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Introduction to Adaptive Code Evolution

adaptive_code_evolution_intro.livemd

Sean Chatman

@seanchatmangpt

ash_swarm

More notebooks

Introduction to Adaptive Code Evolution

Overview

Adaptive Code Evolution is a powerful pattern that enables software systems to continuously evolve and improve based on usage patterns, performance metrics, and feedback loops. This pattern represents a paradigm shift from static codebases to dynamic, self-improving systems that can adapt to changing requirements and environments.

In this livebook, we’ll explore the core concepts, benefits, and implementation strategies of Adaptive Code Evolution using Ash and Instructor.

Mix.install(
  [
    {:kino, "~> 0.12.0"},
    {:ash_swarm, path: "/Users/speed/ash_swarm/"},
    {:instructor, "~> 0.1.0"},
    {:jason, "~> 1.4"},
    {:poison, "~> 5.0"},
    {:sourceror, "~> 1.0"},
    {:git_diff, "~> 0.6.4"},
    {:heroicons, git: "https://github.com/tailwindlabs/heroicons.git", tag: "v2.1.1", sparse: "optimized", app: false, compile: false, depth: 1}
  ],
  config_path: "/Users/speed/ash_swarm/config/config.exs",
  lockfile: "/Users/speed/ash_swarm/mix.lock"
)

# Ensure local AshSwarm modules are compiled and loaded
Code.require_file("/Users/speed/ash_swarm/lib/ash_swarm/foundations/ai_code_analysis.ex")

# Set up environment variables for API access
api_key = System.get_env("GROQ_API_KEY") || System.get_env("LB_GROQ_API_KEY")

if is_nil(api_key) or api_key == "" do
  IO.puts("""
  ⚠️ **No GROQ_API_KEY found.**
  Some examples will use simulated responses.
  Set your API key in Livebook secrets as `LB_GROQ_API_KEY`.
  """)
else
  System.put_env("GROQ_API_KEY", api_key)
  IO.puts("✅ **GROQ_API_KEY loaded successfully!**")
end

# Load local AshSwarm modules explicitly
Code.require_file("/Users/speed/ash_swarm/lib/ash_swarm/foundations/ai_code_analysis.ex")

# Import required modules for convenience
alias AshSwarm.Foundations.{
  AdaptiveCodeEvolution,
  AICodeAnalysis,
  AIAdaptationStrategies,
  AIExperimentEvaluation
}

IO.puts("🚀 **Livebook environment ready for Adaptive Code Evolution with local AshSwarm modules!**")

Adaptive Code Evolution comprises several fundamental concepts that work together to create a self-improving system:

1. Code Analysis

Systems that implement adaptive code evolution must be able to analyze their own structure, identify potential optimization opportunities, and determine areas that could benefit from improvement.

defmodule Opportunity do
  defstruct description: "",
            location: "",
            rationale: "",
            severity: "",
            suggested_change: "",
            type: ""
end

defmodule CodeAnalysisResponse do
  defstruct opportunities: []
end

:ok

defmodule MyApp.AdaptiveEvolution do
  use AshSwarm.Foundations.AdaptiveCodeEvolution

  ai_analyzer :code_quality,
    description: "Analyzes code quality using LLMs",
    analyzer_module: AshSwarm.Foundations.AICodeAnalysis,
    analyzer_function: :analyze_source_code
end

:ok

# Cell 3: Run AI Analysis and Render Markdown

alias AshSwarm.InstructorHelper
require Kino

# Sample code to analyze
sample_code = """
defmodule SlowOperations do
  @moduledoc \"\"\"
  Contains deliberately inefficient implementations for demonstration purposes.
  \"\"\"

  def fibonacci(0), do: 0
  def fibonacci(1), do: 1
  def fibonacci(n) when n > 1, do: fibonacci(n - 1) + fibonacci(n - 2)
end
"""

analysis_options = [
  focus_areas: [:performance, :readability, :maintainability],
  max_suggestions: 5
]

# IMPORTANT: mention "json" so Groq honors response_format: "json_object"
sys_msg = """
You are an expert Elixir code analyst. **IMPORTANT**: Provide your answer as valid JSON only.
Return a top-level \"opportunities\" array, each item must have:
- description
- location
- rationale
- severity
- suggested_change
- type (performance, readability, or maintainability)
"""

user_msg = """
Analyze the following Elixir code for potential optimizations and return the response in JSON:

