CodeSynth

Mission: Automates code generation, structural refactoring, and performance improvements.

Complex Python Example with Parallel Task Execution and Logging

import math
import logging
import concurrent.futures
from typing import Dict, Any, List

logging.basicConfig(level=logging.INFO)

# === Original Code with Performance Bottlenecks and Minimal Error Handling ===
def calculate_employee_compensation(employees: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """
    Calculates compensation for each employee based on custom rate multipliers, 
    overhead tasks, and historical performance. This version is naive, can be slow, 
    and lacks concurrency optimizations.
    """
    results = []
    for emp in employees:
        base = emp.get('base_salary', 50000)
        overhead_tasks = emp.get('tasks', [])
        overhead_sum = 0
        for task in overhead_tasks:
            overhead_sum += math.sqrt(len(task) * 20)  # naive overhead calculation

        # Performance multiplier is poorly handled
        performance_score = emp.get('performance_score', 0.0)
        total_comp = base + overhead_sum + (performance_score * 1.15)

        results.append({
            'id': emp.get('id', 'unknown'),
            'compensation': round(total_comp, 2)
        })
    return results

# === CodeSynth's Proposed Advanced Refactor ===
def calculate_employee_compensation_refactored(employees: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """
    Refactored version:
    1) Leverages concurrency to process employees in parallel.
    2) Includes robust error handling and validations (e.g., ensuring base_salary is numeric).
    3) Uses improved overhead computation with caching for repeated tasks.
    4) Logs key milestones for better observability.
    """
    if not isinstance(employees, list):
        logging.error("Expected 'employees' to be a list.")
        return []

    results = []
    # Cache overhead calculations for repeated tasks to reduce time complexity
    overhead_cache = {}

    def process_employee(emp: Dict[str, Any]) -> Dict[str, Any]:
        try:
            base = float(emp.get('base_salary', 50000))
            overhead_tasks = emp.get('tasks', [])
            perf_score = float(emp.get('performance_score', 0))

            overhead_sum = 0
            for task in overhead_tasks:
                if task not in overhead_cache:
                    overhead_cache[task] = math.sqrt(len(task) * 20)
                overhead_sum += overhead_cache[task]

            total_comp = base + overhead_sum + (perf_score * 1.15)
            employee_id = emp.get('id', 'unknown')

            logging.info(f"Processed employee ID {employee_id} with overhead {round(overhead_sum, 2)}.")
            return {'id': employee_id, 'compensation': round(total_comp, 2)}

        except (ValueError, TypeError) as e:
            logging.warning(f"Skipping employee due to data error: {str(e)} => {emp}")
            return {'id': 'invalid', 'compensation': 0}

    # Utilize ThreadPoolExecutor for parallel processing
    with concurrent.futures.ThreadPoolExecutor(max_workers=4) as executor:
        future_to_emp = {executor.submit(process_employee, emp): emp for emp in employees}
        for future in concurrent.futures.as_completed(future_to_emp):
            result = future.result()
            results.append(result)

    return results

# Example usage:
# employees_data = [
#     {'id': 101, 'base_salary': 60000, 'tasks': ['reporting', 'analysis'], 'performance_score': 2.5},
#     {'id': 102, 'base_salary': 52000, 'tasks': ['data_entry', 'meetings'], 'performance_score': 1.2},
#     {'id': 103, 'base_salary': 75000, 'tasks': ['architecture', 'design'], 'performance_score': 3.1},
# ]
# refined_results = calculate_employee_compensation_refactored(employees_data)
# print(refined_results)