AI-Powered Code Analysis and Optimization

Setup

This livebook explores how AI is used to analyze and optimize code in the Adaptive Code Evolution pattern.

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

alias AshSwarm.Foundations.AICodeAnalysis
alias AshSwarm.Foundations.AIAdaptationStrategies
alias AshSwarm.Foundations.AIExperimentEvaluation
alias AshSwarm.InstructorHelper

AI in Code Analysis

Large Language Models (LLMs) have demonstrated remarkable capabilities in understanding, analyzing, and generating code. In the context of Adaptive Code Evolution, AI plays several critical roles:

1. Static Code Analysis

LLMs can examine code structure, identify anti-patterns, detect potential bugs, and suggest improvements by leveraging their understanding of programming languages and best practices.

# Sample code to analyze
code = """
defmodule SlowCryptography do
  def encrypt(text, key) do
    # Inefficient implementation - creates many intermediate lists
    text
    |> String.to_charlist()
    |> Enum.map(fn char -> 
      char + key 
    end)
    |> Enum.map(fn char -> 
      rem(char, 255) 
    end)
    |> List.to_string()
  end
  
  def decrypt(text, key) do
    # Duplicated logic with slight variation
    text
    |> String.to_charlist()
    |> Enum.map(fn char -> 
      char - key 
    end)
    |> Enum.map(fn char -> 
      rem(char + 255, 255) 
    end)
    |> List.to_string()
  end
end
"""

# Example analysis that would be produced by AI
analysis = %{
  issues: [
    %{
      type: "performance",
      location: "encrypt/2",
      description: "Multiple sequential Enum.map operations create unnecessary intermediate lists",
      suggestion: "Combine operations into a single Enum.map or use Enum.reduce"
    },
    %{
      type: "duplication",
      location: "encrypt/2 and decrypt/2",
      description: "Significant code duplication between these two functions",
      suggestion: "Extract common functionality into a helper function"
    },
    %{
      type: "algorithm",
      location: "encrypt/2 and decrypt/2",
      description: "Simple character substitution encryption is not secure",
      suggestion: "Consider using a standard encryption library like :crypto"
    }
  ]
}

Kino.DataTable.new(analysis.issues)

2. Contextual Understanding

Unlike traditional static analysis tools, LLMs can understand code in context—considering function purposes, variable naming conventions, and relationships between components.

# Contextual understanding example
contextual_analysis = %{
  module_purpose: "Simple character-based encryption (not for production use)",
  function_roles: %{
    "encrypt/2": "Transforms plaintext by shifting character codes",
    "decrypt/2": "Reverses the encryption process"
  },
  improvement_areas: [
    "Code organization: Extract shared functionality",
    "Performance: Combine transformations to reduce intermediary lists",
    "Security: Replace with proper cryptographic algorithms if used in production",
    "Documentation: Add warnings about security limitations"
  ],
  identified_conventions: [
    "Function names clearly indicate purpose",
    "Simple parameter naming"
  ]
}

Kino.Markdown.new("""
**Module Purpose**: #{contextual_analysis.module_purpose}

**Function Roles**:
- encrypt/2: #{contextual_analysis.function_roles["encrypt/2"]}
- decrypt/2: #{contextual_analysis.function_roles["decrypt/2"]}

**Improvement Areas**:
#{Enum.map(contextual_analysis.improvement_areas, fn area -> "- #{area}" end) |> Enum.join("\\n")}
""")

AI in Code Optimization

Once analysis is complete, AI can generate optimized implementations that address identified issues while maintaining the original functionality.

1. Optimization Generation

AI can generate multiple optimization strategies, each with different tradeoffs between performance, readability, and maintainability.

# Optimized version that would be generated by AI
optimized_code = """
defmodule OptimizedCryptography do
  @moduledoc \"\"\"
  Provides simple character substitution encryption functions.
  
  Warning: This implementation is for educational purposes only and is not
  cryptographically secure. Do not use for sensitive data.
  \"\"\"
  
  @doc \"\"\"
  Encrypts text using a simple character substitution cipher.
  
  ## Parameters
  
    * `text` - The plaintext to encrypt
    * `key` - The encryption key (a number)
    
  ## Examples
  
      iex> OptimizedCryptography.encrypt("hello", 5)
      "mjqqt"
  \"\"\"
  def encrypt(text, key), do: transform_text(text, &(&1 + key))
  
  @doc \"\"\"
  Decrypts text that was encrypted using the same key.
  
  ## Parameters
  
    * `text` - The encrypted text
    * `key` - The encryption key (same as used for encryption)
    
  ## Examples
  
      iex> OptimizedCryptography.decrypt("mjqqt", 5)
      "hello"
  \"\"\"
  def decrypt(text, key), do: transform_text(text, &(&1 - key))
  
  # Private helper function to handle the character transformation
  defp transform_text(text, transform_fn) do
    text
    |> String.to_charlist()
    |> Enum.map(fn char -> 
      char
      |> transform_fn.()
      |> rem(255)
      |> normalize_char()
    end)
    |> List.to_string()
  end
  
  # Ensure character value stays within the valid range
  defp normalize_char(char) when char < 0, do: char + 255
  defp normalize_char(char), do: char
end
"""

Kino.Markdown.new("```elixir\n#{optimized_code}\n```")

