Generating and Testing QR Codes for Inventory Management in QA

In quality assurance (QA), ensuring the accuracy and functionality of inventory management systems is critical. One effective way to test product IDs in an inventory application is by generating and validating QR codes. In this blog post, I’ll walk you through how I used Python to create custom QR codes for testing product IDs and share insights on integrating this into your QA testing workflow.

Why QR Codes for QA Testing?

QR codes are a robust way to encode product IDs, allowing for quick scanning and validation in inventory applications. By generating QR codes programmatically, QA teams can:

• Simulate real-world product data.
• Test scanning functionality and data accuracy.
• Automate validation of product IDs against a database.
• Ensure error handling for malformed or invalid IDs.

In this example, I focused on testing a set of product IDs for an inventory application, generating QR codes for each ID and saving them as images for further testing.

The Python Code

Below is the Python script I used to generate QR codes for a list of product IDs. The script uses the qrcode library to create QR codes and save them as PNG files.

#!/usr/bin/python3
import datetime
import qrcode
# List of QR Code data (product IDs)
qr_codes = [
    "22001185000-TV825862",
    "46002897000-AV301140",
    "46002897000-AV320052",
    "46003686000-TS062286",
    "46200239000-HRB3019056",
    "46039182000-HRC0994569",
    "22031593000-0hfg7ddw502291r",
    "46003251000-BA30816015",
    "46002566000-VT5575px;32991",
    "22010473000-zt600331",
    "46200240000-MLSVTXLHQ-7",
    "46200240000-HRC2167455",
    "46074803000-MM0O05JGX-0",
    "46074803000-HRB2539864",
    "46088955000-kc1008395"
]
# Function to generate and save QR codes
def generate_qr_codes(codes):
    for i, code in enumerate(codes):
        # Create QR code instance
        qr = qrcode.QRCode(
            version=1,
            error_correction=qrcode.constants.ERROR_CORRECT_L,
            box_size=10,
            border=4,
        )
        qr.add_data(code)
        qr.make(fit=True)
        # Create an image from the QR Code instance
        img = qr.make_image(fill='black', back_color='white')
        # Save the image to a file
        img.save(f"{code}.png")
# Generate and save QR codes
generate_qr_codes(qr_codes)

How It Works

1. Dependencies: The script uses the qrcode library, which you can install via pip install qrcode pillow. The Pillow library is required for image handling.
2. Product IDs: The qr_codes list contains product IDs in various formats, simulating real-world data with different prefixes, suffixes, and lengths.
3. QR Code Generation:
   • A QRCode instance is created with:
     • version=1 (smallest QR code size, sufficient for short IDs).
     • error_correction=ERROR_CORRECT_L (low error correction, suitable for most use cases).
     • box_size=10 (pixel size of each QR code module).
     • border=4 (white space around the QR code).
   • Each product ID is added to the QR code, and the make method generates the QR code matrix.
4. Image Creation and Saving: The QR code is converted to a black-and-white PNG image and saved with the product ID as the filename (e.g., 22001185000-TV825862.png).

QA Testing Workflow

Here’s how I incorporated this script into my QA testing process:

1. Generate QR Codes:
   • Run the script to create QR code images for all product IDs.
   • Verify that each image is created and named correctly.
2. Test Scanning Functionality:
   • Use a QR code scanner (e.g., a mobile app or the inventory application’s scanner) to read each QR code.
   • Validate that the scanned data matches the original product ID.
3. Validate Against Database:
   • Check that the scanned product ID exists in the inventory database.
   • Test edge cases, such as invalid or duplicate IDs.
4. Error Handling:
   • Test malformed product IDs (e.g., too long, special characters).
   • Ensure the application handles scanning errors gracefully.

Lessons Learned

• Data Diversity: Include a variety of product ID formats in your test data to mimic real-world scenarios.
• Scalability: For large datasets, consider batch processing or parallelizing QR code generation to save time.
• Validation: Always validate QR code content programmatically to catch discrepancies early.
• Error Correction: Adjust the error_correction level (e.g., ERROR_CORRECT_M or ERROR_CORRECT_H) if QR codes will be printed or scanned in challenging conditions.