QA Testing for Vibecoded E-Commerce Stores

The cart-to-checkout pipeline is where most e-commerce revenue is won or lost. AI-generated stores frequently miscalculate totals when users add multiple quantities, apply discount codes, or combine items with different tax rates. A human tester methodically builds carts with various product combinations — mixed taxable and non-taxable items, items with variants, bundled products — and verifies that every line item, subtotal, tax, and shipping calculation is correct.

Checkout abandonment often stems from bugs that are invisible during development. Testers check that the checkout flow handles address validation gracefully, that shipping options update correctly when the address changes, and that payment processing works with various card types. They also verify that users can go back and modify their cart without losing their progress through checkout.

A product catalog that looks complete can still have critical usability issues. AI-generated filtering and search often returns incorrect results when users combine multiple filters, sort by price while a filter is active, or search for products with special characters in their names. Human testers verify that the catalog accurately reflects inventory, that out-of-stock items are handled gracefully, and that product variants display the correct pricing and images.

Search functionality deserves particular attention in vibecoded stores. Testers check that search results are relevant, that typo tolerance works, and that searching for product attributes like color or size returns the right products. They also verify that empty search results provide helpful suggestions rather than a dead end.

The post-purchase experience is frequently neglected in vibecoded stores. Once an order is placed, testers verify that confirmation emails are sent promptly with accurate order details, that the order appears correctly in the customer's account, and that order status updates reflect actual fulfillment progress. AI-generated code often fails to handle edge cases like partial shipments or order modifications.

Testers also check refund and return flows end to end. Can a customer initiate a return? Does the refund amount calculate correctly, especially for partially returned orders? Is the inventory updated when items are returned? These post-purchase flows are critical for customer trust but are often the last thing AI tools generate — and the most likely to contain errors.