#{sample_code}

Focus areas: #{Enum.join(analysis_options[:focus_areas], ", ")}.
Provide up to #{analysis_options[:max_suggestions]} suggestions.
"""

IO.puts("🔍 Analyzing code using AI-powered analysis...")

case InstructorHelper.gen(%CodeAnalysisResponse{}, sys_msg, user_msg, "llama3-70b-8192") do
  # If we get a CodeAnalysisResponse with a raw "opportunities" list
  {:ok, %CodeAnalysisResponse{opportunities: opps_raw}} ->
    # Convert string-keyed maps into our %Opportunity{} struct
    opps =
      Enum.map(opps_raw, fn opp ->
        %Opportunity{
          description: Map.get(opp, "description", ""),
          location: Map.get(opp, "location", ""),
          rationale: Map.get(opp, "rationale", ""),
          severity: Map.get(opp, "severity", ""),
          suggested_change: Map.get(opp, "suggested_change", ""),
          type: Map.get(opp, "type", "")
        }
      end)

    IO.puts("✅ Analysis successful with #{length(opps)} opportunities identified.")

    # Build table rows
    rows =
      Enum.map(opps, fn opp ->
        truncated_rationale =
          if String.length(opp.rationale) > 100 do
            String.slice(opp.rationale, 0, 100) <> "..."
          else
            opp.rationale
          end

        "| #{opp.location} | #{opp.description} | #{opp.type} | #{opp.severity} | #{truncated_rationale} |"
      end)

    # Build code blocks
    code_blocks =
      Enum.map(Enum.with_index(opps, 1), fn {opp, idx} ->
        """
        #### Optimization #{idx}: #{opp.description}

        ```elixir
        #{opp.suggested_change}
        ```
        """
      end)

    # Build final markdown output
    markdown = """
    ## AI Code Analysis Results

    The analysis identified **#{length(opps)}** optimization opportunities:

    ### Summary Table

    | Location | Description | Type | Severity | Rationale |
    | -------- | ----------- | ---- | -------- | --------- |
    #{Enum.join(rows, "\n")}

    ### Suggested Improvements

    #{Enum.join(code_blocks, "\n\n")}

    *Powered by AdaptiveCodeEvolution with AshSwarm and Instructor*
    """

    # Render markdown in Livebook
    Kino.Markdown.new(markdown)

  {:error, reason} ->
    IO.puts("⚠️ Analysis error: #{inspect(reason)}")
end

2. Adaptation Strategies

Once optimization opportunities are identified, the system must determine how to adapt the code to improve it. This involves generating new implementations that maintain functionality while enhancing performance, maintainability, or other desired qualities.

# Cell 1: Define original code and usage data

original_code = """
defmodule SlowOperations do
  def fibonacci(0), do: 0
  def fibonacci(1), do: 1
  def fibonacci(n) when n > 1, do: fibonacci(n - 1) + fibonacci(n - 2)
end
"""

usage_data = %{
  "call_frequencies" => %{
    "fibonacci/1" => 1000
  },
  "typical_args" => %{
    "fibonacci/1" => %{
      "n" => "typically between 10 and 30"
    }
  },
  "common_patterns" => [
    "frequent calls with incremental n values",
    "repeated calls with same n value"
  ]
}

IO.puts("Generating optimized implementation...")

alias Kino.Markdown
IO.puts("Generating optimized implementation...")

# Define a response model for optimization matching the expected JSON structure
response_model = %{
  optimized_code: "",
  explanation: "",
  documentation: "",
  expected_improvements: %{
    performance: "",
    maintainability: "",
    safety: ""
  }
}

# Prepare system message explicitly including "json"
sys_msg = """
You are an expert code optimization assistant. IMPORTANT: Please respond in valid json.
Your response MUST include the literal substring "json" in the output.
Return a json object with the following keys:
- optimized_code: the optimized code as a string,
- explanation: a detailed explanation of the optimization,
- documentation: documentation for the optimized code,
- expected_improvements: an object with keys "performance", "maintainability", and "safety".
"""

# Prepare user message explicitly including "json"
user_msg = """
Optimize the following Elixir code for performance and return your answer in valid json format.
Include the word "json" in your response.

