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1. Practical insights from real-world experiences
Every team building a mobile app hits the same wall. Early on, they rely on manual testing—launch the app, click around on a few devices, confirm that new features work. And most of the time, it looks fine.
Then something breaks in production. Not everywhere—just on an older device, or after an OS update, or when the network drops mid-request. The team scrambles to reproduce it, unsure why the issue slipped through. That’s when the conversation shifts: we need automation.
They start with a small suite. It runs after every commit. It catches obvious regressions. It helps—until it doesn’t. Over time, the suite gets slower. More tests flake. CI pipelines get clogged. Engineers spend more time maintaining tests than writing features. And bugs still make it to production.
This isn’t a tooling problem. It’s a system design problem. Mobile testing breaks when teams treat it like a scaled-up version of web testing. It’s not. The assumptions behind fast, stable web pipelines don’t hold up on mobile. And unless you build your regression strategy around how mobile actually works—infrastructure, devices, architectures, failure modes—you’re going to repeat the same mistakes.
Effective mobile E2E testing means building automation that doesn’t just run tests faster—it should also reduce the maintenance burden, improve accuracy, and free your developers and QA engineers to focus on enhancing your app instead of endlessly fixing broken tests. Achieving this balance is the core challenge of mobile testing, and the teams that get it right consistently deliver a better user experience more quickly and cost-effectively.
This guide is your shortcut through that mess. It’s packed with practical insights from teams that have been there, rebuilt their test systems from the ground up, and figured out what actually works. No fluff. No hype. Just hard-won lessons on how to make mobile E2E testing scale—without dragging your team down.
2. Unique challenges of automated mobile app testing
Mobile app automated testing presents several challenges that aren’t present with browser-based applications:
Platform and device fragmentation
An app downloaded from the App Store or Play Store needs to work reliably on dozens or even hundreds of different combinations of device and OS versions. For example, iPhones with notches may have different screen layouts than other iPhones, or there may be video features that are only available on Pro models with high-end cameras.
App architectures
The different app architectures — native, webview, and cross-compiled apps — each require different testing infrastructure and have architecture-specific test cases. The way the apps are constructed also changes how new features are released to customers, which affects how and when developers can run E2E tests.
You may hear the term “hybrid apps” referring to both WebView-based and cross-compiled apps. Both architectures can be used on Android and iOS—hybrid code bases, get it?
Infrastructure scaling and testing speed
The size and speed of your test suite are limited by physical infrastructure. With mobile apps, it’s not just a matter of spinning up additional browser instances; mobile automation requires physical or emulated devices. These devices need to be provisioned with different set-up conditions (memory, screen size, network state, battery level, etc.), and tests are often slower due to hardware or OS constraints. Running large-scale parallel tests means managing device labs or using costly cloud device farms, which adds operational overhead.
Maintaining mobile tests
Mobile apps change UI more frequently, especially in consumer-facing apps that emphasize UX. Changes to layout, gestures, or navigation patterns can cause automated tests to break unless they’re written with flexibility in mind. A test written for a “swipe-to-dismiss” gesture might break with a design update that replaces the gesture with a button, requiring not just a test update but often refactoring across multiple scenarios.
Since mobile tests often rely on dynamic elements and timing, which makes them brittle, maintaining tests across two platforms (iOS and Android) also doubles the maintenance burden unless your framework and test strategy are carefully structured. Without a maintainable test architecture, teams spend more time fixing tests than building products.
3. Getting to know mobile app testing infrastructure
You can run automated E2E tests for mobile apps on one of three infrastructure options:
- Real devices, the kind you purchase from a store and occupy three dimensions in physical space.
- Emulated devices run on virtual machines, which replicate the device's processor, memory, and network conditions. While they’re not perfect replicas of real devices, they are very close. And the ease and cost of adding emulators often outweighs the imperfections.
- Simulated devices mimic the app’s operation on the host machine’s system specs, which tend to be more powerful than a real device and, as a result, may hide application performance issues
As we go down the list, we trade realism and fidelity to real-world conditions for simplicity and ease of use.
4. Building a mobile regression suite for continuous delivery
Speed matters—but not at the cost of reliability. In mobile app testing, teams often chase one at the expense of the other. Either they automate shallow tests to keep the pipeline moving, or they go deep, but slow everything down. Neither path scales.
