1%} coverage)"
                    )
                
                # Quality improvements for documented functions
                low_quality_functions = [
                    f for f in file_analysis["functions"] 
                    if f["has_docstring"] and f["docstring_quality"] < 70
                ]
                
                if low_quality_functions:
                    improvements.append(
                        f"✨ **{file_path}**: Improve docstring quality for functions: {', '.join(f['name'] for f in low_quality_functions[:3])}"
                    )
        
        return improvements[:10]  # Return top 10 improvements
```

## 6. Enforcement and Compliance

### 6.1 Quality Gates Integration
```python
class QualityGateOrchestrator:
    """Orchestrates all quality gates for comprehensive validation."""
    
    def __init__(self):
        self.quality_gates = {
            "code_quality": CodeQualityGate(),
            "test_coverage": TestCoverageGate(), 
            "security": SecurityGate(),
            "performance": PerformanceGate(),
            "documentation": DocumentationGate()
        }
        self.ai_assistant = None  # AI assistant for analysis
    
    async def run_all_quality_gates(self, 
                                   changed_files: List[str],
                                   deployment_stage: str = "development") -> Dict[str, Any]:
        """Run all quality gates with stage-appropriate thresholds."""
        
        orchestration_result = {
            "deployment_stage": deployment_stage,
            "start_time": time.time(),
            "overall_passed": True,
            "gate_results": {},
            "blocking_issues": [],
            "warnings": [],
            "recommendations": [],
            "quality_score": 100,
            "release_readiness": "not_ready"
        }
        
        # Define stage-specific requirements
        stage_requirements = self.get_stage_requirements(deployment_stage)
        
        # Run each quality gate
        for gate_name, gate in self.quality_gates.items():
            if gate_name in stage_requirements["required_gates"]:
                print(f"Running {gate_name} quality gate...")
                
                gate_result = await gate.evaluate(
                    changed_files, 
                    stage_requirements[gate_name]
                )
                
                orchestration_result["gate_results"][gate_name] = gate_result
                
                # Check for blocking issues
                if not gate_result["passed"] and gate_result["severity"] == "blocking":
                    orchestration_result["overall_passed"] = False
                    orchestration_result["blocking_issues"].extend(gate_result["issues"])
                elif not gate_result["passed"]:
                    orchestration_result["warnings"].extend(gate_result["issues"])
        
        orchestration_result["end_time"] = time.time()
        orchestration_result["total_duration"] = (
            orchestration_result["end_time"] - orchestration_result["start_time"]
        )
        
        # Calculate overall quality score
        orchestration_result["quality_score"] = self.calculate_overall_quality_score(
            orchestration_result["gate_results"]
        )
        
        # Determine release readiness
        orchestration_result["release_readiness"] = self.assess_release_readiness(
            orchestration_result, deployment_stage
        )
        
        # Generate comprehensive recommendations
        if self.ai_assistant:
            orchestration_result["recommendations"] = await self.generate_ai_recommendations(
                orchestration_result
            )
        
        return orchestration_result
    
    def get_stage_requirements(self, stage: str) -> Dict[str, Any]:
        """Get quality requirements for deployment stage."""
        
        requirements = {
            "development": {
                "required_gates": ["code_quality", "test_coverage"],
                "code_quality": {"min_score": 70, "severity": "warning"},
                "test_coverage": {"min_coverage": 80, "severity": "warning"}
            },
            "staging": {
                "required_gates": ["code_quality", "test_coverage", "security", "performance"],
                "code_quality": {"min_score": 80, "severity": "blocking"},
                "test_coverage": {"min_coverage": 90, "severity": "blocking"},
                "security": {"max_high_issues": 0, "severity": "blocking"},
                "performance": {"regression_tolerance": 5, "severity": "warning"}
            },
            "production": {
                "required_gates": ["code_quality", "test_coverage", "security", "performance", "documentation"],
                "code_quality": {"min_score": 90, "severity": "blocking"},
                "test_coverage": {"min_coverage": 95, "severity": "blocking"},
                "security": {"max_high_issues": 0, "max_medium_issues": 0, "severity": "blocking"},
                "performance": {"regression_tolerance": 0, "severity": "blocking"},
                "documentation": {"min_coverage": 95, "severity": "blocking"}
            }
        }
        
        return requirements.get(stage, requirements["development"])
    
    def assess_release_readiness(self, orchestration_result: Dict, stage: str) -> str:
        """Assess overall release readiness."""
        
        if orchestration_result["blocking_issues"]:
            return "blocked"
        
        if orchestration_result["quality_score"] >= 95:
            return "ready"
        elif orchestration_result["quality_score"] >= 85:
            return "ready_with_warnings"
        elif orchestration_result["quality_score"] >= 70:
            return "needs_improvement"
        else:
            return "not_ready"
    
    def generate_quality_dashboard(self, orchestration_result: Dict) -> str:
        """Generate comprehensive quality dashboard."""
        
        dashboard = f"""
# 🎯 Quality Assessment Dashboard

## Overall Status: {self.format_status(orchestration_result['overall_passed'])}
**Quality Score: {orchestration_result['quality_score']:.1f}/100**
**Release Readiness: {orchestration_result['release_readiness'].replace('_', ' ').title()}**