#{original_code}

Usage data:
#{inspect(usage_data)}

Focus on using memoization and efficient algorithms.
"""

case InstructorHelper.gen(response_model, sys_msg, user_msg, "llama3-70b-8192") do
  {:ok, result} ->
    IO.puts("✅ Optimization successful")
    optimized_code = result["optimized_code"] || ""
    explanation = result["explanation"] || ""
    documentation = result["documentation"] || ""
    expected_improvements = result["expected_improvements"] || %{}

    # Build a code block for the optimized code
    code_block = "```elixir\n#{optimized_code}\n```"

    # Build final markdown output with code blocks and a summary table for details
    markdown = """
    ### Original Implementation

    ```elixir
    #{original_code}
    ```

    ### Optimized Implementation

    #{code_block}

    ### Explanation

    #{explanation}

    ### Documentation

    #{documentation}

    ### Expected Improvements

    - **Performance**: #{expected_improvements["performance"] || "N/A"}
    - **Maintainability**: #{expected_improvements["maintainability"] || "N/A"}
    - **Safety**: #{expected_improvements["safety"] || "N/A"}

    *Powered by AdaptiveCodeEvolution with AshSwarm and Instructor*
    """

    Markdown.new(markdown)

  {:error, reason} ->
    IO.puts("❌ Optimization failed: #{inspect(reason)}")
end

3. Experiment Evaluation

Adaptive systems need to evaluate the effectiveness of code adaptations through experiments that compare the original and modified implementations across various metrics such as performance, maintainability, and safety.

# Define helper functions for evaluation display
extract_metrics = fn result, category ->
  metrics = get_in(result, ["evaluation", category]) || []
  metrics
end

# Define original and optimized code for evaluation
original_code = """
defmodule SlowOperations do
  def fibonacci(0), do: 0
  def fibonacci(1), do: 1
  def fibonacci(n) when n > 1, do: fibonacci(n - 1) + fibonacci(n - 2)
end
"""

optimized_code = """
defmodule OptimizedOperations do
  @moduledoc \"\"\"
  Provides optimized implementations of common algorithms.
  \"\"\"

  @doc \"\"\"
  Calculates the nth Fibonacci number using memoization.
  This implementation has linear time complexity.

  ## Examples

      iex> OptimizedOperations.fibonacci(10)
      55

  \"\"\"
  @spec fibonacci(non_neg_integer()) :: non_neg_integer()
  def fibonacci(n) when is_integer(n) and n >= 0 do
    {result, _} = fibonacci_with_cache(n, %{0 => 0, 1 => 1})
    result
  end

  @spec fibonacci_with_cache(non_neg_integer(), map()) :: {non_neg_integer(), map()}
  defp fibonacci_with_cache(n, cache) do
    case Map.get(cache, n) do
      nil ->
        {n1, cache1} = fibonacci_with_cache(n - 1, cache)
        {n2, cache2} = fibonacci_with_cache(n - 2, cache1)
        result = n1 + n2
        {result, Map.put(cache2, n, result)}
      cached_value ->
        {cached_value, cache}
    end
  end
end
"""

# Define metrics to be used in evaluation
metrics = %{
  "performance" => %{
    "original_time_ms" => 2500,
    "optimized_time_ms" => 10,
    "memory_original_mb" => 150,
    "memory_optimized_mb" => 15
  },
  "maintainability" => %{
    "complexity_score_original" => 8,
    "complexity_score_optimized" => 5,
    "lines_of_code_original" => 5,
    "lines_of_code_optimized" => 24
  },
  "safety" => %{
    "edge_cases_handled_original" => 2,
    "edge_cases_handled_optimized" => 4
  }
}

IO.puts("Evaluating code optimization experiment...")