2. Optimization Rationale

AI can provide detailed explanations of the optimizations performed and their expected impacts:

optimization_rationale = %{
  refactorings: [
    %{
      type: "Extract method",
      description: "Created transform_text/2 helper function to eliminate code duplication",
      benefits: "Reduces code size by 40%, improves maintainability"
    },
    %{
      type: "Combine operations",
      description: "Combined sequential Enum.map calls into a single operation",
      benefits: "Reduces intermediate list creation, improves performance"
    },
    %{
      type: "Add proper documentation",
      description: "Added module documentation with security warning and function docs with examples",
      benefits: "Improves code understandability and correct usage"
    },
    %{
      type: "Add helper function",
      description: "Created normalize_char/1 to handle negative character values",
      benefits: "Makes the normalization logic explicit and reusable"
    }
  ],
  expected_improvements: %{
    performance: "~20% faster for inputs longer than 100 characters due to reduced intermediate list creation",
    maintainability: "Significantly improved due to reduced duplication and better documentation",
    safety: "Slightly improved with explicit warnings about security limitations"
  }
}

Kino.DataTable.new(optimization_rationale.refactorings)

AI in Optimization Evaluation

After generating optimizations, AI can evaluate their effectiveness:

evaluation_data = """
Original implementation:
- Performance metrics: 2.5ms average execution time for 1000 character input
- Memory usage: Creates 4 intermediate lists per operation
- Cyclomatic complexity: 2 per function
- Duplication: 80% code duplication between functions

Optimized implementation:
- Performance metrics: 1.9ms average execution time for 1000 character input
- Memory usage: Creates 2 intermediate lists per operation
- Cyclomatic complexity: 1 per public function, 2 for private helper
- Duplication: 0% (extracted common functionality)
"""

evaluation_result = %{
  metrics: [
    %{metric: "Execution time", original: "2.5ms", optimized: "1.9ms", improvement: "24%"},
    %{metric: "Memory usage", original: "4 lists", optimized: "2 lists", improvement: "50%"},
    %{metric: "Code duplication", original: "80%", optimized: "0%", improvement: "100%"},
    %{metric: "Lines of code", original: "28", optimized: "32", improvement: "-14% (justified)"}
  ],
  success_rating: 0.85,
  recommendation: "Accept optimization",
  notes: "The slight increase in code size is justified by the improved structure, documentation, and elimination of duplication."
}

Kino.DataTable.new(evaluation_result.metrics)

The AI Adaptation System in AshSwarm

AshSwarm implements these AI capabilities through three core modules:

1. AICodeAnalysis: Analyzes code to identify optimization opportunities
2. AIAdaptationStrategies: Generates optimized implementations
3. AIExperimentEvaluation: Evaluates the effectiveness of optimizations

Let’s examine the core functionality of these modules:

The Adaptation Process

adaptation_process = %{
  steps: [
    %{
      step: "Code Analysis",
      module: "AICodeAnalysis",
      function: "analyze_code/2",
      description: "Examines code structure and identifies optimization opportunities"
    },
    %{
      step: "Optimization Generation",
      module: "AIAdaptationStrategies",
      function: "generate_optimized_implementation/3",
      description: "Creates optimized implementations based on analysis results"
    },
    %{
      step: "Experiment Design",
      module: "AIExperimentEvaluation",
      function: "design_experiment/3",
      description: "Designs experiments to evaluate optimizations"
    },
    %{
      step: "Optimization Evaluation",
      module: "AIExperimentEvaluation",
      function: "evaluate_experiment/4",
      description: "Evaluates optimization effectiveness using experiments"
    },
    %{
      step: "Adaptation Application",
      module: "AdaptiveCodeEvolution",
      function: "apply_adaptation/2",
      description: "Applies successful adaptations to the codebase"
    }
  ]
}

Kino.DataTable.new(adaptation_process.steps)

AI Models and Performance Considerations

Different AI models offer varying capabilities for code analysis and optimization:

model_comparison = [
  %{
    model: "llama3-8b-8192",
    strengths: "Fast, cost-effective, good for simple optimizations",
    limitations: "May struggle with complex code structures, limited reasoning depth",
    recommended_for: "Frequent, incremental optimizations of small functions"
  },
  %{
    model: "llama3-70b-8192",
    strengths: "Strong reasoning capabilities, high-quality optimizations, deeper analysis",
    limitations: "Higher computational cost, slower response times",
    recommended_for: "Complex optimizations, architectural improvements, critical code paths"
  },
  %{
    model: "gpt-4o",
    strengths: "Excellent comprehension of complex systems, state-of-the-art reasoning",
    limitations: "Highest cost, API-dependent, potential vendor lock-in",
    recommended_for: "System-level optimizations, critical performance bottlenecks"
  }
]

Kino.DataTable.new(model_comparison)

Summary

AI-powered code analysis and optimization enables the Adaptive Code Evolution pattern by:

1. Identifying opportunities for improvement that might be missed by human developers
2. Generating optimized implementations that maintain functionality while enhancing performance
3. Evaluating the effectiveness of optimizations to ensure they deliver real benefits
4. Providing detailed explanations and documentation of optimization rationales

In the next livebook, we’ll explore multi-language support and advanced adaptation strategies.