Multi-Language Support and Advanced Adaptation Strategies

Setup

In this livebook, we’ll explore how to extend the Adaptive Code Evolution pattern to support multiple programming languages and implement advanced adaptation strategies.

Mix.install([
  {:ash_swarm, path: "../"},
  {:kino, "~> 0.12.0"}
])

alias AshSwarm.Foundations.AIAdaptationStrategies
alias AshSwarm.InstructorHelper

Multi-Language Support

While the Adaptive Code Evolution pattern was initially designed for Elixir codebases, the core concepts can be extended to support multiple programming languages, each with their own unique optimization patterns and best practices.

Language Detection

The first step in supporting multiple languages is automatic detection of the programming language from code samples:

# Example language detection function
detect_language = fn code ->
  cond do
    String.contains?(code, "defmodule") -> "Elixir"
    String.contains?(code, "def ") && String.contains?(code, ":") && !String.contains?(code, "class ") -> "Elixir"
    String.contains?(code, "import ") && String.contains?(code, "def ") && !String.contains?(code, "defmodule") -> "Python"
    String.contains?(code, "class ") && String.contains?(code, "def ") -> "Python"
    String.contains?(code, "function") && String.contains?(code, "=>") -> "JavaScript"
    String.contains?(code, "import React") -> "JavaScript/React"
    String.contains?(code, "fn") && String.contains?(code, "->") && String.contains?(code, "let") -> "Rust"
    String.contains?(code, "#include") -> "C/C++"
    true -> "unknown language"
  end
end

test_samples = [
  %{name: "Elixir Example", code: "defmodule Test do\n  def hello, do: \"world\"\nend"},
  %{name: "Python Example", code: "def fibonacci(n):\n    if n <= 1:\n        return n\n    else:\n        return fibonacci(n-1) + fibonacci(n-2)"},
  %{name: "JavaScript Example", code: "function add(a, b) {\n  return a + b;\n}\n\nconst multiply = (a, b) => a * b;"}
]

results = Enum.map(test_samples, fn sample ->
  %{
    name: sample.name,
    detected_language: detect_language.(sample.code)
  }
end)

Kino.DataTable.new(results)

Language-Specific Optimization Patterns

Each programming language has its own set of idiomatic optimization patterns:

language_patterns = %{
  "Elixir" => [
    %{
      name: "Module attribute constants",
      description: "Use module attributes for constants instead of functions",
      example: "@constant_value 42 # instead of def constant_value, do: 42",
      benefit: "Evaluated at compile-time rather than runtime"
    },
    %{
      name: "Pattern matching dispatch",
      description: "Use pattern matching for control flow instead of conditionals",
      example: "def process(%{status: :ok} = data), do: ... # instead of if/else",
      benefit: "More declarative, often more performant"
    },
    %{
      name: "Stream for large collections",
      description: "Use Stream for lazy evaluation of large collection transformations",
      example: "data |> Stream.map(...) |> Stream.filter(...) |> Enum.into([])",
      benefit: "Reduces intermediate data structures"
    }
  ],
  
  "Python" => [
    %{
      name: "Generator expressions",
      description: "Use generators instead of building full lists in memory",
      example: "(x for x in range(1000)) # instead of [x for x in range(1000)]",
      benefit: "Memory efficient for large datasets"
    },
    %{
      name: "Function caching",
      description: "Use @functools.lru_cache for expensive function calls",
      example: "@functools.lru_cache(maxsize=None)\ndef fibonacci(n): ...",
      benefit: "Avoids redundant computations"
    },
    %{
      name: "NumPy vectorization",
      description: "Use NumPy for numerical operations instead of Python loops",
      example: "numpy.sum(array) # instead of sum(x for x in array)",
      benefit: "Orders of magnitude faster for numerical computations"
    }
  ],
  
  "JavaScript" => [
    %{
      name: "Array methods over loops",
      description: "Use built-in array methods instead of explicit loops",
      example: "array.filter(x => x > 0) # instead of for loops with conditions",
      benefit: "More declarative and often optimized internally"
    },
    %{
      name: "Memoization with closures",
      description: "Use closures to cache expensive function results",
      example: "const memoizedFn = memoize(expensiveFn)",
      benefit: "Avoids redundant computations"
    },
    %{
      name: "Debouncing/throttling",
      description: "Limit frequency of expensive operations",
      example: "const debouncedFn = debounce(expensiveFn, 200)",
      benefit: "Reduces computational load for frequent events"
    }
  ]
}

selected_language = Kino.Input.select("Select language to view patterns:", ["Elixir", "Python", "JavaScript"])

selected = Kino.Input.read(selected_language)
patterns = Map.get(language_patterns, selected, [])
Kino.DataTable.new(patterns)

