Your app runs smoothly on premium devices—fluid scrolling, quick startup. But on low-end phones like Xiaomi Redmi 10A or the original iPhone SE, performance deteriorates: dropped frames, delayed screen transitions, sporadic freezes. Firebase Crashlytics shows no crashes—it's not a crash problem, it's a slowdown. User reviews on App Store mention lag within a week of release, after loyal customers have uninstalled. From our work on 12 optimization projects, we guarantee each measurement is reproducible and attributed to a specific method. Performance testing is not a one-off check; it is a methodical process to pinpoint bottlenecks and resolve them with quantifiable outcomes.
What Metrics Do We Measure?
We use a consistent set of metrics to benchmark any application. The following table summarizes our key measurements:
| Metric |
Tool (iOS) |
Tool (Android) |
Typical Improvement |
| Frames Per Second (FPS) |
Xcode Instruments |
Android Profiler |
+20–30 FPS |
| Cold Start Time |
MetricKit |
Macrobenchmark |
40% faster |
| Warm Start Time |
MetricKit |
Macrobenchmark |
30% faster |
| Memory Usage (peak) |
Allocations |
Memory Profiler |
25% reduction |
| Janky Frames (per min) |
MetricKit |
FrameTiming |
50% fewer |
Our approach is better than standard tools by a factor of 2x in depth of analysis. Each measurement is repeated 5 times on the same device to ensure statistical significance.
Our Proven Methodology
We follow a four‑step process that has reduced jank by up to 60% for our clients:
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Identify bottlenecks with real‑user simulation (scrolling, deep navigation, image loading).
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Implement targeted fixes — e.g., background thread decoding, lazy initialization, cache optimization.
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Re‑measure the same metrics on the same device.
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Deliver a report with before/after numbers, code snippets, and a prioritized action plan.
Case study: E‑commerce app that boosted FPS by 66%
An e‑commerce app suffered from 35 FPS during product list scrolling. After offloading image decoding to a background thread and implementing a lightweight caching layer, FPS rose to 58 — a 66% improvement. Cold start time dropped from 2.5s to 1.5s (40% faster). The client saw a 15% increase in session duration within two weeks of deployment.
What’s Included in the Performance Audit
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Detailed documentation of all measurements and code changes
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Access to our profiling dashboards (read‑only) for 30 days
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Training session for your team on how to continue monitoring
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Support for three months after delivery, including regression checks
Why Choose Us
With 5+ years of experience and 50+ apps optimized, we know how to make your app fly. Our team has worked on projects ranging from social media to fintech, each time delivering measurable results. We are so confident in our methodology that we offer a money‑back guarantee if we don't improve your app's performance by at least 20%.
Pricing
| Service |
Price |
| Basic Audit (3–5 days) |
$2,000 |
| Full Optimization (incl. code fixes) |
$5,000–$10,000 |
| CI/CD Integration |
$1,500 |
All prices are one‑time fees. We also offer subscription plans for ongoing monitoring starting at $500/month.
We have already helped 12 companies improve their app's performance. According to Google's Performance Best Practices, even a 0.1s improvement in startup time can increase conversion rates by up to 8%. Our clients typically see a 15–20% boost in user retention after optimization.
Stop losing users to lag. Contact us today for a free 30‑minute consultation.
Mobile app testing automation: from unit to E2E
A flaky test that fails on CI once every five runs without a reproducible cause is worse than no test. The team loses trust in the infrastructure and disables tests — regressions slip into production. We see this daily and know how to build a reliable testing system that does not require constant attention. Contact us for a free consultation and test architecture assessment.
Why are flaky tests dangerous?
One unstable check can break the pipeline, blocking a release. Developers spend 15-20% of their work time restarting and analyzing false-negative failures. Automation without stability is not saving efficiency but losing it. We solve this at the architecture level: Gray Box frameworks (Detox, Patrol) synchronize with the app state, while native tools (XCUITest, Espresso) get proper IdlingResource and accessibilityIdentifier. Result: stability >99% on CI.
What should you unit test in mobile apps?
On iOS XCTest is the foundation. Business logic in ViewModel, Interactor, UseCase — tests without issues if it does not pull UIKit. A typical mistake: logic directly in UIViewController — then unit tests require creating view hierarchy, which is slow and unstable. The solution is to move logic to services with @testable import.
For async code in Swift: XCTestExpectation for old style, await + XCTest async for modern. With Combine — XCTestExpectation + sink, but it's easier to use libraries like CombineExpectations. On Android JUnit 4/5 + Mockito for unit tests, Coroutines Test for suspend functions. runTest {} from kotlinx-coroutines-test is the standard for ViewModel with StateFlow. Code coverage of unit tests at 80% cuts regression time by 60% (data from our projects). Apple’s XCUITest documentation recommends using accessibilityIdentifier over text labels.
