# Unified Quality-Driven Development (QDD) Tool Specification

## Problem Statement

Current state has fragmented tools:
- `refactor_auto` - Works on existing code only
- `scaffold_project` - Creates templates without quality enforcement
- `quality_gate` - Validates but doesn't fix
- `pdmt_todos` - Plans but doesn't implement

**Missing**: A unified tool that creates AND refactors code with guaranteed quality standards.

## Solution: `pmat qdd` - Quality-Driven Development Tool

### Core Concept

```rust
pub enum QDDOperation {
    Create(CreateSpec),      // New code with quality built-in
    Refactor(RefactorSpec),  // Existing code to quality standards
    Enhance(EnhanceSpec),    // Add features maintaining quality
    Migrate(MigrateSpec),    // Transform code between patterns
}

pub struct QDDResult {
    code: String,
    quality_score: QualityScore,
    coverage_tests: Vec<TestCase>,
    property_tests: Vec<PropertyTest>,
    documentation: Documentation,
    metrics: QualityMetrics,
}
```

## Unified Interface

### MCP Tool: `quality_driven_development`

```yaml
Tool: quality_driven_development
Aliases: [qdd, unified_refactor, quality_create]
Purpose: Create or refactor code with guaranteed quality standards
Parameters:
  operation: "create" | "refactor" | "enhance" | "migrate"
  spec:
    # For create
    type: "function" | "module" | "service" | "test"
    name: string
    purpose: string
    inputs: [...]
    outputs: [...]
    
    # For refactor
    file_path: string
    target_metrics:
      complexity: 10
      coverage: 80
      tdg: 5
    
    # For enhance
    base_file: string
    features: [...]
    maintain_api: boolean
    
    # For migrate
    from_pattern: string
    to_pattern: string
    files: [...]
    
  quality_profile: "extreme" | "standard" | "relaxed"
  auto_test: boolean
  auto_document: boolean
  
Output:
  code: string
  tests: string
  documentation: string
  quality_report: QualityReport
  rollback_plan: RollbackPlan
```

## Implementation Architecture

### 1. Quality Profile Engine

```rust
pub struct QualityProfile {
    name: String,
    rules: Vec<QualityRule>,
    thresholds: QualityThresholds,
    patterns: DesignPatterns,
}

impl QualityProfile {
    pub fn extreme() -> Self {
        Self {
            thresholds: QualityThresholds {
                max_complexity: 5,
                max_cognitive: 5,
                min_coverage: 90,
                max_tdg: 3,
                zero_satd: true,
                zero_dead_code: true,
                require_doctests: true,
                require_property_tests: true,
            },
            patterns: DesignPatterns {
                enforce_solid: true,
                enforce_dry: true,
                enforce_kiss: true,
                enforce_yagni: true,
            },
            ..Default::default()
        }
    }
}
```

### 2. Code Generation Engine

```rust
pub struct QualityCodeGenerator {
    profile: QualityProfile,
    ast_builder: AstBuilder,
    test_generator: TestGenerator,
    doc_generator: DocGenerator,
}

impl QualityCodeGenerator {
    pub fn create_function(&self, spec: FunctionSpec) -> QDDResult {
        // 1. Generate initial implementation
        let mut ast = self.ast_builder.build_function(&spec);
        
        // 2. Apply quality patterns
        ast = self.apply_patterns(ast);
        
        // 3. Decompose if complex
        if self.measure_complexity(&ast) > self.profile.thresholds.max_complexity {
            ast = self.decompose_function(ast);
        }
        
        // 4. Generate tests
        let tests = self.test_generator.generate_for(&ast, &spec);
        
        // 5. Generate documentation
        let docs = self.doc_generator.generate_for(&ast, &spec);
        
        // 6. Validate quality
        self.validate_quality(&ast, &tests)?;
        
        QDDResult {
            code: ast.to_source(),
            coverage_tests: tests,
            documentation: docs,
            quality_score: self.calculate_score(&ast),
            metrics: self.calculate_metrics(&ast),
        }
    }
}
```

