The world of mobile apps is rapidly evolving, but ensuring their reliability and performance remains a significant challenge. With 77% of users abandoning an app within three days if it fails to engage them, QA has become crucial for user trust and business success. In this article, we'll explore the limitations of traditional QA approaches and how AI-powered testing can revolutionize the way we test mobile apps.
Traditional Mobile QA is Broken
Despite decades of tooling, mobile QA remains plagued by fundamental problems. Manual testing – having humans tap through scenarios on devices – is familiar but slow. It scales poorly as apps grow complex: testers can only execute so many cases in limited device labs or emulators. "Manual testing is slow, time-consuming, error-prone" and inevitably leaves coverage gaps that delay releases. On the other hand, scripted automation was supposed to solve this. Teams write UI tests with frameworks like Selenium, Appium, Robot Framework, or Espresso to click buttons and verify outcomes.
However, these scripts are brittle. Even minor UI changes break them, causing maintenance nightmares. Common mobile automation tools (Appium, XCUI, Espresso, etc.) are "cumbersome to implement, time-consuming to manage, and brittle". Every OS update, screen redesign, or dependency change can require laborious test rewrites. In large apps, QA often ends up hiring expensive SDETs or repurposing developers just to keep the tests running.
The Reality of Traditional QA
Device and OS fragmentation make coverage effectively impossible. There are over 24,000 Android models and dozens of iOS versions in the wild. Ensuring every combination works flawlessly would take years of testing. Even a pool of 20-50 devices covers only a fraction of users. As one QA expert puts it, testing mobile apps is like designing one shirt to fit "every single person on the planet" – an almost impossible task.
The result is that teams must guess which devices and flows matter most. Siloed tooling compounds the problem: many organizations have separate test suites for mobile vs. web, different teams for iOS vs. Android, and disparate frameworks. The end result is inconsistent coverage, tech sprawl, and defects that slip into production.
AI-Powered QA Testing
AI-powered QA testing has shown how to accelerate development: by running nightly tests and continuous integrations, teams can release every sprint without sacrificing quality. Indeed, TestDevLab notes that adding AI to automation can supercharge QA efficiency even further – reducing manual effort, improving test coverage, and speeding up release cycles.
But at its core, confidence comes from covering all meaningful user flows – not just scripted checkboxes. That includes negative tests, edge cases, network failures, performance bottlenecks, and security scenarios. For example, a change to a login screen shouldn't crash the app for 5% of users on older devices, and QA should catch that. Unfortunately, traditional approaches rarely do. They often miss scenarios (e.g., "What if the user's session expires mid-flow?") or assume too much (only happy paths). When these gaps manifest in the wild, they erode user trust and require costly hotfixes.
Revolutionizing Mobile QA with AI
There are multiple approaches organizations are taking to solve this problem. Most of them focus on automating the scripting process. The common method is to use AI, specifically large language models (LLMs), to automatically generate manual test scripts. This creates a no-code/low-code platform for QA, where testers can define scenarios in natural language, and the system converts them into executable scripts.
These platforms usually build on existing frameworks like Appium, Selenium, and Playwright to extend their capabilities. This approach removes some of the grunt work and allows testers to focus on what really matters – verifying expected behavior and understanding real user behavior.