Mobile releases can’t wait for manual QA cycles or engineers to decipher why another flaky test failed. But they also can’t rely on fast test suites that skip key flows or fail to represent real user behavior. The goal isn’t just test automation. It’s high-confidence shipping, where every release passes through meaningful, trustworthy checks that mirror what users actually do on their devices.
Building a regression suite that delivers that kind of confidence takes more than coverage. It takes the right goals, infrastructure, and tools to support fast iteration and reliable feedback. Your testing strategy has to reflect how mobile really works—across platforms, architectures, and environments—while staying flexible enough to evolve with the app. Otherwise, you’ll either burn out trying to maintain the suite or stop trusting it altogether.
The teams that get it right aren’t chasing stability—they’re designing for it. They’re not just writing tests—they’re building systems that earn trust at scale.
5. Let your goals guide your app testing strategy
Continuous delivery without continuous testing is a risky proposition, doubly so when it comes to mobile app testing, given user tolerance for bugs and quality issues. As you consider questions about test frameworks and device infrastructure, keep your testing goals top of mind. Any team focused on continuous delivery will need to key in on these four things:
Optimize for maximum total test coverage
When 80% of user workflows have an automated regression test that runs against every build, there’s a very slim chance of an escape, and teams can be confident that the next version of the app is ready to be released.
Choose a framework and development environment that simplifies test creation and refactoring, and aim for a turnaround time under a week.
→ Skip to: Selecting the right automation frameworks
Minimize QA cycle times
Almost by definition, testing means development stops. Whether that’s a single developer babysitting their PR or a team of developers waiting for feedback on their merged branch, the developer who’s testing is not working on new features.
QA cycles increase for two reasons:
- Limited parallelization extends the run of a test suite.
- Brittle, flaky tests require repeated runs and individual human review.
A robust testing infrastructure is necessary to handle the concurrency requirements of an extensive test suite, prevent false positives, and minimize investigation and maintenance time; in turn, providing developers with feedback faster and giving them more productive work time.
→ Skip to: Managing flakiness
Strive for realism
One of the biggest challenges with mobile app testing is that the apps behave differently on different devices and under different conditions. The apps themselves need to work with older, less powerful devices, which may be missing features that are present on newer devices.
While it’s technically possible to maintain a private inventory of devices, it’s also wildly impractical to maintain the sheer number of them required to run large test suites concurrently. This is where device farms and cloud-based emulation come in. Choose a solution that provides real and emulated devices and supports real-world test cases.
In particular, avoid iOS simulation, which can disguise performance issues because the “device” is getting the full processing power of a Mac computer.
→ Skip to: Evaluating device farms
6. Selecting the right automation frameworks
Most teams make framework selection harder than it needs to be. In reality, two questions determine 90% of your options:
- Is your app iOS-only, Android-only, or both?
- Is it fully native, hybrid, or web-based?
That’s it. Once you answer those, the decision tree narrows fast.
If you’re testing only Android, you use Espresso. If you’re testing only iOS, you use XCUITest. If you’re testing both—especially hybrid or cross-platform apps—you’re likely choosing Appium, or something built on top of it. There is some nuance there; you can read about those details here.
Appium has become the default not because it’s trendy, but because it’s the only cross-platform, open-source mobile automation framework with broad support, flexible language bindings, and enough real-world usage to be production-safe.
It’s not always the fastest or simplest option, but it shows up everywhere for a reason. It handles hybrid apps, WebViews, and system dialogs better. And in a world where most apps aren’t just one thing, that flexibility matters.
Are there other options? Yes, but they’re usually narrow by design: Maestro for simple flows, Detox for React Native, and integration_test for Flutter. If those tools match your app and team exactly, they can work well. But if you need to scale, extend, or unify mobile automation under one roof, you’ll probably end up with Appium anyway.
Pick the tool that fits your architecture and delivery model, not just what’s fastest to set up. That’s how you avoid costly rewrites when test coverage starts to matter.
7. Managing flakiness
Many teams think mobile test flakiness comes down to poor device clouds or weak automation frameworks. They’re partly right—inadequate infrastructure makes everything worse. But even with perfect infrastructure, mobile introduces instability factors that web testing teams rarely face.