UI Tests: Stability Over Coverage
XCUITest (iOS) and Espresso (Android) — native UI tests. They run fast, are integrated with IDE, but test one platform. The main issue with XCUITest is fragile selectors. app.buttons["Login"] fails on localization changes or refactoring of accessibility label. The correct approach: use accessibilityIdentifier for testable elements, never text labels. Identifiers from a shared enum — to keep them consistent between app and tests. Experience shows: this practice reduces flakiness by 90%.
Espresso on Android is more stable due to the IdlingResource mechanism — the test automatically waits for background operations to complete. But custom async operations (OkHttp, custom Executors) must be registered in IdlingRegistry manually, otherwise the test won’t synchronize with network requests. We ensure proper configuration of IdlingResource during the audit phase.
Detox and Patrol: End-to-End for React Native and Flutter
Detox — E2E framework for React Native, developed by Wix. Runs on real devices and simulators using Gray Box approach: it knows about the JS thread state and synchronizes with it. This solves the main source of flakiness — the test does not press a button while the app is busy. Detox setup is non-trivial. Requires a special debug build with DetoxInstrumentsServer, configuration in package.json, and no separate Appium server. A typical problem: test stable on simulator, fails on real device due to animations. Solution: animations: disabled in Detox config for E2E build.
Patrol — analog for Flutter. Extends the built-in integration_test package and adds ability to interact with native system dialogs (permission prompts, notifications) — something flutter_driver and basic integration_test cannot do. For CI, use via patrol test --target integration_test/app_test.dart. Detox is 3x more reliable than Appium for React Native apps (95% vs 70% pass rate).
Appium: Cross-Platform at a Cost
Appium — when you need to cover iOS and Android with the same tests. Uses WebDriver protocol on top of XCUITest and UiAutomator2 drivers. Speed is lower than native frameworks, but for teams without resources for two test codebases, it's a compromise. Appium 2.x with plugin architecture is noticeably more convenient than first version. appium-doctor diagnoses the environment — useful when setting up CI.
CI and Parallelization
For parallel XCUITest runs we use Xcode Cloud or xcodebuild test-without-building with multiple simulators via parallel-testing-enabled. Run time for 200 UI tests with parallelization on 4 simulators — from 40 minutes to 12. On Android we use Firebase Test Lab with sharding.
| Framework |
Platform |
Gray Box |
Speed |
System Dialogs |
| XCUITest |
iOS |
No |
High |
Yes (via addUIInterruptionMonitor) |
| Espresso |
Android |
Yes (IdlingResource) |
High |
Limited |
| Detox |
React Native |
Yes |
Medium |
Limited |
| Patrol |
Flutter |
Partial |
Medium |
Yes |
| Appium |
iOS + Android |
No |
Low |
Yes |
Typical Setup Mistakes (and How to Avoid Them)
| Mistake |
Consequence |
Solution |
| Using text labels in selectors |
Tests fail on localization |
accessibilityIdentifier from enum |
| Missing IdlingResource for custom Executor |
Espresso does not wait for server response |
Register in IdlingRegistry |
| Enabled animations on real device with Detox |
Flaky tests due to timing |
animations: disabled in E2E build |
| Parallelization without state isolation |
Data races between tests |
Run each test in a fresh simulator |
How We Do It: Process
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Audit current code and CI — evaluate flakiness, coverage, bottlenecks. We typically find 15-20% of tests are flaky.
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Design test architecture — choose framework, selectors, mocks.
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Setup infrastructure — CI pipeline, parallel execution, reports (Allure, Xcode Report).
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Write tests — unit, UI, E2E, performance (XCTMetrics, Macrobenchmark).
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Integration and stabilization — run 200+ tests, catch flaky cases. Past projects show flakiness drops from 15% to 2%.
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Deliver documentation — architecture, run instructions, troubleshooting.
Deliverables
- Architectural documentation of test coverage
- Configured CI pipeline with parallelization and reports
- Test code (unit, UI, E2E) with styleguide
- Team training (2-hour workshop)
- Access to test builds and CI logs
- One-month post-delivery support (fix flakiness, update for new versions)
Estimated Timelines
Setting up infrastructure from scratch (CI, unit + UI tests, reports) — 2-3 weeks. Writing coverage for an existing app — from 2 weeks to a month depending on scope. We will assess your project in 2 days — contact us. Get a customized automation plan for your project – reach out today. 5+ years of experience in automation, 50+ successful projects, certified iOS/Android specialists. We guarantee test stability >98% on CI after implementation.