Mobile application testing on the devices, networks and store rules that decide your ratings
Mobile apps live in a hostile environment: thousands of device models, calls and notifications interrupting, networks that come and go, and store reviewers who reject crashes. We test on real devices and automate what is stable.
Overview
What it is. Mobile application testing verifies iOS and Android apps for functionality, usability, compatibility, performance and stability, store readiness and security basics, on real devices and across operating system versions.
Who it is for. Product teams shipping iOS and Android apps, retailers and service companies with consumer apps, and teams adding payments, subscriptions or accounts to their apps.
Why it matters
The problem it addresses. A mobile app that works on the team's own phones may crash on a popular mid-range model, lose state when the user switches apps or fail when a network drops. Store thresholds turn those faults into lost visibility.
When it matters most. Before store submission, before every major release, when a new OS version ships, and when crash reports cluster on specific devices.
What happens without it. Crashes and stalls produce poor reviews and reduced store visibility, rejected submissions delay launches, and payment or account faults cost revenue directly.
What we test
Functional and usability testing on iOS and Android
Device, OS version, screen size and form-factor matrix, including rotation and foldables
Install, upgrade and uninstall; permissions and privacy prompts
Interruptions
calls, notifications, background and foreground, low battery
Network behaviour
offline, slow and switching connections, and deep links
In-app purchases, subscriptions and restore flows
Performance and stability
start-up time, memory, crash and ANR behaviour
Store readiness checks against platform review guidelines
Automation of stable flows with Appium
How UpDoer approaches Mobile Application Testing
Understand
Requirements, tickets, code diffs and product context.
We gather your analytics, store listing, crash reports and release plan, and ask where reviews or tickets show pain.
Risk Mapping
Where a failure would cost the most.
Flows and devices are ranked by revenue, usage and crash exposure, and platform review rules relevant to your app are noted.
Test Strategy
Coverage chosen, test cases drafted.
We define the device matrix, what runs automated, what stays manual, and the interruption and network conditions to cover.
Execute
Manual and automated testing runs.
Automated flows run on the matrix; testers work through exploratory sessions, interruptions and purchase flows on real devices.
AI-Assisted Quality Engineering
Failures classified, release risk scored.
AI clusters crashes by device and OS, drafts scenarios from stories, repairs trivial locator breaks and compares screens across devices.
Human Verification
A QA engineer checks every flagged result.
A QA engineer judges touch, gesture and interruption behaviour on real devices, and confirms which defects are reproducible and how severe.
Report & Improve
The verified quality signal, then better coverage.
Reports give results by device tier and flow, store-readiness notes and untested devices, with risk stated.
Mobile testing: real devices, real interruptions
01
Where it breaks
Crash on a popular modelState lost on app switchStore rejects the build02
Test workflow
PhoneDevicesTestResult03
AI assists
Cluster crashes by deviceCompare screensRepair trivial locators04
Humans decide
Handle the real deviceJudge store-review risk05
Release signal
Results by device tierUntested devices listedRelease call by a person
Illustrative workflow. Not a client result or a measured outcome.
Mobile coverage matrix
A dash means no AI step is described for that area.
| Area | What we validate | AI assistance | Human validation |
|---|---|---|---|
| Functionality | Core flows on iOS and Android | Drafts cases; repairs locators | Confirms behaviour and rates defects |
| Devices and form factors | Models, OS versions, screen sizes, rotation, folding | Groups failures by device | Confirms on real hardware |
| Interruptions and state | Calls, notifications, backgrounding, low battery | Suggests interruption scenarios | Tests in realistic sequences |
| Network | Offline, slow, switching networks, deep links | Clusters network-related failures | Judges acceptable behaviour |
| Purchases | Purchase, subscription, restore, refund | — | Judges customer impact |
| Stability and performance | Crash, ANR, start-up time, memory | Summarises session metrics | Interprets against store thresholds |
Area: Functionality
- WHAT WE VALIDATE
- Core flows on iOS and Android
- AI ASSISTANCE
- Drafts cases; repairs locators
- HUMAN VALIDATION
- Confirms behaviour and rates defects
Area: Devices and form factors
- WHAT WE VALIDATE
- Models, OS versions, screen sizes, rotation, folding
- AI ASSISTANCE
- Groups failures by device
- HUMAN VALIDATION
- Confirms on real hardware
Area: Interruptions and state
- WHAT WE VALIDATE
- Calls, notifications, backgrounding, low battery
- AI ASSISTANCE
- Suggests interruption scenarios
- HUMAN VALIDATION
- Tests in realistic sequences
Area: Network
- WHAT WE VALIDATE
- Offline, slow, switching networks, deep links
- AI ASSISTANCE
- Clusters network-related failures
- HUMAN VALIDATION
- Judges acceptable behaviour
Area: Purchases
- WHAT WE VALIDATE
- Purchase, subscription, restore, refund
- AI ASSISTANCE
- —
- HUMAN VALIDATION
- Judges customer impact
Area: Stability and performance
- WHAT WE VALIDATE
- Crash, ANR, start-up time, memory
- AI ASSISTANCE
- Summarises session metrics
- HUMAN VALIDATION
- Interprets against store thresholds
Where AI helps
- Clustering crash reports and error logs by device model and OS version.
- Drafting test scenarios from user stories and release notes for review.
- Proposing locator repairs for stable automated flows after interface changes, reviewed by a person.
- Summarising results by device tier for a release decision.
How we use AI: AI can accelerate test design, analysis and repetitive QA work, but important outputs remain subject to human review and validation. It informs the release decision; it does not make it.
Where humans matter
- Handling the device: gesture feel, scrolling, haptics, camera, sensors and thermal behaviour.
- Testing interruptions the way users meet them: a call during checkout, a lost connection mid-payment.
- Judging store-review risk and whether a defect is likely to cause rejection.
- Deciding which devices belong in the matrix and accepting the risk of the rest.
- Assessing whether an experience feels reliable and trustworthy.
Tools we build with
- Automation
- SeleniumPlaywrightAppiumKatalonTestComplete
- API & Performance
- PostmanSoapUISwaggerApache JMeter
- CI/CD & Reporting
- JenkinsTeamCityAllureMaven
Frequently asked questions
Do you test on real devices?
Yes, real devices are central to mobile testing. Our published work refers to a device roster; the exact make and model coverage is confirmed in scoping against your analytics.
Which tools do you use for automation?
Appium is part of our published tool set, run from CI with Allure reporting. We discuss alternatives if your team already uses another framework.
Can you help us avoid store rejection?
We check your app against the relevant review guidelines and report likely causes of rejection. The final decision always rests with the store.