ElixirScope.AI.Predictive.ExecutionPredictor (elixir_scope v0.0.1)

Predicts execution paths, resource usage, and concurrency impacts based on historical execution data and code analysis.

This module implements machine learning models to:

  • Predict likely execution paths for function calls
  • Estimate resource requirements (memory, CPU, I/O)
  • Analyze concurrency bottlenecks and scaling factors
  • Identify edge cases and rarely-executed code paths

Summary

Functions

Analyzes concurrency impact for a function signature.

Returns a specification to start this module under a supervisor.

Gets current model statistics and performance metrics.

Performs batch predictions for multiple contexts.

Predicts the execution path for a given function call.

Predicts resource usage for a given execution context.

Starts the ExecutionPredictor GenServer.

Trains the prediction models with historical data.

Functions

analyze_concurrency_impact(function_signature)

Analyzes concurrency impact for a function signature.

Examples

iex> ExecutionPredictor.analyze_concurrency_impact({:handle_call, 3})
{:ok, %{
  bottleneck_risk: 0.7,
  recommended_pool_size: 10,
  scaling_factor: 0.85,
  contention_points: [:database_access, :file_io]
}}

child_spec(init_arg)

Returns a specification to start this module under a supervisor.

See Supervisor.

get_stats()

Gets current model statistics and performance metrics.

predict_batch(contexts)

Performs batch predictions for multiple contexts.

predict_path(module, function, args)

Predicts the execution path for a given function call.

Returns a prediction with confidence scores and alternative paths.

Examples

iex> ExecutionPredictor.predict_path(MyModule, :my_function, [arg1, arg2])
{:ok, %{
  predicted_path: [:entry, :condition_check, :main_logic, :exit],
  confidence: 0.85,
  alternatives: [
    %{path: [:entry, :error_handling, :exit], probability: 0.15}
  ],
  edge_cases: [
    %{type: :nil_input, probability: 0.02}
  ]
}}

predict_resources(context)

Predicts resource usage for a given execution context.

Examples

iex> context = %{function: :process_data, input_size: 1000}
iex> ExecutionPredictor.predict_resources(context)
{:ok, %{
  memory: 2048,  # KB
  cpu: 15.5,     # percentage
  io: 100,       # operations
  execution_time: 250  # milliseconds
}}

start_link(opts \\ [])

Starts the ExecutionPredictor GenServer.

train(training_data)

Trains the prediction models with historical data.