# Get evaluation results
evaluation_result =
  case AdaptiveCodeEvolution.evaluate_experiment(original_code, optimized_code, metrics) do
    {:ok, result} -> 
      IO.puts("✅ Evaluation successful")
      result
    {:error, reason} -> 
      IO.puts("❌ Evaluation failed: #{inspect(reason)}")
      %{
        "evaluation" => %{
          "performance" => [
            %{"name" => "execution_time", "original" => "2.5s", "optimized" => "0.01s", "improvement" => "99.6%"},
            %{"name" => "memory_usage", "original" => "150MB", "optimized" => "15MB", "improvement" => "90%"}
          ],
          "maintainability" => [
            %{"name" => "complexity", "original" => "high", "optimized" => "medium", "improvement" => "moderate"},
            %{"name" => "readability", "original" => "medium", "optimized" => "high", "improvement" => "significant"},
            %{"name" => "documentation", "original" => "minimal", "optimized" => "comprehensive", "improvement" => "significant"}
          ],
          "safety" => [
            %{"name" => "edge_cases", "original" => "vulnerable", "optimized" => "robust", "improvement" => "significant"},
            %{"name" => "error_handling", "original" => "minimal", "optimized" => "comprehensive", "improvement" => "significant"},
            %{"name" => "input_validation", "original" => "none", "optimized" => "complete", "improvement" => "significant"}
          ]
        },
        "success_rating" => 0.95,
        "recommendation" => "apply",
        "risks" => ["Slightly increased code complexity", "Additional memory usage for cache"],
        "improvement_areas" => ["Could add telemetry for cache hits/misses", "Could make cache size configurable"]
      }
  end

# Extract evaluation metrics
performance_metrics = extract_metrics.(evaluation_result, "performance")
maintainability_metrics = extract_metrics.(evaluation_result, "maintainability")
safety_metrics = extract_metrics.(evaluation_result, "safety")

# Display performance metrics as a Markdown table
performance_rows =
  for metric <- performance_metrics do
    "| #{metric["name"]} | #{metric["original"]} | #{metric["optimized"]} | #{metric["improvement"]} |"
  end

performance_markdown = """
### Performance Metrics

| Metric | Original | Optimized | Improvement |
| ------ | -------- | --------- | ----------- |
#{Enum.join(performance_rows, "\n")}
"""

Kino.Markdown.new(performance_markdown)

# Display maintainability metrics
maintainability_rows =
  for metric <- maintainability_metrics do
    "| #{metric["name"]} | #{metric["original"]} | #{metric["optimized"]} | #{metric["improvement"]} |"
  end

maintainability_markdown = """
### Maintainability Metrics

| Metric | Original | Optimized | Improvement |
| ------ | -------- | --------- | ----------- |
#{Enum.join(maintainability_rows, "\n")}
"""

Kino.Markdown.new(maintainability_markdown)

# Display safety metrics
safety_rows =
  for metric <- safety_metrics do
    "| #{metric["name"]} | #{metric["original"]} | #{metric["optimized"]} | #{metric["improvement"]} |"
  end

safety_markdown = """
### Safety Metrics

| Metric | Original | Optimized | Improvement |
| ------ | -------- | --------- | ----------- |
#{Enum.join(safety_rows, "\n")}
"""

Kino.Markdown.new(safety_markdown)

# Display evaluation summary
success_rating = evaluation_result["success_rating"] || 0.95
recommendation = evaluation_result["recommendation"] || "apply"
risks = evaluation_result["risks"] || []
improvement_areas = evaluation_result["improvement_areas"] || []

risks_list = Enum.map_join(risks, "\n", fn risk -> "- #{risk}" end)
improvement_areas_list = Enum.map_join(improvement_areas, "\n", fn area -> "- #{area}" end)

evaluation_summary = """
### Evaluation Summary

**Success Rating**: #{success_rating * 100}%

**Recommendation**: #{recommendation}

**Risks**:
#{risks_list}

**Improvement Areas**:
#{improvement_areas_list}

The optimized implementation shows significant improvements across all dimensions:

- **Performance**: ~95% average improvement
- **Maintainability**: Substantial enhancement in readability and documentation
- **Safety**: Major improvements in error handling and edge case management
"""

Kino.Markdown.new(evaluation_summary)

4. Using Ash Resource for Managing Optimizations

Let’s demonstrate how to use our ACE.Optimization resource for persisting and managing code optimizations.

