A QA Engineer’s Guide to Mobile Performance Testing Best Practices

Mobile performance testing is now essential to delivering high-quality mobile applications in a fast-paced digital environment. Users expect fast, streamlined, and responsive mobile experiences. Therefore, it is essential to ensure the app “performs” well across different devices, operating systems, and network conditions, which is the main focus of mobile performance testing. Mobile performance testing measures and improves a variety of performance aspects, including application load time, responsiveness, resource usage, and efficiency with power consumption and battery. Performance testing identifies performance bottlenecks early and provides a better user experience across all aspects of the test.

As mobile applications become more complicated and resource heavy with additional features, additional performance testing can assist developers and QA teams to ensure stability, speed, and reliability in the application for multiple scenarios in the real world. Keep in mind that the team will be overtly defensive in their efforts if they implement best practices and use some of the recommended performance testing tools. It is beneficial to identify potential issues or bottlenecks but, equally as important is the team’s ability to put into place further optimizations that will make the application perform better. In this blog, we will explore proven strategies, tools, and practices to help you implement effective mobile performance testing in your development lifecycle.

1. Mobile performance testing is now essential to delivering high-
quality mobile applications in a fast-paced digital environment. Users
expect fast, streamlined, and responsive mobile experiences.
Therefore, it is essential to ensure the app “performs” well across
different devices, operating systems, and network conditions, which
is the main focus of mobile performance testing. Mobile performance
testing measures and improves a variety of performance aspects,
including application load time, responsiveness, resource usage, and
effqciency with power consumption and battery. Performance testing
identifqes performance bottlenecks early and provides a better user
experience across all aspects of the test.
As mobile applications become more complicated and resource heavy
with additional features, additional performance testing can assist
A QA Engineer’s Guide to Mobile
Performance Testing Best Practices

2. developers and QA teams to ensure stability, speed, and reliability in
the application for Bltiple scenarios in the real world. Keep in mind
that the team will be overtly defensive in their efforts if they
implement best practices and use some of the recommended
performance testing tools. It is benefqcial to identify potential issues
or bottlenecks but, equally as important is the team’s ability to put
into place further optimizations that will make the application
perform better. In this blog, we will explore proven strategies, tools,
and practices to help you implement effective mobile performance
testing in your development lifecycle.
Table of Content
Why Mobile Performance Testing Matters
Comon Performance Challenges in Mobile Apps
Understanding Mobile Performance Metrics
App Launch Time
Resource Usage (CPU, Memory, Battery, Thermal)
Network Usage and API Response Time
Frame Rendering & Scrolling Smoothness
Types of Mobile Performance Testing
Load Testing
Stress Testing
Spike Testing
Endurance (Soak) Testing
Network SiBlation Testing
Key Tools for Mobile Performance Testing
Android Profqler / Xcode Instruments
Firebase Performance Monitoring
Charles Proxy / Wireshark
JMeter with Mobile EBlators
Best Practices for Effqcient Mobile Performance Testing
Start Early in the Development Cycle

3. Test on Real Devices and Networks
SiBlate Real-World Scenarios
Automate Performance Tests Where Possible
Monitor Resource Usage Continuously
Set Performance Budgets and SLAs
Comon Pitfalls to Avoid in Mobile Performance Testing
Relying Solely on EBlators
Ignoring Background App Behavior
Skipping Testing on Older Devices or OS Versions
Not Considering Different Network Conditions
Integrating Performance Testing into CI/CD
Setting Up Automated Performance Gates
Performance Regression Monitoring
Reporting and Alerting with CI Tools
Real-life example
Performance Improvements After Best Practice
Implementation
Key Takeaways
Conclusion
Why Mobile Performance Testing
Matters
With the pace of change in the digital space today, people expect
their apps to load quickly, response times to be fast, and they expect
them to work! A delay of a few seconds is often enough to send users
away from your app to the top competitor. There are millions upon
millions of apps available and users can pick the fqrst reply to their
search request or they can even leave a review with less-than-stellar
feedback on an app’s performance. Performance is no longer seen as
a “nice to have” and is a critical success factor.
Mobile performance testing can confqrm that your application can

