AI-Driven Remediation Testing

"""Fault injection service implementation.""" import logging import uuid from typing import Dict, Optional logger = logging.getLogger(__name__) class FaultService: """ Service for injecting and cleaning up faults. This is a stub implementation. In production, this would integrate with chaos engineering tools like Chaos Mesh, Litmus, etc. """ def __init__(self): """Initialize fault service.""" self.active_faults: Dict[str, Dict] = {} async def inject( self, fault_type: str, params: Dict[str, str], run_id: str ) -> tuple[bool, str, str]: """ Inject a fault into the system. Args: fault_type: Type of fault (pod_kill, network_delay, etc.) params: Fault-specific parameters run_id: Scenario run identifier Returns: Tuple of (success, fault_id, message) """ fault_id = f"fault-{uuid.uuid4().hex[:8]}" logger.info( f"Injecting fault: type={fault_type}, params={params}, run_id={run_id}" ) # Store fault info self.active_faults[fault_id] = { "type": fault_type, "params": params, "run_id": run_id, } # In production, integrate with chaos engineering platform # Example integrations: # - Chaos Mesh: Create ChaosExperiment CRD # - Litmus: Create ChaosEngine # - Gremlin: Call Gremlin API message = f"Fault {fault_type} injected successfully (stub)" logger.info(f"Fault {fault_id}: {message}") return True, fault_id, message async def cleanup(self, fault_id: str, run_id: str) -> tuple[bool, str]: """ Clean up an injected fault. Args: fault_id: Fault identifier run_id: Scenario run identifier Returns: Tuple of (success, message) """ logger.info(f"Cleaning up fault: fault_id={fault_id}, run_id={run_id}") if fault_id not in self.active_faults: message = f"Fault {fault_id} not found" logger.warning(message) return False, message fault_info = self.active_faults.pop(fault_id) # In production, integrate with chaos engineering platform # Example: Delete ChaosExperiment, stop attack, etc. message = f"Fault {fault_info['type']} cleaned up successfully (stub)" logger.info(message) return True, message