Introduction

Digital native companies ship software at a high pace. Frequent updates are expected, not exceptional.

This speed changes the cost of failure. A crash, a slow screen, or a broken permission flow pushes users to leave the app and share negative feedback. Most users do not wait for fixes.

The paradox is simple. The faster teams ship, the faster quality issues turn into user and business loss.

For this reason, quality assurance cannot sit at the end of development. It must shape how products are designed and built from the start.

In this blog post, we look at common quality mistakes digital-native teams make and how to avoid them.

Common QA issues caused by gaps in software testing

Testing only ideal user journeys

Many teams test for perfect conditions with strong networks, full device battery, correct user inputs, and uninterrupted sessions dominating test coverage.

This does not match real usage. Users switch apps mid-flow, receive calls, lose connectivity, and operate devices in power-saving modes. When these conditions are ignored, failures appear only after release.

How to avoid this

Start by validating core user journeys under normal conditions so basic functionality is proven first. Once these flows are stable, extend the same journeys to include disruption instead of creating separate edge-case tests.

Run those flows while changing networks mid-session, interrupting the app, lowering battery levels, or triggering OS restrictions such as power-saving mode. Observe whether the journey resumes, fails gracefully, or breaks completely.

Applying the same testing approach to mobile and web

Some teams reuse web testing strategies for mobile products with minimal change.

This causes gaps. Web testing focuses on browser behavior and layout consistency. Mobile products depend on operating system behavior, device hardware, permissions, sensors, and storage limits. These factors directly affect user flows.

How to avoid this

User journeys should be validated in the environments where they actually run. Web flows need coverage across real browsers and devices to catch rendering, interaction, and client-side performance issues. Mobile flows need validation on real devices to observe how OS behavior, permissions, background behavior,and hardware constraints affect the journey.

Using unrealistic test data

Unrealistic test data represents only a narrow slice of product behaviour. It misses common states such as partially completed flows, failed actions, changed settings, and data created from earlier use.

Because these states are missing, test results give a false sense of confidence. Features appear stable, risky changes move forward, and release decisions are made on incomplete coverage.

These gaps create direct business risk. Teams approve releases based on misleading stability signals, which increases user drop-offs, support effort, and release delays, impacting revenue and engineering efforts.

How to avoid this

This issue can be addressed by generating test data that mirrors production conditions. Validation should include partial configurations, outdated preferences, and edge-case histories. Data generation must be repeatable so that failures can be consistently reproduced and resolved.

Measuring performance the wrong way

Performance is often evaluated using backend metrics such as request processing time, error rates, and infrastructure resource usage.

Users judge performance through their interactions with the application. A short freeze while scrolling or a delayed tap response feels broken, even if backend systems are healthy.

How to avoid this

Improving performance assessment starts with tracking KPIs across real devices, regions, and network conditions, because user experience changes with each of these variables. To capture those changes, teams run complete user flows in these environments rather than inspecting isolated measurements. As those flows execute, they observe how interactions respond when location, network behavior, and system load shift. This progression makes it possible to trace delays back to their source, whether the interface, the network path, or the server systems..

Treating accessibility as a late check

Accessibility is often addressed late in the release cycle, mainly to meet compliance requirements. When treated this way, it becomes a checkbox activity instead of a core quality concern, which excludes users and adds avoidable risk.

Accessibility expectations vary by region and are commonly based on WCAG standards, along with regulations such as the ADA in the US and the European Accessibility Act. Handling accessibility at the end increases the risk of non-compliance, legal exposure, and release delays.

When these are not followed, organisations face legal complaints, penalties, mandatory remediation, and public scrutiny. Fixes are often forced under tight timelines, which increases cost and delays releases.

How to avoid this

Addressing this requires observing performance at the user interaction level on real devices. Teams need to measure responsiveness, visual stability, and behavior during screen transitions while a real user flow is executed. Session-level insights collected during real device testing, including UI behavior alongside network and system data, help teams understand how performance issues surface from the user’s perspective rather than relying only on backend indicators.

Making quality sustainable

When requirements and designs are reviewed only for functional correctness, real usage patterns, device constraints, and environmental conditions are overlooked before development starts.

Test suites expand, maintenance effort grows, and teams spend more time repairing tests than validating product behavior. At the same time, relying solely on internal environments limits visibility into issues that appear only under real user conditions..

Example

A team defines a new onboarding flow based only on functional steps and validates it in an internal test environment. After release, users on certain devices and networks face delays and broken transitions that were not considered earlier. Reviewing the onboarding flow with real usage scenarios before development and validating builds on real devices helps surface these issues before release.

Conclusion

For digital native teams, quality is not a final step. It directly protects user trust, brand credibility, and revenue.

Speed alone does not create an advantage. Teams succeed when they account for how small failures scale at high velocity.

If more time is spent fixing test scripts than fixing real product issues, the problem is not speed. It is how quality is handled.

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FAQs

Q1. Why do digital native products fail even with frequent testing‍

Ans: Because testing often covers only ideal conditions. Real users face unstable networks, interruptions, low battery states, and legacy data. When these conditions are missing from test coverage, failures surface only after release.

Q2. Is UI automation enough for digital native quality‍

Ans: No. Heavy UI automation increases maintenance effort and slows teams down. Most validation should happen at the unit and API levels. UI automation should be limited to stable, business-critical flows.

Q3. Why does performance feel slow even when backend metrics look fine‍

Ans: Users experience performance through interaction, not server uptime. Short UI freezes, delayed taps, or layout shifts feel broken even if backend systems are healthy. Measuring interaction timing and visual stability exposes these issues.