### 3. Refactoring Engine

```rust
pub struct QualityRefactoringEngine {
    analyzer: CodeAnalyzer,
    refactorer: Refactorer,
    validator: QualityValidator,
}

impl QualityRefactoringEngine {
    pub async fn refactor_to_quality(&self, code: &str, profile: QualityProfile) -> QDDResult {
        let mut current = code.to_string();
        let mut iteration = 0;
        
        loop {
            // 1. Analyze current state
            let analysis = self.analyzer.analyze(&current)?;
            
            // 2. Check if meets quality
            if self.meets_profile(&analysis, &profile) {
                break;
            }
            
            // 3. Identify worst violation
            let target = self.identify_refactoring_target(&analysis, &profile);
            
            // 4. Apply targeted refactoring
            let refactored = match target {
                Target::Complexity(func) => self.decompose_complex_function(func),
                Target::Satd(comment) => self.implement_todo(comment),
                Target::DeadCode(code) => self.remove_dead_code(code),
                Target::Tdg(debt) => self.reduce_technical_debt(debt),
                Target::Coverage(uncovered) => self.generate_tests(uncovered),
            }?;
            
            // 5. Validate improvement
            let new_analysis = self.analyzer.analyze(&refactored)?;
            if !self.is_improvement(&analysis, &new_analysis) {
                return Err(QDDError::NoImprovementPossible);
            }
            
            current = refactored;
            iteration += 1;
            
            if iteration > MAX_ITERATIONS {
                return Err(QDDError::MaxIterationsReached);
            }
        }
        
        Ok(QDDResult::from(current))
    }
}
```

### 4. Pattern Application Engine

```rust
pub struct PatternEngine {
    patterns: HashMap<String, Box<dyn DesignPattern>>,
}

impl PatternEngine {
    pub fn apply_pattern(&self, code: &str, pattern: &str) -> Result<String> {
        let pattern_impl = self.patterns.get(pattern)?;
        pattern_impl.apply(code)
    }
    
    pub fn detect_violations(&self, code: &str) -> Vec<PatternViolation> {
        self.patterns.values()
            .flat_map(|p| p.detect_violations(code))
            .collect()
    }
}

// Example patterns
pub struct SingleResponsibilityPattern;
impl DesignPattern for SingleResponsibilityPattern {
    fn apply(&self, code: &str) -> Result<String> {
        // Detect functions doing multiple things
        // Decompose into single-purpose functions
    }
}

pub struct DependencyInjectionPattern;
impl DesignPattern for DependencyInjectionPattern {
    fn apply(&self, code: &str) -> Result<String> {
        // Replace hard-coded dependencies with injected ones
        // Add trait boundaries for testability
    }
}
```