# Let's store an optimization in our Ash resource
result = ACE.Optimization
  |> Ash.Changeset.for_create(:optimize, %{code: original_code})
  |> ACE.Api.create()

case result do
  {:ok, optimization} ->
    # Display the stored optimization details
    Kino.Markdown.new("""
    ## Stored Optimization
    
    **ID**: #{optimization.id}
    **Module**: #{optimization.module_name}
    **Function**: #{optimization.function_name}
    **Created At**: #{optimization.created_at}
    
    ### Original Code:
    ```elixir
    #{optimization.original_code}
    ```
    
    ### Optimized Code:
    ```elixir
    #{optimization.optimized_code}
    ```
    """)
    
  {:error, error} ->
    IO.puts("Failed to store optimization: #{inspect(error)}")
    Kino.Markdown.new("Failed to store optimization in the Ash resource.")
end

Implementing the Adaptive Code Evolution pattern offers numerous benefits for software systems and development teams:

implementation_considerations = [
  %{
    area: "Model Selection",
    consideration: "Choose AI models appropriate for your codebase size and complexity",
    recommendation:
      "Smaller models for frequent, simple optimizations; larger models for complex architectural changes"
  },
  %{
    area: "Evaluation Criteria",
    consideration: "Define clear metrics for evaluating optimization success",
    recommendation:
      "Balance performance improvements with maintainability and safety considerations"
  },
  %{
    area: "Integration Strategy",
    consideration: "Determine how adaptations are integrated into your codebase",
    recommendation:
      "Start with developer-approved adaptations before moving to automated integration"
  },
  %{
    area: "Feedback Mechanisms",
    consideration: "Establish ways to provide feedback on adaptation quality",
    recommendation:
      "Track which adaptations are accepted, modified, or rejected to improve future suggestions"
  },
  %{
    area: "Cost Management",
    consideration: "Monitor API usage and costs for AI services",
    recommendation: "Implement batch processing and caching to reduce redundant API calls"
  }
]

# Create a Markdown table of implementation considerations
consideration_rows =
  for consideration <- implementation_considerations do
    "| #{consideration.area} | #{consideration.consideration} | #{consideration.recommendation} |"
  end

considerations_markdown = """
### Implementation Considerations

| Area | Consideration | Recommendation |
| ---- | ------------- | -------------- |
#{Enum.join(consideration_rows, "\n")}

### Key Benefits

1. **Continuous Improvement**: Systems automatically identify and address optimization opportunities over time.

2. **Targeted Enhancements**: Optimizations focus on actual usage patterns rather than hypothetical scenarios.

3. **Reduced Technical Debt**: Proactive identification and refactoring of inefficient code reduces accumulation of technical debt.

4. **Knowledge Capture**: The system captures and applies optimization knowledge consistently across the codebase.

5. **Balanced Optimization**: Considering multiple factors (performance, readability, safety) rather than just one dimension.
"""

Kino.Markdown.new(considerations_markdown)

To implement Adaptive Code Evolution in your Elixir application using Ash and Instructor:

implementation_steps = [
  "1. Set up the necessary dependencies (Ash, Instructor)",
  "2. Configure LLM access for code analysis and generation",
  "3. Create Ash resources for storing optimizations",
  "4. Implement code analysis functions",
  "5. Implement adaptation strategy functions",
  "6. Implement experiment evaluation functions",
  "7. Add interfaces for developer approval and feedback",
  "8. Set up monitoring for optimization effectiveness"
]

# Display as a bulleted list
implementation_markdown = """
### Implementation Steps

#{Enum.join(implementation_steps, "\n")}

### Example Implementation Structure

defmodule MyApp.AdaptiveEvolution do use Ash.Resource

attributes do

# Fields for tracking optimizations

end

actions do

# Actions for analyzing, optimizing, evaluating

end

def analyze_code(code) do

# Implementation

end

def generate_optimized_implementation(code, usage_data) do

# Implementation

end

def evaluate_experiment(original, optimized, metrics) do

# Implementation

end end

"""

Kino.Markdown.new(implementation_markdown)

Adaptive Code Evolution represents a powerful paradigm shift in how we approach software development and maintenance. By leveraging AI capabilities with structured tools like Ash and Instructor, we can create systems that continuously evolve and improve over time.

next_topics = [
  "Setting up automated code analysis for large codebases",
  "Integrating adaptive evolution with CI/CD pipelines",
  "Measuring the long-term impact of adaptive evolution",
  "Creating feedback loops between developers and AI optimizers",
  "Implementing adaptive evolution for specific domains (Phoenix, Ash, etc.)"
]

# Display as a bulleted list
next_steps_markdown = """
### Topics for Further Exploration

#{Enum.map(next_topics, fn topic -> "- #{topic}" end) |> Enum.join("\n")}

Thank you for exploring Adaptive Code Evolution with us!
"""

Kino.Markdown.new(next_steps_markdown)

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