Research alternative solutions for C# to C++ transformation

[konard]

Summary

This PR addresses issue #43 by researching alternative solutions for C# to C++ code transformation, with a focus on the three approaches discussed in the referenced Habr article.

Research Findings

The investigation covered three main memory management strategies:

1. ✅ Reference Counting with Smart Pointers (Chosen approach in article)

   • Custom SmartPtr class that dynamically switches between strong/weak reference modes
   • Provides automatic memory management similar to C# GC
   • Handles circular references through weak pointers
   • Maintains C++ performance characteristics

2. ❌ Garbage Collection for C++ (Rejected)

   • Using existing GC like Boehm GC
   • Rejected due to client code limitations and performance impact
   • Conflicts with C++ deterministic destruction principles

3. ❌ Static Analysis (Dismissed)

   • Determine object deletion points through code analysis
   • Dismissed due to high complexity and requirement to analyze both library and client code

Alternative Transformation Tools (2024)

Research identified several alternative C# to C++ transformation approaches:

• AlterNative: .NET to C++ translator using AST transformations
• AI-based solutions: Tools like GitHub Copilot (good for basic conversion, requires debugging)
• Commercial tools: CodePorting.Native (professional-grade, paid)
• Manual approaches: Mono, PInvoke, IDE features

Current Implementation Analysis

The existing RegularExpressions.Transformer.CSharpToCpp uses:

• Regex-based transformation rules for syntax conversion
• Multi-stage processing (FirstStage, LastStage rules)
• Pattern matching for C# language constructs
• Both C# and Python implementations

Deliverables

This PR includes:

• 📄 experiments/alternative-solutions-research.md: Comprehensive research documentation
• 🔬 experiments/simple-memory-demo.cpp: Working C++ demonstration of all three memory management approaches
• ⚡ experiments/memory-management-examples.cpp: Detailed implementation examples with advanced patterns
• 🐍 experiments/transformation-alternatives-demo.py: Python demo showing AST-based vs regex-based transformation approaches

Key Insights

1. Memory Management: Smart pointer approach from Habr article represents optimal balance between automation and performance
2. Current Approach: Regex-based transformation works well for syntax conversion but has limited semantic understanding
3. Enhancement Opportunities: AST-based transformation could improve handling of complex language constructs
4. Practical Recommendations: Consider hybrid approach combining current regex method with semantic analysis layers

Test Plan

• Research and document alternative approaches
• Create working C++ examples demonstrating memory management strategies
• Implement Python demonstration of transformation alternatives
• Test all code examples (compile and run successfully)
• Validate findings against current codebase patterns

🤖 Generated with Claude Code

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Resolves #43