Why Device Farms are a Must-Have for Software Teams: A VeteranTester‘s Perspective
Have you ever launched a mobile app only to later discover crashes or crippling data usage issues plaguing users on certain Samsung devices? Or found a phenomenal web application fail to render properly on last-generation iPads still common in enterprises?
I have seen these outcomes far too often over my 14 years of experience testing software across thousands of mobile, tablet and desktop devices and browsers.
The underlying issue? The inability to test software comprehensively on real user devices before releasing them in the wild.
Enter device farms – on-demand cloud-based access to thousands of real mobile, tablet and desktop devices to test your latest app or website on.
As an industry-leading software testing expert relied upon by global majors to formulate robust device testing strategies, I cannot emphasize enough the outright necessity of device farms for tech-driven organizations today.
In this comprehensive guide, let‘s uncover what exactly device farms bring to the table, their tangible benefits, what to test on device farms, who can benefit from them, and real-world deployment tips that generate maximum ROI.
What is a Device Farm?
In simple terms, a device farm (aka real device cloud) provides instant on-demand access to thousands of popular real mobile devices – phones, tablets, desktops etc. – hosted remotely in data centers rather than needing to be procured and managed in-house.
These continually updated device farms available via cloud platforms like BrowserStack and others enable software teams to:
✅ Test mobile and web apps on real devices instead of simulators
✅ Cover 100s of models, OS versions, networks and locations
✅ Catch compatibility issues early before app release
✅ Benchmark performance – CPU, battery, memory etc.
✅ Debug via developer tools in real-time
✅ Scale test execution capacity on demand
Clearly, access to real devices hosted remotely in the cloud drives both breadth and depth of testing – spanning an ever-expanding spectrum of mobile, tablet and desktop devices and browsers in a highly flexible and cost-efficient manner compared to traditional device labs.
But what exactly is the nature and scale of this device fragmentation problem that necessitates leveraging device farms? Let‘s analyze.
Staggering Device Fragmentation Requires Expansive Real Device Coverage
The hardware and software environment in which digital experiences need to deliver business value today is extraordinarily fragmented:
- 9,000+ Android device models in circulation as of 2022 according to DeviceAtlas data
- 22,000+ iOS device models since 2007 per AppBrain Statista
- 8,000+ Kindle Fire tablet variants over time as per Amazon
- 5,500+ device models launched just in 2022 till June according to GSM Arena
- 100+ Chromebook devices from 20+ OEMS like Acer, HP, ASUS etc.
Combine this hardware diversity with the variance across operating systems, browsers and screen sizes, and you begin to fathom the vast matrix of device-software combinations (50,000+ browser-OS-device tuples) users access digital content on even as we speak.
This staggering fragmentation demands testing software on the maximum possible real devices, browsers and OSs to deliver seamless functioning. How much coverage is enough? Industry benchmarks suggest:
| Device Type | Minimum Test Coverage Target |
|---|---|
| Android Phones and Tablets | 1000+ device models |
| iOS Phones and Tablets | 500+ device models |
| Desktop Browsers | All popular browsers on Windows and Mac |
A cloud-based device farm enables attaining precisely such expansive test coverage through on-demand access to thousands of latest real mobile, tablet and desktop devices as per need.
But why exactly are real devices so crucial vs. simulators? Let‘s analyze next.
Real Devices vs Simulators: Why the Difference Matters
While Android Studio emulators, Xcode simulators and tools like BrowserStack Responsive offer local emulated access, those do not replicate real-world device behavior:
✖ Hardware constraints – limited memory, camera, GPS etc.
✖ Real-world performance – laggy UI, excessive battery or data usage
✖ Native app environment including push notifications, interrupted usage etc.
✖ Location and network-specific issues – low bandwidth, custom fonts etc.
This makes testing on authentic real-world devices via device farms absolutely critical for both web and native mobile apps.
Let‘s see some real examples of device-specific issues detected only on real mobile devices:
| App / Device | Issue |
|---|---|
| Landing page on Samsung Galaxy S21 | Button overflow due to excess padding |
| Travel app on iPhone 7 | Crash due to memory constraints |
| Chat app on OnePlus 6T | Laggy scrolling due to repaints |
| Hotel booking app on Pixel 5A | Japanese fonts rendering issue |
These nuances would likely have evaded detection on simulators alone. Device farms bridge precisely this gap by making every popular real device available on-demand for testing locally or remotely.