## Workflow Examples

### Example 1: Create New Function with Quality

```yaml
Request:
  tool: quality_driven_development
  operation: create
  spec:
    type: function
    name: calculate_risk_score
    purpose: "Calculate risk score based on complexity and coverage"
    inputs:
      - name: complexity
        type: u32
      - name: coverage
        type: f32
    outputs:
      type: f32
      range: 0.0..1.0
  quality_profile: extreme

Response:
  code: |
    /// Calculates risk score based on complexity and coverage metrics.
    /// 
    /// # Arguments
    /// * `complexity` - Cyclomatic complexity of the code
    /// * `coverage` - Test coverage as a percentage (0.0 to 1.0)
    /// 
    /// # Returns
    /// Risk score between 0.0 (low risk) and 1.0 (high risk)
    /// 
    /// # Example
    /// ```
    /// let risk = calculate_risk_score(10, 0.8);
    /// assert!(risk >= 0.0 && risk <= 1.0);
    /// ```
    pub fn calculate_risk_score(complexity: u32, coverage: f32) -> f32 {
        let complexity_factor = normalize_complexity(complexity);
        let coverage_factor = invert_coverage(coverage);
        combine_factors(complexity_factor, coverage_factor)
    }
    
    fn normalize_complexity(complexity: u32) -> f32 {
        (complexity as f32 / 20.0).min(1.0)
    }
    
    fn invert_coverage(coverage: f32) -> f32 {
        1.0 - coverage.clamp(0.0, 1.0)
    }
    
    fn combine_factors(complexity: f32, coverage: f32) -> f32 {
        (complexity * 0.6 + coverage * 0.4).clamp(0.0, 1.0)
    }
    
  tests: |
    #[cfg(test)]
    mod tests {
        use super::*;
        use proptest::prelude::*;
        
        #[test]
        fn test_risk_score_bounds() {
            assert_eq!(calculate_risk_score(0, 1.0), 0.0);
            assert_eq!(calculate_risk_score(100, 0.0), 1.0);
        }
        
        proptest! {
            #[test]
            fn prop_risk_score_in_range(
                complexity in 0u32..100,
                coverage in 0.0f32..1.0
            ) {
                let risk = calculate_risk_score(complexity, coverage);
                prop_assert!(risk >= 0.0 && risk <= 1.0);
            }
            
            #[test]
            fn prop_risk_increases_with_complexity(
                c1 in 0u32..50,
                c2 in 50u32..100,
                coverage in 0.0f32..1.0
            ) {
                let risk1 = calculate_risk_score(c1, coverage);
                let risk2 = calculate_risk_score(c2, coverage);
                prop_assert!(risk2 >= risk1);
            }
        }
    }
    
  quality_report:
    complexity: 3  # Each helper function = 1
    coverage: 100%
    tdg: 2
    satd: 0
    dead_code: 0%
    defect_probability: 2%
```

### Example 2: Refactor Existing Complex Function

```yaml
Request:
  tool: quality_driven_development
  operation: refactor
  spec:
    file_path: "src/parser.rs"
    function: "parse_expression"  # Currently complexity: 31
    target_metrics:
      complexity: 8
      coverage: 85
  quality_profile: extreme

Response:
  code: |
    // Original monolithic function decomposed into:
    
    pub fn parse_expression(input: &str) -> Result<Expression> {
        let tokens = tokenize(input)?;
        let ast = build_ast(&tokens)?;
        validate_expression(&ast)?;
        Ok(ast)
    }
    
    fn tokenize(input: &str) -> Result<Vec<Token>> {
        TokenIterator::new(input).collect()
    }
    
    fn build_ast(tokens: &[Token]) -> Result<Expression> {
        AstBuilder::new(tokens).build()
    }
    
    fn validate_expression(expr: &Expression) -> Result<()> {
        ExpressionValidator::validate(expr)
    }
    
    // ... 5 more helper functions, each with complexity < 5
    
  refactoring_steps:
    - Extract tokenization logic (complexity: 31 → 24)
    - Extract AST building (complexity: 24 → 16)
    - Extract validation (complexity: 16 → 12)
    - Decompose remaining conditionals (complexity: 12 → 8)
    - Extract error handling (complexity: 8 → 6)
    
  quality_report:
    before:
      complexity: 31
      coverage: 45%
      tdg: 18
    after:
      complexity: 6
      coverage: 85%
      tdg: 4
    improvements:
      complexity: -80%
      coverage: +89%
      tdg: -78%
```

### Example 3: Enhance with New Features

```yaml
Request:
  tool: quality_driven_development
  operation: enhance
  spec:
    base_file: "src/analyzer.rs"
    features:
      - "Add caching for analysis results"
      - "Support incremental analysis"
      - "Add performance metrics"
    maintain_api: true
    
Response:
  code: |
    // Enhanced with caching, incremental analysis, and metrics
    pub struct Analyzer {
        cache: AnalysisCache,
        metrics: PerformanceMetrics,
        incremental: IncrementalEngine,
    }
    
    impl Analyzer {
        // Original API maintained
        pub fn analyze(&self, code: &str) -> Result<Analysis> {
            self.analyze_with_options(code, AnalysisOptions::default())
        }
        
        // Enhanced implementation
        pub fn analyze_with_options(&self, code: &str, options: AnalysisOptions) -> Result<Analysis> {
            let start = Instant::now();
            
            // Check cache first
            if let Some(cached) = self.cache.get(code) {
                self.metrics.record_cache_hit();
                return Ok(cached);
            }
            
            // Perform incremental analysis if possible
            let result = if let Some(base) = self.incremental.find_base(code) {
                self.incremental_analyze(base, code)?
            } else {
                self.full_analyze(code)?
            };
            
            // Update cache and metrics
            self.cache.insert(code, result.clone());
            self.metrics.record_analysis_time(start.elapsed());
            
            Ok(result)
        }
    }
```