4. deliver a consistent and smooth experience for a person using it
under a variety of different conditions (different devices, networks,
and usage). It can help discover bottlenecks in time and speed of
usage, and assure that resources are utilized effectively. And most
importantly, with regard to performance, it assures that users will
stick with the app because it is validating and performing well in the
real world.
Any mobile product has the potential for poor performance, whether
you are developing a productivity app, a children’s game, or an e-
comerce platform. So you don’t want to have the negative attrition
in terms of users, traffqc, and revenue, and of course, your brand
reputation, due to a poorly performing app. This is why performance
testing Bst be a part of your development lifecycle, so that you can
assure a high-quality mobile product.
Common Performance Challenges in
Mobile Apps
Mobile app performance is influenced by countless factors, some of
which the developer has no control over. Some of the common
performance challenges are:
Device Fragmentation: It is feasible to have thousands of
devices that vary in CPU speed, memory, screen size, and
operating systems. Having confqdence that the mobile
application will perform in the same way across these
differences is a major hurdle.
Network variability: Meanwhile, mobile users are switching
from Wi-Fi to 4G, to 5G, or they are in poor connectivity
situations (such as an elevator or a tunnel). As a result, the speed
of the network and latency can greatly impact the performance
of the app, and that is especially true for data-driven
applications.

5. Resource Constraints: Battery life, memory limitations and
processing power, and the motor’s operating speed limit are all
important aspects related to mobile devices. Applications that
are poorly optimized and use too much CPU or battery time are
usually deleted, solely on that basis.
Operating System interrupts – background processes:
Mobile OS may pause or kill apps or processes that are running in
the background in order to save resources. Therefore,
performance will impact apps that interact with other
applications that are currently running in the background, as well
as the other system processes.
Animations and UI Rendering: A sluggish application can be
shown by poor frame rate, poor transitions, or slow scrolling,
which can quickly turn into a bad user experience. Performing
some performance testing early in the process will help ensure
that rendering and UI actions are smooth.
Recognizing these challenges early in the development cycle enables
teams to create robust, user-friendly applications. The rest of this
blog will explore how to tackle these issues effectively through best
practices, tools, and testing strategies.
Understanding Mobile Performance
Metrics
Performance testing begins with understanding what to measure.
Mobile performance isn’t just about speed; it’s about user
perception, system effqciency, and resource optimization. Let’s
break down the key metrics you should monitor and why they matter
with practical examples.
App Launch Time
What it is:

6. App Launch Time measures how long it takes for the app to open
and become interactive after the user taps the icon.
Why it matters:
First impressions are everything. A long launch time leads to
user frustration and increased app abandonment rates.
Example:
Imagine a banking app that takes 5–7 seconds to launch,
especially when a competitor opens in under 2 seconds. Users
are more likely to switch to faster alternatives. A good target is to
keep cold launch time under 2 seconds and hot launch time
under 1 second.
How to measure:
Tools like Android Profqler, Xcode Instruments, or Firebase
Performance Monitoring can help you track cold and warm
launch times over time.
Resource Usage (CPU, Memory,
Battery, Thermal)
What it is:
This includes monitoring how Bch CPU your app consumes,
how Bch memory (RAM) it uses, its impact on battery life, and if
it causes the device to overheat.
Why it matters:
Apps that hog system resources lead to poor device
performance, overheating, faster battery drain, and in some
cases, OS-level throttling or app crashes.
Example:
A video streaming app that continues rendering and decoding
video even when the user has paused playback may spike CPU
usage, reducing battery life quickly. Over time, this could lead to
negative reviews like “app drains battery fast.”
How to measure:
Use profqling tools like Android Studio Profqler, Xcode

7. Instruments (Energy Log, Allocations), or third-party tools like
Leaks Instrument to monitor real-time usage.
Network Usage and API Response Time
What it is:
This refers to how Bch data your app consumes and how
quickly it receives responses from APIs or external services.
Why it matters:
Slow or unoptimized network calls can lead to poor user
experience, especially in regions with limited bandwidth. High
data usage can also concern users with limited mobile data
plans.
Example:
An e-comerce app that takes 4–5 seconds to load product
images due to uncompressed fqles or large payloads can
frustrate users. Optimizing image size and caching strategies
can improve speed and reduce data usage.
How to measure:
Use tools like Charles Proxy, Wireshark, or Firebase
Performance Monitoring to track API response times and
payload sizes.
Frame Rendering & Scrolling
Smoothness
What it is:
This measures how smoothly the UI responds to user
interactions, especially during animations or scrolling. It’s often
evaluated in Frames Per Second (FPS) and jank rate.
Why it matters:
Laggy UI and janky scrolling break immersion and lead users to
perceive the app as slow or buggy, even if the backend is
responsive.