So while emulators have some usefulness for preliminary sanity testing during initial development, serious pre-production testing necessitates access to real user devices – exactly what cloud device farms deliver.
Having discussed what device farms enable, why real devices matter over emulators, and the staggering device fragmentation landscape necessitating expansive test coverage, let us now analyze 8 tangible benefits device farms unlock for software teams.
8 Benefits of Using Device Farms
| Benefits | Details |
|---|---|
| Find Real-World Issues Early | Test on real devices surfaces mobile-specific issues undetectable on simulators e.g. compatibility failures. Device farms enable access to 100s of device models right from development stage allowing early detection vs. waiting for field failures. |
| Cover More Test Devices | A cloud device farm catalog spans thousands of unique device models – far exceeding what most organizations can host internally in device labs. This allows significantly expanding test coverage at lower TCO. |
| Benchmark Real Performance | Tools like Android profiler provide device performance data – memory, CPU etc. – under simulated conditions. Device farms allow measuring real device vitals accurately mimicking actual usage contexts. |
| Faster Debugging | Remotely accessing real devices in the cloud accelerates reproducing issues, running diagnostics, and debugging – unlike offline internal device labs. |
| No Hassles of Device Management | Maintaining real device labs is extremely challenging – procurement, setup, maintenance and constant upgrades as new devices launch. Device farms entirely spare you that overhead by providing devices-as-a-service. |
| Cost and Time Savings | Accessing thousands of real devices hosted remotely eliminates capital costs of device procurement and IT overhead of managing device labs in-house – translating to 60%+ cost savings. |
| Flexible Usage | Device hours can scale up and down dynamically allowing usage and billing as per actual testing needs across the dev cycle. No need for fixed capacity overprovisioning. |
| Seamless Test Automation | Device farms integrate natively with test automation frameworks like Selenium and Appium allowing automated testing at scale across thousands of real device, browser and location combinations. |
As the data shows, utilizing device farms for software testing generates manifold benefits spanning product quality, cost efficiency and accelerated release velocity.
8 Vital Test Types to Execute on Device Farms
Having discussed what device farms enable for software teams, let us now cover the optimal way to leverage them through a structured testing methodology across 8 key dimensions from functionality to Performance.
| Test Area | Details |
|---|---|
| Functional Correctness Testing | Validate full range of core user journeys – signup, login, search, payments etc. on real devices via both manual testing and automation. |
| UI/UX Testing | Interactively test visual appeal, ease-of-use, visibility of touch targets etc. on real devices manually. Automate mistap analysis for buttons, links, touch areas. |
| Interrupt Testing | Mimic real-world interruptions via calls, notifications, device rotation etc. for web and native mobile apps to detect side-effects. |
| Performance Testing | Profile memory usage, battery drain, app crashes (ANRs), network traffic, laggy UI etc accurately on physical devices reflecting real-world constraints. |
| Localization Testing | Verify language support, custom translations, locale-specific assets etc. work correctly across geos using real overseas device farms covering key target markets. |
| Security Testing | Audit mobile apps for vulnerabilities like insecure data storage, transmission etc. and enforce security standards validation before go-live on app stores. |
| Accessibility Testing | Validate web/mobile app compliance with accessibility standards and usability for hearing, visually, motor impaired users via assistive technologies on real devices. |
| Cross-browser Testing | Ensure web UI, layouts, asset loading etc. works correctly across desktop and mobile browsers, versions and window sizes using real browsers accessed via device cloud. |
This blueprint encompasses functional, non-functional, UI/UX and device/user environment focused test areas for a structured validation approach – best leveraged by accessing real devices using cloud-based device farms.
Integrations for Optimal Leverage
To optimize productivity, device farm platforms offer deep integrations with test automation solutions:
Mobile App and API Testing
- Appium for native, web and hybrid mobile app test automation
- Espresso and XCUITest for Android and iOS test automation
- REST API frameworks like Postman, Cucumber etc.
Web Testing
- Selenium and Selenium Grid for cross-browser test automation
- Cypress for test automation
These cloud device farm integrations allow running automated test suites across thousands of real devices, mobile OS versions and browsers in parallel.
Additional integrations further augment efficiency:
CI/CD Pipelines
Embed device farm testing into DevOps deployment pipelines by hooking into Tekton, Jenkins, CircleCI etc. This shifts test automation left to the earliest stages of agile development.
Defect Tracking
Tight coupling with tracking systems like JIRA, Trello etc. helps auto-log device farm test execution status directly against requirements and user stories for traceability.
