🔄 Testing Integration Guide

Workflow 1: Daily Development Testing

Scenario: Developer making firmware changes

# Morning: Set baseline
python regression_suite.py --run-all --set-baseline

# After code changes: Quick validation
python test_automation.py --all

# Detailed test with monitoring
python live_monitor.py &  # Terminal 1
python test_automation.py --workout intervals  # Terminal 2

# End of day: Regression check
python regression_suite.py --run-all --compare

# Generate report
python test_reporter.py --results test_results.json

Expected Time: 20 minutes total

Tools Used: All 5

Workflow 2: Pre-Release Validation

Scenario: Preparing for production release

Day 1: Full Regression Suite

python regression_suite.py --run-all
python test_reporter.py --results regression_results_*.json

Day 2: Extended Stability Test

python regression_suite.py --category stability
# Runs for 5 minutes minimum

Day 3: Real-World Simulation

# iOS App: Run all 10 workout scenarios
# OR via command line:
for workout in intervals endurance recovery tabata ftp; do
    python test_automation.py --workout $workout
    sleep 30
done

Final: Comprehensive Report

python test_reporter.py --results test_results.json \
    --output release_validation_report.html

Expected Time: 3 hours spread over 3 days

Deliverable: HTML report for stakeholders

Workflow 3: Continuous Integration

GitHub Actions Integration:

# .github/workflows/test.yml
name: Automated Testing

on: [push, pull_request]

jobs:
  test:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v2
      
      - name: Setup Python
        uses: actions/setup-python@v2
        with:
          python-version: '3.9'
      
      - name: Install dependencies
        run: pip install colorama tabulate
      
      - name: Run regression tests
        run: python regression_suite.py --run-all --quick
      
      - name: Generate report
        run: python test_reporter.py --results regression_results_*.json
      
      - name: Upload report
        uses: actions/upload-artifact@v2
        with:
          name: test-report
          path: test_report_*.html
      
      - name: Check for regressions
        run: python regression_suite.py --compare || true

Technique 1: Parallel Testing

Test multiple devices simultaneously:

#!/bin/bash
# parallel_test.sh

# Device 1
python test_automation.py --workout intervals --node-id 1 &
PID1=$!

# Device 2
python test_automation.py --workout endurance --node-id 2 &
PID2=$!

# Device 3
python test_automation.py --workout recovery --node-id 3 &
PID3=$!

# Wait for all
wait $PID1 $PID2 $PID3

echo "All tests complete!"

Technique 2: Data Collection Pipeline

# collect_and_analyze.py
import subprocess
import json
from datetime import datetime

def run_test_collect_data():
    # Run test
    subprocess.run(["python", "test_automation.py", 
                   "--workout", "intervals"])
    
    # Collect results
    with open("test_results.json", "r") as f:
        results = json.load(f)
    
    # Analyze
    tests = results['tests']
    avg_power = sum(t['avg_power'] for t in tests) / len(tests)
    
    # Store in CSV
    with open("performance_log.csv", "a") as f:
        f.write(f"{datetime.now()},{avg_power}\n")
    
    # Generate report
    subprocess.run(["python", "test_reporter.py", 
                   "--results", "test_results.json"])

if __name__ == "__main__":
    run_test_collect_data()

Technique 3: Alerting System

# alert_on_failure.py
import subprocess

def send_alert(subject, body):
    # Email/Slack/Discord notification
    print(f"ALERT: {subject}")
    print(body)

def run_tests_with_alerts():
    result = subprocess.run(
        ["python", "regression_suite.py", "--run-all"],
        capture_output=True
    )
    
    if result.returncode != 0:
        send_alert(
            "🚨 Test Failure Detected",
            "Regression tests failed. Check logs."
        )
    else:
        send_alert(
            "✅ All Tests Passed",
            "Regression suite completed successfully."
        )

if __name__ == "__main__":
    run_tests_with_alerts()

Export Data for Analysis

# export_metrics.py
import json
import csv

def export_to_csv(json_file, csv_file):
    with open(json_file, 'r') as f:
        data = json.load(f)
    
    with open(csv_file, 'w', newline='') as f:
        writer = csv.writer(f)
        writer.writerow(['Timestamp', 'Test', 'Status', 
                        'Avg_Power', 'Avg_HR'])
        
        for test in data['tests']:
            writer.writerow([
                data['timestamp'],
                test['name'],
                test['status'],
                test.get('avg_power', 'N/A'),
                test.get('avg_hr', 'N/A')
            ])

export_to_csv('test_results.json', 'analysis.csv')

Trend Analysis

# trend_analysis.py
import pandas as pd
import matplotlib.pyplot as plt

def plot_trends(csv_file):
    df = pd.read_csv(csv_file)
    
    plt.figure(figsize=(12, 6))
    plt.plot(df['Timestamp'], df['Avg_Power'], marker='o')
    plt.title('Average Power Trend')
    plt.xlabel('Test Run')
    plt.ylabel('Power (W)')
    plt.xticks(rotation=45)
    plt.tight_layout()
    plt.savefig('power_trend.png')
    
    print("✅ Trend plot saved")

plot_trends('analysis.csv')