09_code2logic_integration

Example 9: code2logic Integration

This example demonstrates integrating vallm with code2logic for logical code analysis and control flow visualization.

Overview

• code2logic: Extracts logical structures, control flow, functions
• vallm: Validates code quality and complexity
• Combined: Deep logical analysis with quality validation

Prerequisites

pip install code2logic vallm

What It Demonstrates

1. Function Extraction — code2logic extracts all functions and their signatures
2. Control Flow Analysis — branches, loops, conditions analysis
3. Quality Validation — vallm checks syntax, complexity, imports
4. Call Graph Building — vallm's graph builder shows dependencies
5. Visualization — Generate DOT graphs for control flow

Running the Example

cd examples/09_code2logic_integration
python main.py

Expected Output

🚀 code2logic + vallm Integration Example
============================================================

============================================================
Analyzing with code2logic...
============================================================
Found 2 functions:
  - process_order (8 branches)
  - calculate_discount (6 branches)

============================================================
Validating with vallm...
============================================================

Verdict: pass
Score: 0.95

Validator results:
  ✓ syntax: 1.00
  ✓ imports: 1.00
  ⚠ complexity: 0.85
      - process_order has high cyclomatic complexity

============================================================
Building call graph...
============================================================
Functions: ['process_order', 'calculate_discount']
Classes: ['OrderStatus']
Imports: 3
Calls: 2

📊 Report saved to .vallm/code2logic_integration_report.json

💡 Recommendations:
   - Simplify 1 high-complexity function

============================================================
FINAL SUMMARY
============================================================
Quality Score: 0.95/1.0
Verdict: PASS

How It Works

1. Extract Functions

from code2logic import extract_functions

functions = extract_functions(code)
for func in functions:
    print(f"{func['name']} ({func['complexity']} branches)")

2. Analyze Control Flow

from code2logic import analyze_control_flow

flow = analyze_control_flow(code, "process_order")
print(f"Branches: {len(flow['branches'])}")
print(f"Loops: {len(flow['loops'])}")
print(f"Conditions: {len(flow['conditions'])}")

3. Validate with vallm

from vallm import Proposal, validate

proposal = Proposal(code=code, language="python")
result = validate(proposal)

4. Build Call Graph

from vallm.core.graph_builder import build_python_graph

graph = build_python_graph(code, "module_name")
print(f"Functions: {graph.functions}")
print(f"Calls: {len(graph.calls)}")

5. Visualize

from code2logic.visualize import generate_dot

dot = generate_dot(code)
# Render with Graphviz
# dot -Tpng control_flow.dot -o flow.png

Report Format

{
  "tools": {
    "code2logic": true,
    "vallm": true
  },
  "analysis": {
    "logical_structure": {
      "functions": 2,
      "control_flow": {
        "branches": 8,
        "loops": 1,
        "conditions": 6
      }
    },
    "quality_validation": {
      "verdict": "pass",
      "score": 0.95
    },
    "call_graph": {
      "functions": 2,
      "calls": 2
    }
  },
  "recommendations": [
    "Simplify 1 high-complexity function"
  ]
}

Use Cases

Refactoring Assistant

# Find complex functions that need refactoring
def find_refactoring_candidates(codebase):
    candidates = []
    
    for file in codebase:
        # Logical analysis
        functions = extract_functions(file)
        
        for func in functions:
            flow = analyze_control_flow(file, func['name'])
            
            # Quality check
            proposal = Proposal(code=file, language="python")
            result = validate(proposal)
            
            if flow['branches'] > 10 or result.score < 0.7:
                candidates.append({
                    'function': func['name'],
                    'branches': flow['branches'],
                    'score': result.score
                })
    
    return candidates

Complexity Dashboard

# Generate complexity metrics dashboard
metrics = {
    'total_functions': len(functions),
    'avg_complexity': sum(f['complexity'] for f in functions) / len(functions),
    'high_complexity': sum(1 for f in functions if f['complexity'] > 10),
    'quality_score': vallm_result['score']
}

Documentation Generation

# Auto-generate documentation from analysis
for func in functions:
    flow = analyze_control_flow(code, func['name'])
    
    doc = f"""
## {func['name']}

**Complexity**: {func['complexity']} branches
**Quality Score**: {validate(Proposal(code=func['code'], language='python')).score:.2f}

### Control Flow
- Branches: {len(flow['branches'])}
- Loops: {len(flow['loops'])}
- Conditions: {len(flow['conditions'])}
"""
    print(doc)

Troubleshooting

code2logic not found

pip install code2logic

Graphviz not installed

# Ubuntu/Debian
sudo apt-get install graphviz

# macOS
brew install graphviz

# Windows
choco install graphviz

Further Reading

• code2logic documentation
• vallm graph analysis
• vallm complexity validation