## Quality Gate Results

| Gate | Status | Score | Issues | Duration |
|------|---------|-------|---------|----------|
"""
        
        for gate_name, gate_result in orchestration_result["gate_results"].items():
            status_icon = "✅" if gate_result["passed"] else "❌"
            score = gate_result.get("score", 0)
            issues = len(gate_result.get("issues", []))
            duration = gate_result.get("duration", 0)
            
            dashboard += f"| {gate_name.title()} | {status_icon} | {score:.1f} | {issues} | {duration:.2f}s |\n"
        
        if orchestration_result["blocking_issues"]:
            dashboard += "\n## 🚫 Blocking Issues\n"
            for issue in orchestration_result["blocking_issues"]:
                dashboard += f"- **{issue.get('category', 'Unknown')}**: {issue.get('message', 'No message')}\n"
        
        if orchestration_result["warnings"]:
            dashboard += "\n## ⚠️ Warnings\n"
            for warning in orchestration_result["warnings"]:
                dashboard += f"- **{warning.get('category', 'Unknown')}**: {warning.get('message', 'No message')}\n"
        
        if orchestration_result["recommendations"]:
            dashboard += "\n## 💡 Recommendations\n"
            for rec in orchestration_result["recommendations"][:5]:
                dashboard += f"- {rec}\n"
        
        return dashboard
    
    def format_status(self, passed: bool) -> str:
        """Format status with appropriate emoji."""
        return "✅ PASSED" if passed else "❌ FAILED"
```

### 6.2 Continuous Quality Monitoring
```python
class ContinuousQualityMonitor:
    """Continuous monitoring of quality metrics across the development lifecycle."""
    
    def __init__(self):
        self.metrics_collector = QualityMetricsCollector()
        self.trend_analyzer = QualityTrendAnalyzer()
        self.alert_manager = QualityAlertManager()
        self.dashboard = QualityDashboard()
    
    async def run_monitoring_cycle(self):
        """Run complete quality monitoring cycle."""
        
        print("🔍 Starting quality monitoring cycle...")
        
        # Collect current quality metrics
        current_metrics = await self.collect_comprehensive_metrics()
        
        # Analyze trends
        trend_analysis = await self.trend_analyzer.analyze_trends(current_metrics)
        
        # Detect quality degradation
        quality_alerts = self.detect_quality_degradation(current_metrics, trend_analysis)
        
        # Send alerts if needed
        if quality_alerts:
            await self.alert_manager.send_quality_alerts(quality_alerts)
        
        # Update quality dashboard
        await self.dashboard.update_metrics(current_metrics, trend_analysis)
        
        # Store metrics for historical analysis
        await self.metrics_collector.store_metrics(current_metrics)
        
        print(f"✅ Quality monitoring cycle completed. Score: {current_metrics.get('overall_score', 0):.1f}/100")
    
    async def collect_comprehensive_metrics(self) -> Dict[str, Any]:
        """Collect all quality metrics from various sources."""
        
        return {
            "timestamp": time.time(),
            "code_quality": await self.collect_code_quality_metrics(),
            "test_metrics": await self.collect_test_metrics(),
            "security_metrics": await self.collect_security_metrics(),
            "performance_metrics": await self.collect_performance_metrics(),
            "documentation_metrics": await self.collect_documentation_metrics(),
            "overall_score": 0  # Calculated below
        }
    
    def detect_quality_degradation(self, 
                                 current_metrics: Dict, 
                                 trend_analysis: Dict) -> List[Dict]:
        """Detect quality degradation patterns."""
        
        alerts = []
        
        # Code quality degradation
        if trend_analysis["code_quality"]["trend"] == "declining":
            if trend_analysis["code_quality"]["rate"] > 5:  # 5 point decline
                alerts.append({
                    "type": "quality_degradation",
                    "category": "code_quality", 
                    "severity": "high",
                    "message": f"Code quality declining at {trend_analysis['code_quality']['rate']:.1f} points/week",
                    "current_value": current_metrics["code_quality"]["score"],
                    "trend_data": trend_analysis["code_quality"]
                })
        
        # Test coverage decline
        if current_metrics["test_metrics"]["coverage"] < 85:
            alerts.append({
                "type": "coverage_below_threshold",
                "category": "test_coverage",
                "severity": "medium",
                "message": f"Test coverage at {current_metrics['test_metrics']['coverage']:.1f}%, below 85% threshold",
                "current_value": current_metrics["test_metrics"]["coverage"]
            })
        
        # Performance regression
        if trend_analysis["performance_metrics"]["response_time"]["trend"] == "increasing":
            alerts.append({
                "type": "performance_regression",
                "category": "performance",
                "severity": "medium", 
                "message": "Response time increasing trend detected",
                "trend_data": trend_analysis["performance_metrics"]["response_time"]
            })
        
        return alerts

This comprehensive Quality Assurance Standards document establishes rigorous, AI-enhanced quality processes that ensure the Chat App maintains high standards across all aspects of development, testing, and deployment. The framework provides automated validation, continuous monitoring, and intelligent recommendations for maintaining exceptional quality throughout the development lifecycle.