Clearly, complementing exploratory manual testing with test automation and ALM integration unlocks maximum efficiency and productivity from device farm adoption.
Hybrid App Testing Scenarios
The growing trend towards hybrid mobile apps using technologies like Ionic, Flutter, React Native etc. merits some explicit discussion around device farm leverage.
Common testing objectives include:
🔹 Validating functionality across all popular mobile OS platforms – iOS and Android priority
🔹 Testing integration with native device capabilities – camera, geofencing etc.
🔹 Ensuring performance does not lag on older OS versions with resource constraints
🔹 Mimicking real-world events like push notification, interrupts etc.
🔹 Checking layout and UX across multiple real device models, OS and screen sizes
A cloud device farm proves invaluable for above aspects allowing JIT access to 100s of device-OS combinations required for releases supporting iOS, Android etc. – much faster than setting up internal device lab capacity.
Automation frameworks like Appium further help accelerate test cycles by parallel test execution across numerous mobile environments reducing overall execution time.
Tracking Test Coverage
An optimal device testing and defect management strategy warrants some key test coverage metrics focus:
1. Models Tested
- Number / percentage of unique real device models validated
2. OS Versions
- Android version distribution – Jellybean, Kitkat, Marshmallow etc.
- iOS version distribution – iOS 12, 13, 14 etc.
3. Most Frequent Crashes
- Device models and OS versions facing maximum app crashes
4. Browsers Covered
- Safari versions – mobile vs. desktop
- Chrome versions
- Legacy browsers – IE11 etc.
5. Top Devices by Usage
- Models mapping to largest customer base from analytics data
6. Test Case to Device Matrix
- Traceability between test cases to device/OS/browser coverage
Actively tracking above KPIs provides actionable insights into test coverage levels, gaps to address, and high-risk target environments.
Now that we have covered device farm capabilities, testing methodology tips and best practices extensively, let us look at some real-world case studies next illustrating the benefits unlocked.
Real-World Impact – Case Studies
Device farms deliver immense value accelerating test cycles, enhancing release quality, and reducing business risks across industry verticals:
UK National Health Service (NHS)
❖ Manual and automated testing performed across 1300+ device models
❖ Covering latest device launches like iPhone 14 series, Samsung Fold etc.
❖ Ensuring healthcare applications reliably support clinicians and patients
Swiggy
❖ Food delivery mobile app tested on 1000+ devices
❖ Accelerating bug detection across fragmented mobile landscape
❖ Cloud device access reduced testing timelines by 40%
Clearly, expanding test coverage across exponentially fragmentation mobile device landscape is pivotal for digital experience quality and reliability across verticals like healthcare, retail, fintech etc.
Emerging Trends Expanding Relevance
The device fragmentation challenge and consequently importance of test cloud access only intensifies going forward as 5G, foldable devices and IoT proliferation bring a deluge of new gadgets and form factors.
Key trends to track include:
1. 5G Testing
Validate application performance under very low latency 5G networks with tools reporting video streaming quality, webpage load times etc.
2. Foldable Devices
Test seamless UI experience across folding and unfolding statuses on upcoming bendy screen devices.
3. Wearables
Confirm application compatibility with smart watches, bands, glasses etc. gaining mass adoption.
As software pervades every facet of customer and employee experience across sectors, scale of quality validation necessitates leveraging both automation techniques as well device farms spanning the broadest spectrum of environments.
Key Takeaways
1️⃣ Device farms help test mobile/web apps on 100s of real phones, tablets and desktops cost-efficiently.
2️⃣ Real devices detect issues missed on simulators ensuring better user experience.
3️⃣ Combining automation with device cloud access accelerates testing.
4️⃣ Faster debugging and expanded test coverage drives release velocity.
5️⃣ Support for new device types – foldables, wearables etc. expands scope.
Conclusion
This guide has hopefully armed you with an exhaustive perspective into device farms – their immense value proposition, how to capitalize via a structured methodology, productivity best practices, real-world usage proof-points and future direction.
QAs and test managers can significantly enhance their arsenal by embracing device farms – enabling digital experiences resilient across an unfathomably vast array of mobile, tablet and desktop environments end-users rely on daily.
Aligned with leading analyst recommendations, I strongly urge software teams to make device farms a pivotal facet of their test strategy today without which validation coverage will remain grossly inadequate.
Most providers offer trial accounts – use that to validate upside firsthand. Should any assistance be needed formulating an optimal device testing strategy for your organization, feel free to reach out!