Implementation in AshSwarm

To support multiple languages in AshSwarm, the following enhancements are needed:

implementation_steps = [
  %{
    step: "Language detection",
    description: "Add a function to detect programming language from code samples",
    module: "AshSwarm.InstructorHelper",
    complexity: "Low"
  },
  %{
    step: "Language-specific prompts",
    description: "Create language-specific optimization prompt templates",
    module: "AshSwarm.Foundations.AIAdaptationStrategies",
    complexity: "Medium"
  },
  %{
    step: "Pattern libraries",
    description: "Build libraries of optimization patterns for each language",
    module: "AshSwarm.Foundations.OptimizationPatterns",
    complexity: "High"
  },
  %{
    step: "Language-specific validators",
    description: "Implement validators to ensure generated code is syntactically correct",
    module: "AshSwarm.Foundations.CodeValidators",
    complexity: "Medium"
  },
  %{
    step: "CLI enhancements",
    description: "Update CLI to support language-specific options",
    module: "Mix.Tasks.AshSwarm.Adaptive.Evolve",
    complexity: "Low"
  }
]

Kino.DataTable.new(implementation_steps)

Advanced Adaptation Strategies

Beyond basic optimizations, advanced adaptation strategies can transform how codebases evolve over time.

Incremental vs. Whole-Module Adaptation

Different optimization approaches can be applied depending on code complexity and risk tolerance:

adaptation_approaches = [
  %{
    approach: "Whole-module replacement",
    description: "Generate a completely new implementation of a module",
    benefits: "Comprehensive optimization, architectural improvements",
    risks: "Higher chance of introducing bugs, more disruptive",
    suitable_for: "Small, isolated modules with good test coverage"
  },
  %{
    approach: "Incremental function replacement",
    description: "Replace individual functions one at a time",
    benefits: "Lower risk, easier to review and test",
    risks: "May miss cross-function optimization opportunities",
    suitable_for: "Critical modules where reliability is paramount"
  },
  %{
    approach: "Additive optimization",
    description: "Add optimized alternatives alongside original implementations",
    benefits: "Zero risk to existing functionality, easy rollback",
    risks: "Code bloat, confusion over which version to use",
    suitable_for: "Performance-critical functions where correctness must be maintained"
  },
  %{
    approach: "Guided refactoring",
    description: "AI suggests changes for human implementation",
    benefits: "Human oversight of all changes, knowledge transfer",
    risks: "Time-consuming, may miss optimization opportunities",
    suitable_for: "Complex business logic, poorly understood legacy code"
  }
]

Kino.DataTable.new(adaptation_approaches)

Meta-Adaptation: Evolving the Adaptation Process

The adaptation process itself can be evolved and optimized over time:

meta_adaptation = %{
  strategies: [
    %{
      name: "Adaptive prompt engineering",
      description: "Automatically refine prompts based on optimization success rates",
      implementation: "Track which prompts lead to successful optimizations and evolve them using genetic algorithms"
    },
    %{
      name: "Multi-model consensus",
      description: "Use multiple AI models and combine their outputs based on confidence scores",
      implementation: "Generate optimizations with different models, evaluate each, and select the best or combine strengths"
    },
    %{
      name: "Adaptation memory",
      description: "Build a database of successful adaptation patterns to guide future optimizations",
      implementation: "Store before/after code pairs with performance metrics to teach the system effective patterns"
    },
    %{
      name: "Self-optimizing evaluators",
      description: "Use machine learning to improve the accuracy of optimization evaluations",
      implementation: "Train models on human feedback about optimization quality to improve automated evaluation"
    }
  ]
}

Kino.DataTable.new(meta_adaptation.strategies)

Implementation Example: Python-Optimized Fibonacci

Let’s see an example of how the system would optimize a Python implementation of the Fibonacci algorithm:

python_code = """
def fibonacci(n):
    if n <= 0:
        return 0
    elif n == 1:
        return 1
    else:
        return fibonacci(n - 1) + fibonacci(n - 2)

def is_prime(num):
    if num <= 1:
        return False
    if num <= 3:
        return True
    if num % 2 == 0 or num % 3 == 0:
        return False
    i = 5
    while i * i <= num:
        if num % i == 0 or num % (i + 2) == 0:
            return False
        i += 6
    return True
"""

# Simulated AI-generated optimized Python code
optimized_python = """
import functools
import math

@functools.lru_cache(maxsize=None)
def fibonacci(n):
    \"\"\"Calculate the nth Fibonacci number using memoization.
    