## Integration with Existing Tools

### Quality Gate Integration

```rust
impl QDDTool {
    pub async fn ensure_quality(&self, result: QDDResult) -> Result<()> {
        // Automatically run quality gate on generated code
        let gate_result = self.quality_gate.check(&result.code).await?;
        
        if !gate_result.passed {
            // Automatically fix violations
            let fixed = self.auto_fix(result.code, gate_result.violations)?;
            self.ensure_quality(fixed).await
        } else {
            Ok(())
        }
    }
}
```

### PDMT Integration

```rust
impl QDDTool {
    pub fn generate_implementation_todos(&self, spec: Spec) -> Vec<Todo> {
        // Use PDMT to generate implementation tasks
        let todos = self.pdmt.generate_todos(
            &spec.to_requirements(),
            PdmtConfig {
                granularity: Granularity::Fine,
                include_tests: true,
                include_docs: true,
                quality_requirements: self.profile.to_pdmt_requirements(),
            }
        )?;
        
        todos
    }
}
```

## CLI Interface

```bash
# Create new function with quality
pmat qdd create function calculate_metrics \
  --inputs "data:Vec<f64>" \
  --output "Metrics" \
  --quality extreme

# Refactor existing code
pmat qdd refactor src/complex.rs \
  --target-complexity 8 \
  --target-coverage 80 \
  --auto-test

# Enhance with features
pmat qdd enhance src/service.rs \
  --add-feature "caching" \
  --add-feature "retry-logic" \
  --maintain-api

# Migrate to pattern
pmat qdd migrate src/**/*.rs \
  --from "singleton" \
  --to "dependency-injection" \
  --validate
```

## Quality Guarantees

### Invariants Enforced

1. **Complexity Invariant**: No function exceeds profile complexity
2. **Coverage Invariant**: All code has tests meeting coverage threshold
3. **SATD Invariant**: Zero technical debt comments
4. **TDG Invariant**: Technical debt gradient within threshold
5. **Pattern Invariant**: All code follows specified design patterns

### Rollback Capability

```rust
pub struct RollbackPlan {
    original: String,
    checkpoints: Vec<Checkpoint>,
    validation: QualityReport,
}

impl RollbackPlan {
    pub fn rollback_to_checkpoint(&self, index: usize) -> Result<String> {
        let checkpoint = &self.checkpoints[index];
        if self.validate_checkpoint(checkpoint)? {
            Ok(checkpoint.code.clone())
        } else {
            Err(QDDError::CheckpointInvalid)
        }
    }
}
```

## Success Metrics

| Metric | Target | Measurement |
|--------|--------|-------------|
| Quality Achievement Rate | 100% | All generated code passes quality gates |
| Refactoring Success Rate | > 95% | Successful quality improvements |
| Generation Speed | < 5s | Time to generate quality code |
| Test Coverage Generated | > 80% | Auto-generated test coverage |
| Pattern Compliance | 100% | Adherence to design patterns |

## Future Enhancements

1. **ML-Powered Pattern Detection**: Learn patterns from high-quality codebases
2. **Team Style Profiles**: Customize to team coding standards
3. **Cross-Language Support**: Apply patterns across language boundaries
4. **Architecture Generation**: Generate entire service architectures
5. **Performance Optimization**: Add performance as quality dimension