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function test_enhanced_workflow

Maturity: 44

A comprehensive test function that validates the EnhancedSQLWorkflow system by testing component initialization, request parsing, and data assessment capabilities.

File:
/tf/active/vicechatdev/full_smartstat/test_enhanced_workflow.py
Lines:
13 - 71
Complexity:
moderate

Purpose

This function serves as an integration test for the enhanced SQL workflow system. It verifies that all components (Config, StatisticalAgent, EnhancedSQLWorkflow) can be initialized correctly, tests the parsing of combined data and analysis requests, and validates data assessment functionality using a sample pandas DataFrame. The test provides detailed console output showing the success or failure of each step, making it useful for debugging and validation during development.

Source Code

def test_enhanced_workflow():
    """Test the enhanced workflow components"""
    print("=== Testing Enhanced SQL Workflow ===")
    
    try:
        # Initialize components
        config = Config()
        agent = StatisticalAgent(config)
        workflow = EnhancedSQLWorkflow(config, agent)
        
        print("āœ“ Components initialized successfully")
        
        # Test request parsing
        combined_request = """
        Data: Recent laboratory test results from the last 3 months with customer information
        Analysis: Analyze testing patterns, identify most common tests, and examine trends over time
        """
        
        print("\nTesting request parsing...")
        analysis_request = workflow._parse_combined_request(combined_request)
        
        print(f"āœ“ Parsed request:")
        print(f"  - Data description: {analysis_request.data_description}")
        print(f"  - Analysis description: {analysis_request.analysis_description}")
        print(f"  - Expected result type: {analysis_request.expected_result_type}")
        
        # Test data assessment with sample dataframe
        print("\nTesting data assessment...")
        import pandas as pd
        import numpy as np
        
        # Create sample dataframe
        sample_data = {
            'RequestNr': [1001, 1002, 1003, 1004, 1005],
            'DateCreated': pd.date_range('2025-01-01', periods=5),
            'sCustomer': ['CustomerA', 'CustomerB', 'CustomerA', 'CustomerC', 'CustomerB'],
            'TestResult': [1.5, 2.3, np.nan, 4.1, 3.7],
            'Status': ['Complete', 'Complete', 'Pending', 'Complete', 'Complete']
        }
        
        df = pd.DataFrame(sample_data)
        
        assessment = workflow._assess_data_for_analysis(df, analysis_request)
        
        print(f"āœ“ Data assessment completed:")
        print(f"  - Row count: {assessment['row_count']}")
        print(f"  - Column count: {assessment['column_count']}")
        print(f"  - Quality score: {assessment['data_quality_score']}/100")
        print(f"  - Is satisfactory: {assessment['is_satisfactory']}")
        
        if assessment['improvements']:
            print(f"  - Suggested improvements: {', '.join(assessment['improvements'])}")
        
        print("\nāœ“ Enhanced SQL Workflow test completed successfully!")
        
    except Exception as e:
        print(f"āœ— Test failed: {str(e)}")
        import traceback
        traceback.print_exc()

Return Value

This function does not return any value (implicitly returns None). It prints test results to stdout, including success indicators (āœ“) or failure indicators (āœ—) along with detailed information about each test step. If an exception occurs, it prints the error message and full traceback.

Dependencies

  • pandas
  • numpy
  • sys
  • os
  • traceback

Required Imports

import sys
import os
from enhanced_sql_workflow import EnhancedSQLWorkflow
from enhanced_sql_workflow import AnalysisRequest
from statistical_agent import StatisticalAgent
from config import Config
import pandas as pd
import numpy as np
import traceback

Conditional/Optional Imports

These imports are only needed under specific conditions:

import pandas as pd

Condition: required for creating sample test data

Required (conditional) 
import numpy as np

Condition: required for creating NaN values in test data

Required (conditional) 
import traceback

Condition: used only when exceptions occur to print full stack trace

Required (conditional)

Usage Example

# Run the test function directly
test_enhanced_workflow()

# Or call it from a test suite
if __name__ == '__main__':
    test_enhanced_workflow()

# Expected output:
# === Testing Enhanced SQL Workflow ===
# āœ“ Components initialized successfully
# Testing request parsing...
# āœ“ Parsed request:
#   - Data description: ...
#   - Analysis description: ...
# Testing data assessment...
# āœ“ Data assessment completed:
#   - Row count: 5
#   - Column count: 5
# āœ“ Enhanced SQL Workflow test completed successfully!

Best Practices

  • This is a test function and should be run in a development or testing environment, not in production
  • Ensure all required modules (enhanced_sql_workflow, statistical_agent, config) are available in the Python path before running
  • The function prints output directly to stdout; consider capturing output if running in automated test suites
  • The test uses hardcoded sample data; modify the sample_data dictionary to test different scenarios
  • Exception handling is comprehensive but prints to console; integrate with proper logging for production test suites
  • The function tests internal methods (_parse_combined_request, _assess_data_for_analysis) which may change; update tests if the API changes
  • Consider wrapping this in a proper test framework (pytest, unittest) for better integration with CI/CD pipelines

Tags

testing integration-test workflow sql data-analysis validation pandas statistical-agent request-parsing data-assessment unit-test

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