    This implementation uses the built-in lru_cache decorator to automatically
    cache results, dramatically improving performance for recursive calls.
    
    Args:
        n: The position in the Fibonacci sequence (0-indexed)
        
    Returns:
        The nth Fibonacci number
    \"\"\"
    if n <= 0:
        return 0
    elif n == 1:
        return 1
    else:
        return fibonacci(n - 1) + fibonacci(n - 2)

def is_prime(num):
    \"\"\"Determine if a number is prime.
    
    Uses an optimized trial division algorithm that only checks factors
    up to the square root, and skips even numbers and multiples of 3.
    
    Args:
        num: The number to check for primality
        
    Returns:
        True if the number is prime, False otherwise
    \"\"\"
    if num <= 1:
        return False
    if num <= 3:
        return True
    if num % 2 == 0 or num % 3 == 0:
        return False
    
    # Only check up to the square root of num
    for i in range(5, int(math.sqrt(num)) + 1, 6):
        if num % i == 0 or num % (i + 2) == 0:
            return False
    return True
"""

Kino.Markdown.new("""
### Original Python Code:

#{python_code}

### Optimized Python Code:

#{optimized_python}

**Key Optimizations:**
1. Added `@functools.lru_cache` for memoization of fibonacci results
2. Improved prime checking algorithm to use `math.sqrt()` instead of `i * i <= num`
3. Added comprehensive docstrings with type information
4. Maintained identical function signatures for backward compatibility
""")

Advanced Multi-Language Adaptation Pipeline

Here’s an example of an advanced multi-language adaptation pipeline:

pipeline_diagram = """
┌────────────────┐     ┌─────────────────┐     ┌───────────────┐     ┌──────────────┐
│ Code Detection │────▶│ Language-Specific│────▶│ Optimization  │────▶│ Validation & │
│ & Analysis     │     │ Pattern Library  │     │ Generation    │     │ Testing      │
└────────────────┘     └─────────────────┘     └───────────────┘     └──────────────┘
       │                        │                      │                     │
       ▼                        ▼                      ▼                     ▼
┌────────────────┐     ┌─────────────────┐     ┌───────────────┐     ┌──────────────┐
│ Cross-Language │     │ Meta-Adaptation  │     │ Performance   │     │ Deployment & │
│ Optimization   │     │ Strategy         │     │ Benchmarking  │     │ Monitoring   │
└────────────────┘     └─────────────────┘     └───────────────┘     └──────────────┘
"""

Kino.Markdown.new("""
## Advanced Multi-Language Adaptation Pipeline

#{pipeline_diagram}

The advanced pipeline includes:

1. **Code Detection & Analysis**: Automatically identify language and code patterns
2. **Language-Specific Pattern Library**: Apply language-appropriate optimization techniques
3. **Optimization Generation**: Create optimized implementations using AI
4. **Validation & Testing**: Ensure optimizations maintain functionality
5. **Cross-Language Optimization**: Identify opportunities to leverage strengths of other languages
6. **Meta-Adaptation Strategy**: Self-improve adaptation algorithms
7. **Performance Benchmarking**: Measure and validate performance gains
8. **Deployment & Monitoring**: Safely deploy optimizations and monitor results
""")

Summary

In this livebook, we’ve explored:

1. Multi-Language Support

   • Language detection techniques
   • Language-specific optimization patterns
   • Implementation strategies for AshSwarm

2. Advanced Adaptation Strategies

   • Different approaches to code adaptation
   • Meta-adaptation for evolving the adaptation process itself
   • Practical examples of Python code optimization

3. Implementation Considerations

   • Pipeline design for multi-language support
   • How to gradually enhance AshSwarm’s capabilities

The Adaptive Code Evolution pattern, when extended to support multiple languages and advanced strategies, provides a powerful framework for continuous, AI-driven codebase improvement across diverse technology stacks.