# Code Generation Agent with Execution and Validation

## Overview
Demonstrates advanced code generation capabilities with automatic execution, testing, and iterative refinement. This agent can generate code from natural language specifications, validate functionality, and improve implementations based on test results.

## Use Case
- Automated code generation from specifications
- Code prototyping and rapid development
- Test-driven development assistance
- Code refactoring and optimization

## Agent Specification

### Core Functionality
- **Input**: Natural language code requirements and constraints
- **Processing**: Code generation, execution, testing, and refinement
- **Output**: Working code with tests and documentation
- **Memory**: Code patterns, best practices, and error corrections

### Signature Pattern
```python
class CodeGenerationSignature(dspy.Signature):
    """Generate, test, and refine code from natural language specifications."""
    specification: str = dspy.InputField(desc="Natural language code requirements")
    language: str = dspy.InputField(desc="Target programming language")
    constraints: str = dspy.InputField(desc="Technical constraints and requirements")
    test_requirements: str = dspy.InputField(desc="Testing and validation requirements")

    code: str = dspy.OutputField(desc="Generated code implementation")
    tests: str = dspy.OutputField(desc="Comprehensive test suite")
    documentation: str = dspy.OutputField(desc="Code documentation and usage examples")
    execution_result: str = dspy.OutputField(desc="Test execution results and validation")
    quality_score: float = dspy.OutputField(desc="Code quality assessment (0.0-1.0)")
```

## Expected Execution Flow

### Phase 1: Specification Analysis (0-200ms)
```
[00:00:000] Natural language specification parsed
[00:00:050] Requirements extracted and categorized
[00:00:100] Technical constraints identified
[00:00:150] Code structure and approach determined
[00:00:200] Generation strategy selected
```

### Phase 2: Code Generation (200ms-2s)
```
[00:00:200] Initial code generation started
[00:00:800] Core functionality implemented
[00:01:200] Error handling and edge cases added
[00:01:600] Code style and formatting applied
[00:01:800] Documentation generation completed
[00:02:000] Initial code draft ready
```

### Phase 3: Test Generation and Execution (2s-4s)
```
[00:02:000] Test cases generated from requirements
[00:02:400] Unit tests for core functionality created
[00:02:800] Integration tests for complete workflow added
[00:03:200] Code execution in sandboxed environment
[00:03:600] Test results analyzed and scored
[00:04:000] Quality assessment completed
```

### Phase 4: Refinement Loop (4s-8s)
```
[00:04:000] Test failures identified and categorized
[00:04:500] Code improvements generated
[00:05:000] Enhanced implementation tested
[00:05:500] Performance optimizations applied
[00:06:000] Final validation and quality check
[00:06:500] Documentation updated with final version
[00:07:000] Complete solution package prepared
```

## Technical Requirements

### Dependencies
```python
# Core Kailash SDK
from kailash.workflow.builder import WorkflowBuilder
from kailash.runtime.local import LocalRuntime
from kailash.nodes.llm_agent import LLMAgentNode
from kailash.nodes.python_code import PythonCodeNode
from kailash.nodes.file_operations import FileOperationsNode

# Code execution and validation
import dspy
import subprocess
import ast
import sys
import tempfile
import docker
from typing import List, Dict, Optional, Tuple
import pytest
import black
import pylint
```

### Configuration
```yaml
code_generation:
  supported_languages: ["python", "javascript", "java", "go", "rust"]
  max_code_length: 10000
  execution_timeout: 30
  max_refinement_iterations: 3

sandbox_config:
  type: "docker"
  image: "python:3.11-slim"
  memory_limit: "512m"
  cpu_limit: "1.0"
  network_mode: "none"

quality_metrics:
  complexity_threshold: 10
  test_coverage_minimum: 80
  documentation_completeness: 90
  performance_requirements: true

llm_config:
  provider: "openai"
  model: "gpt-4"
  temperature: 0.2
  max_tokens: 2000
```

### Memory Requirements
- **Runtime Memory**: ~300MB (includes code execution sandbox)
- **Sandbox Environment**: ~512MB per execution
- **Code Storage**: ~50MB for generated artifacts
- **Test Execution**: ~100MB for test framework

## Architecture Overview

### Agent Coordination Pattern
```
Specification → Code Generator → Test Generator → Execution Engine
     ↑                                                    ↓
Quality Gate ← Code Refiner ← Test Analyzer ← Results Validator
```

### Data Flow
1. **Specification Parsing**: Extract requirements and constraints
2. **Code Generation**: Create initial implementation
3. **Test Creation**: Generate comprehensive test suite
4. **Execution**: Run code in sandboxed environment
5. **Analysis**: Evaluate results and identify improvements
6. **Refinement**: Iteratively improve code quality
7. **Validation**: Final quality assessment and packaging