Some of these issues can be prevented with the right environment design (emulator setup, test data seeding). But many others—like OS-level quirks, device fragmentation, or resource contention—can only be mitigated. The tables below break down the most common sources of flakiness in mobile E2E testing, explain why they happen, and highlight how serious teams design their tests and infrastructure to manage them.
8. Evaluating device farms
Not all device farms serve the same purpose. Some offer access to a wide range of devices—a solution geared toward manual testing, exploratory sessions, or plugging into your own automation if you’re prepared to handle the scale and upkeep. Others are purpose-built for automated testing at scale, with control over provisioning, execution, debugging, and maintenance.
It’s a critical distinction. Many teams over-index on device diversity—chasing coverage across screen sizes and OS versions—without realizing they’re trading away depth. You can have 300 device models in your grid and still miss core user journeys if you can’t test things like Apple ID logins, system alerts, or OS-level integrations. Those flows require deep control over the device, not just shared access. And without that control, key parts of the app go untested or get covered manually.
Before picking a device farm, ask what kind of testing you’re trying to scale. If you choose based on access alone, you’ll end up with impressive surface area and shallow coverage, and you’ll be rebuilding your test infrastructure the moment real automation becomes a priority.
The table below lists the five types of device farm services serious teams should evaluate.
If you pick a farm based only on device access, you’ll need to rebuild your testing infrastructure before you know it. Here’s some no-nonsense criteria for evaluating device farms, tailored for teams that care about scalable, reliable, and deep mobile test coverage—not just meeting minimum requirements.
- Depth of device control: Can you run tests that interact with system alerts, biometric prompts, or OS-level flows (like Apple ID)? Shared access isn’t enough. If the farm doesn’t give you deep control, you’re leaving critical flows untested or stuck doing them manually.
- Test execution architecture: Is the farm purpose-built for automated testing? Look for isolated environments, stable provisioning, and test reliability under load. A farm that prioritizes manual access won’t cut it once you’re scaling test volume or chasing flake.
- Debugging and observability: What happens when a test fails? Can you get full logs, video replays, screenshots, and device-level telemetry without jumping through hoops? If debugging is painful, test maintenance will stall fast.
- Provisioning speed and reliability: How long does it take to spin up a device? Can you run tests in parallel, or are you waiting in a queue? Bottlenecks here kill velocity and block integration into CI/CD.
- Scalability of automation: Does the platform handle large test suites, run them in parallel, and integrate cleanly into your CI? Or does it slow down as test volume grows? Manual-first platforms often hit a wall here.
- Maintenance and flake handling: Who owns test stability? Does the platform help detect flake, retry intelligently, or self-heal DOM changes? Or is your team on the hook for every brittle test? Most farms leave this to you.
- Access and diversity of devices: Yes, this matters—but it’s not the top priority. You want enough variety to match your user base, but not at the cost of test depth, speed, or reliability.
- Support and services: Do they offer real support when you hit an edge case? Some platforms are DIY to the core. Others offer real partnerships, helping you build and maintain tests, not just rent devices.
- Total cost of ownership: Not just licensing fees—factor in time spent writing, maintaining, and debugging tests. A cheaper farm might cost more in engineering hours.
If you’re pursuing continuous delivery on mobile, you need a pipeline designed for mobile from the ground up. That means more than parallelization and test selection—it means intentional design around devices, observability, and failure isolation. You’re not just building for speed. You’re building for trust.
Design for mobile reality
Mobile testing isn’t just a scaled-up version of web testing. The infrastructure is different. The execution environments are harder to control. The points of failure are deeper in the stack. The consequences of a miss are higher because mobile releases are slower to patch and harder to roll back.
If you want to ship mobile updates confidently, you need testing systems that are stable under load, clear when failures occur, and fast enough to keep up with development. That means building for test coverage, not just device access. It means running tests in isolated, repeatable environments with observability built in. It means parallelism that respects real constraints—device type, OS version, system state—not just a pool of runners.
When the test system reflects the complexity of the app surface, the result is faster feedback, broader coverage, and fewer surprises during release. That’s not something you get by default. It’s something you get by design.
