# Proactive Quality Analysis

Automated code quality issue detection and continuous improvement recommendations.

## Overview

The Proactive Quality Scanner automatically detects code quality issues and provides improvement recommendations across multiple dimensions:
- Performance optimization opportunities
- Maintainability improvements
- Security vulnerabilities
- Code duplication
- Technical debt analysis
- Complexity reduction

## Proactive Scanner Implementation

```python
class ProactiveQualityScanner:
 """Proactive code quality issue detection and analysis"""

 def __init__(self, context7_client: Context7Client):
 self.context7_client = context7_client
 self.issue_detectors = self._initialize_detectors()
 self.pattern_analyzer = CodePatternAnalyzer()

 async def scan(self, codebase: str, focus_areas: List[str]) -> ProactiveResult:
 """Comprehensive proactive quality scanning"""

 scan_results = {}

 # Performance analysis
 if "performance" in focus_areas:
 scan_results["performance"] = await self._scan_performance_issues(codebase)

 # Maintainability analysis
 if "maintainability" in focus_areas:
 scan_results["maintainability"] = await self._scan_maintainability_issues(codebase)

 # Security vulnerabilities
 if "security" in focus_areas:
 scan_results["security"] = await self._scan_security_issues(codebase)

 # Code duplication
 if "duplication" in focus_areas:
 scan_results["duplication"] = await self._scan_code_duplication(codebase)

 # Technical debt
 if "technical_debt" in focus_areas:
 scan_results["technical_debt"] = await self._analyze_technical_debt(codebase)

 # Code complexity
 if "complexity" in focus_areas:
 scan_results["complexity"] = await self._analyze_complexity(codebase)

 # Generate improvement recommendations
 recommendations = await self._generate_improvement_recommendations(scan_results)

 return ProactiveResult(
 scan_results=scan_results,
 recommendations=recommendations,
 priority_issues=self._identify_priority_issues(scan_results),
 estimated_effort=self._calculate_improvement_effort(recommendations)
 )

 async def _scan_performance_issues(self, codebase: str) -> PerformanceResult:
 """Scan for performance-related issues"""

 issues = []

 # Get language-specific performance patterns from Context7
 for language in self._detect_languages(codebase):
 try:
 # Resolve library ID
 library_id = await self.context7_client.resolve_library_id(language)

 # Get performance best practices
 perf_docs = await self.context7_client.get_library_docs(
 context7CompatibleLibraryID=library_id,
 topic="performance",
 tokens=3000
 )

 # Analyze code against performance patterns
 language_issues = await self._analyze_performance_patterns(
 codebase, language, perf_docs
 )
 issues.extend(language_issues)

 except Exception as e:
 logger.warning(f"Failed to get performance docs for {language}: {e}")

 # Common performance issues
 common_issues = await self._detect_common_performance_issues(codebase)
 issues.extend(common_issues)

 return PerformanceResult(
 issues=issues,
 score=self._calculate_performance_score(issues),
 hotspots=self._identify_performance_hotspots(issues),
 optimizations=self._suggest_optimizations(issues)
 )
```

## Usage Examples

```python
# Initialize proactive scanner
proactive_scanner = ProactiveQualityScanner(
 context7_client=context7_client,
 rule_engine=BestPracticesEngine()
)

# Scan for improvement opportunities
improvements = await proactive_scanner.scan_codebase(
 path="src/",
 scan_types=["security", "performance", "maintainability", "testing"]
)

# Generate improvement recommendations
recommendations = await proactive_scanner.generate_recommendations(
 issues=improvements,
 priority="high",
 auto_fix=True
)
```

## Configuration

```yaml
proactive_analysis:
 enabled: true
 scan_frequency: "daily"
 focus_areas:
 - "performance"
 - "security"
 - "maintainability"
 - "technical_debt"

 auto_fix:
 enabled: true
 severity_threshold: "medium"
 confirmation_required: true
```

## Advanced Patterns

### Machine Learning Quality Prediction

```python
class QualityPredictionEngine:
 """ML-powered quality issue prediction"""

 def __init__(self, model_path: str):
 self.model = self._load_model(model_path)
 self.feature_extractor = CodeFeatureExtractor()

 async def predict_quality_issues(self, codebase: str) -> PredictionResult:
 """Predict potential quality issues using ML"""

 # Extract code features
 features = await self.feature_extractor.extract_features(codebase)

 # Make predictions
 predictions = self.model.predict(features)

 # Analyze prediction confidence
 confidence_scores = self.model.predict_proba(features)

 # Group predictions by issue type
 issue_predictions = self._group_predictions_by_type(
 predictions, confidence_scores
 )

 return PredictionResult(
 predictions=issue_predictions,
 confidence_scores=confidence_scores,
 high_risk_areas=self._identify_high_risk_areas(issue_predictions),
 prevention_recommendations=self._generate_prevention_recommendations(
 issue_predictions
 )
 )
```

### Real-time Quality Monitoring

```python
class RealTimeQualityMonitor:
 """Real-time code quality monitoring and alerting"""

 def __init__(self, webhook_url: str, notification_config: Dict):
 self.webhook_url = webhook_url
 self.notification_config = notification_config
 self.quality_history = deque(maxlen=1000)
 self.alert_thresholds = notification_config.get("thresholds", {})

 async def monitor_quality_changes(self, codebase: str):
 """Continuously monitor quality changes"""

 while True:
 # Get current quality metrics
 current_metrics = await self._get_current_quality_metrics(codebase)

 # Compare with historical data
 if self.quality_history:
 previous_metrics = self.quality_history[-1]
 quality_change = self._calculate_quality_change(
 previous_metrics, current_metrics
 )

 # Check for quality degradation
 if quality_change < -self.alert_thresholds.get("degradation", 0.1):
 await self._send_quality_alert(
 alert_type="quality_degradation",
 metrics=current_metrics,
 change=quality_change
 )

 # Store metrics
 self.quality_history.append(current_metrics)

 # Wait for next check
 await asyncio.sleep(self.notification_config.get("check_interval", 300))
```

## Related

- [TRUST 5 Validation](trust5-validation.md)
- [Best Practices Engine](best-practices.md)
- [Integration Patterns](integration-patterns.md)