### Execution Environment
```dockerfile
# Sandboxed execution environment
FROM python:3.11-slim
RUN pip install pytest black pylint mypy
WORKDIR /code
COPY requirements.txt .
RUN pip install -r requirements.txt
CMD ["python", "-m", "pytest", "-v"]
```

## Success Criteria

### Functional Requirements
- ✅ Generates syntactically correct code for 95% of specifications
- ✅ Produced code passes all generated tests
- ✅ Handles edge cases and error conditions appropriately
- ✅ Meets specified performance requirements

### Code Quality Metrics
- ✅ Cyclomatic complexity score <10 for all functions
- ✅ Test coverage >80% for generated code
- ✅ Code style compliance (PEP 8 for Python)
- ✅ Documentation completeness score >90%

### Performance Requirements
- ✅ Simple functions generated in <5 seconds
- ✅ Complex implementations completed in <30 seconds
- ✅ Test execution time <10 seconds
- ✅ Memory usage <500MB total per generation

## Enterprise Considerations

### Security
- Sandboxed code execution with network isolation
- Input validation and sanitization
- Code injection prevention
- Secure temporary file handling

### Compliance
- Code license compatibility checking
- Security vulnerability scanning
- Dependency audit and approval
- IP and copyright compliance

### Integration
- Version control system integration
- CI/CD pipeline compatibility
- Code review workflow integration
- Enterprise coding standards enforcement

## Error Scenarios

### Compilation/Syntax Errors
```python
# Response when generated code has syntax errors
{
  "code": "# Corrected implementation after syntax error",
  "execution_result": "SYNTAX_ERROR_CORRECTED",
  "error_details": "Fixed missing parenthesis on line 15",
  "iterations_required": 2,
  "quality_score": 0.85
}
```

### Test Failures
```python
# Handling when generated code fails tests
{
  "code": "# Implementation with test failures addressed",
  "execution_result": "TESTS_PASSING_AFTER_REFINEMENT",
  "test_results": {
    "total_tests": 12,
    "passed": 12,
    "failed": 0,
    "coverage": 85.2
  },
  "quality_score": 0.88
}
```

### Performance Issues
```python
# Response when code doesn't meet performance requirements
{
  "code": "# Optimized implementation",
  "execution_result": "PERFORMANCE_OPTIMIZED",
  "optimizations_applied": [
    "Algorithm complexity reduced from O(n²) to O(n log n)",
    "Memory usage optimized with generators",
    "Caching added for expensive operations"
  ],
  "quality_score": 0.92
}
```

### Sandbox Execution Failure
```python
# Fallback when sandbox environment fails
{
  "code": "# Generated code (execution validation unavailable)",
  "execution_result": "SANDBOX_UNAVAILABLE",
  "validation_method": "STATIC_ANALYSIS_ONLY",
  "confidence_level": "MEDIUM",
  "manual_review_recommended": true
}
```

## Testing Strategy

### Unit Tests
- Code generation pipeline component testing
- Syntax validation and formatting verification
- Test case generation accuracy assessment
- Sandbox environment isolation validation

### Integration Tests
- End-to-end code generation and execution
- Multi-language support validation
- Performance requirement compliance testing
- Quality metric calculation verification

### Security Tests
- Sandbox escape attempt prevention
- Malicious code injection resistance
- Resource usage limit enforcement
- Network isolation validation

### Performance Tests
- Code generation speed benchmarking
- Concurrent generation capacity testing
- Memory usage optimization validation
- Execution timeout handling verification

## Implementation Details

### Key Components
1. **Specification Parser**: Extracts requirements from natural language
2. **Code Generator**: Creates implementations using LLM with code patterns
3. **Test Generator**: Automatically creates comprehensive test suites
4. **Execution Engine**: Runs code in secure sandboxed environment
5. **Quality Analyzer**: Assesses code quality across multiple dimensions
6. **Refinement Controller**: Manages iterative improvement process

### Code Generation Strategies
- **Template-Based**: Uses proven patterns for common implementations
- **Test-Driven**: Generates tests first, then implements to pass tests
- **Incremental**: Builds complex functionality in smaller, testable pieces
- **Pattern Recognition**: Applies learned patterns from successful generations

### Quality Assessment Framework
```python
class CodeQualityAssessment:
    def __init__(self):
        self.metrics = {
            'syntactic_correctness': 0.25,
            'test_coverage': 0.20,
            'documentation': 0.15,
            'performance': 0.20,
            'maintainability': 0.20
        }

    def calculate_score(self, code_analysis):
        # Weighted scoring algorithm
        pass
```
