Top 8 Bugs Caught by Our QA Team in 2025 (And How We Found Them)

Over the past year, our team worked alongside product and engineering teams to uncover issues that could have quietly undermined performance, security, or user experience if left undetected.

Below are the top bugs we caught this year and how we found them. 

1. Edge-Case Checkout Flow Failures

The issue:
On seasonal promotion drove a surge in traffic for a D2C e-commerce site. Most customers completed checkout without issue, but a subset ran into payment gateway failures when they applied a promo code, and then switched payment methods before placing the order. 

Why this happens:
Checkout flows span pricing, promos, tax calculation, inventory, and payments. When state changes mid-flow aren’t recalculated consistently, edge cases appear.

How to prevent:
Testing was created for non-linear checkout paths and rapid state changes. Engineering hardened state management and validation, to be able to handle multiple payment and cart scenarios.

2. Third-Party Integration Failures After API Updates 

The issue:
A consumer finance app relied on third-party services for identity verification and payment processing. After a routine API update from one provider, certain transactions began failing silently. Some users couldn’t complete account funding; others saw delayed confirmations. The failures only occurred for specific user profiles and transaction types, making the issue difficult to detect without targeted testing.

Why this happens:
Third-party services evolve independently. Simple changes such as new required fields, modified response formats, or rate limit adjustments can introduce breaking behavior that doesn’t surface as a hard error. Without explicit validation, these issues often appear first in production.

How to prevent:
Create testing around third-party integrations and targeted monitoring to surface partial failures. Define fallback behavior so users receive clear feedback when external services degrade.

3. Cross-Platform Regressions Between Web and Mobile Apps

The issue:
On a real estate buy/sell marketplace supporting both web and mobile apps, a backend change to listing filters worked correctly on the website but caused mobile users to see incomplete or incorrect results.

Why this happens:
Web and mobile clients often consume the same APIs differently and ship on different cadences. When small backend or contract changes unintentionally favor one platform, regressions appear that can only be found when workflows are validated end-to-end.

How to prevent:
Validate shared user journeys across web and mobile simultaneously. By establishing this process, to detect inconsistencies early.

4. Front-End Regressions After Updates or Refactoring 

The issue:
A video commerce platform refactored shared UI components to support a new product discovery experience. While the update improved performance in isolation, it introduced regressions in existing flows, product overlays failed to load correctly during live streams, and certain interactions became unresponsive.

Why this happens:
Front-end refactors frequently touch shared components, styles, or state management. Planned changes can have unintended downstream effects if not exercised across all critical flows.

How to prevent:
Expand regression coverage around shared components and high-traffic user flows, to catch issues introduced by refactors before they reached production.

5. Deep Linking Failures from Web to Mobile Apps 

The issue:
A crypto wallet allowed users to initiate actions from the web by scanning a QR code that deep-linked into the mobile app. After a routing update, some users were sent to the wrong screen, while others couldn’t open the app at all.

Why this happens:
Deep linking depends on tight coordination between web URLs, mobile routing, OS-level configuration, and app state. Small changes to routing logic or app initialization can break these flows.

How to prevent:
Testing for deep links across devices, OS versions, and app states including installed, not installed, logged in, and logged out. 

6. Race Conditions in High-Traffic Scenarios 

The issue:
A personal identity management app experienced data inconsistencies during periods of high usage. When users rapidly completed identity verification steps in parallel sessions, duplicate records were created, causing downstream verification failures and support escalations.

Why this happens:
Concurrency issues rarely surface in local or low-traffic testing environments. Without stress and concurrency testing, race conditions remain hidden until real-world usage exposes them.

How to prevent:
Simulate concurrent actions under load, to implement locking and idempotency safeguards and validate fixes at scale.

7. Data Sync Failures Under Poor Network Conditions 

The issue:
Users of a finance app could initiate transfers while offline or on unstable connections. Actions appeared successful in the UI but failed to sync once connectivity was restored.

Why this happens:
Offline and degraded network scenarios add complexity that’s often under-tested. Without clear retry and reconciliation logic, applications can present misleading success states.

How to prevent:
Introduce network throttling and offline test scenarios, to help teams implement clearer sync handling..

8. Cross-Channel Authentication Flow Failures 

The issue:
A personal identity management app relied on emailed magic links to simplify login and reduce password friction. While the flow worked reliably on the web, users opening the same link on mobile experienced inconsistent behavior. Some links opened a browser instead of the app, others landed users on a generic home screen without completing authentication, and in certain cases the link expired mid-flow. 

Why this happens:
Magic links sit at the intersection of email clients, browsers, mobile operating systems, deep-link routing, and app state. Small differences, such as whether the app is installed, whether the user is already authenticated, or how the OS hands off the link, can break the flow. 

How to prevent:
Validate magic link flows across web and mobile, covering multiple email clients, OS versions, and app states. Verify that authentication completes consistently regardless of where the link is opened, and add clear fallback behavior.

What these issues had in common

These issues don’t live on the happy path. They surface at the edges between systems, platforms, and real user behavior. Strong QA focuses there, where the stakes are highest.

By combining targeted automation, thoughtful scenario coverage and testing that happens end-to-end and cross-platform, teams can catch these bugs before customers ever see them.

Covering your bases

Most product teams don’t struggle because they ignore testing completely. But a variety of scenarios make getting to ideal test coverage challenging.

There are tests teams know they should be running, such as broad regression coverage or cross-platform validation, but don’t have the tooling or bandwidth to get them running. There are also tests teams understand are necessary but can’t fully define yet, because the right scenarios only emerge with scale and real usage. Then there are the blind spots: tests teams don’t realize they’re missing until a failure occurs in production. 

Tessana aims to help teams close all these gaps. Our AI-powered test plan generation quickly surfaces coverage gaps and risk areas across systems, platforms, and user behavior, while our experienced QA strategists apply human judgment to prioritize what matters most, refine scenarios, and adapt testing as your product evolves. The result is deeper coverage, fewer surprises, and confidence that your testing strategy reflects how your product is actually used.