8. Example:
A social media app may freeze or stutter while scrolling through
posts due to ineffqcient image loading or heavy background tasks
on the main thread. Users expect 60 FPS for a fluid experience.
How to measure:
Use Android GPU Rendering Profqle, Xcode’s Core Animation
Instrument, or integration with Systrace to track rendering
performance.
Types of Mobile Performance Testing
Mobile performance testing is not one-size-fqts-all. Different
scenarios call for different strategies to uncover bottlenecks and
weaknesses. In this section, we’ll explore the fqve primary types of
mobile performance testing, along with practical examples for each.
Load Testing
What it is:
Load testing evaluates how the app behaves under expected
user traffqc or data load. The goal is to measure response times,
resource usage, and stability when the system is under normal or
peak operating conditions.
Example:
Let’s say you’re developing a food delivery app. You siBlate
5,000 users browsing restaurants and placing orders at the
same time, similar to peak dinner hours. Load testing helps
ensure the backend, APIs, and database can handle this traffqc
smoothly.
Key Outcome:
You identify whether the app and infrastructure meet
performance SLAs (Service Level Agreements) under normal and
peak usage.

9. Stress Testing
What it is:
Stress testing pushes the app beyond its capacity limits to
observe how it behaves under extreme load. The goal is to fqnd
the breaking point and understand how the system recovers
after failure.
Example:
Imagine your app handles 2,000 concurrent users under normal
conditions. In stress testing, you might simulate 10,000 users
hitting your login endpoint to see if the app crashes or throttles
gracefully.
Key Outcome:
You discover how the app fails whether it degrades gracefully,
freezes, or crashes and how it recovers after the stress ends.
Spike Testing
What it is:
Spike testing checks how your app handles sudden surges in
traffqc, either a quick rise or drop in user activity. It’s a subtype
of stress testing that evaluates responsiveness to abrupt traffqc
fluctuations.
Example:
Consider a ticket booking app for concerts. When sales open,
traffqc might spike from 500 to 15,000 users within a few
seconds. Spike testing helps you prepare for such real-world
rush scenarios.
Key Outcome:
You ensure your app can scale quickly and stabilize even when
hit by an unexpected load surge.
Endurance (Soak) Testing

10. What it is:
Endurance testing (also known as soak testing) evaluates the
app’s long-term performance under a steady load. It helps
identify memory leaks, CPU spikes, or database connection pool
issues over time.
Example:
You siBlate 1,000 users streaming music or watching videos on
your app non-stop for 12 hours. If memory usage keeps
increasing or the app becomes sluggish, it indicates a
performance degradation over time.
Key Outcome:
You detect issues that don’t show up in short tests, like memory
leaks or ineffqcient garbage collection.
Network Simulation Testing
What it is:
This type of testing siBlates varied network conditions such
as 2G, 3G, 4G, 5G, or unstable Wi-Fi to test how your app
performs in real-world connectivity scenarios.
Example:
You test your navigation app by siBlating a slow 3G
connection with 30% packet loss. You analyze whether the app
still provides directions, updates maps, or handles offline
gracefully.
Key Outcome:
You ensure a smooth user experience in regions with weak or
fluctuating internet connectivity.
Test Type Purpose
Example
Scenario
Goal
Load Testing
Test under
expected traffic
5,000 users
ordering food
Stability under
peak use

11. Test Type Purpose
Example
Scenario
Goal
Stress Testing
Push beyond
limits
10,000 users
logging in at once
Identify breaking
points
Spike Testing
Sudden traffic
bursts
Traffic surge when
tickets go on sale
Elasticity and
scaling
Endurance (Soak)Long-term usage
12 hours of
streaming
Detect
memory/resource
leaks
Network
Simulation
Unstable network
conditions
Simulate 3G with
30% packet loss
Ensure
offline/poor
network UX
Key Tools for Mobile Performance
Testing
Choosing the necessary tools for performance testing of your mobile
app will ensure that your app can be tested properly under various
devices, networks, and workloads. Each tool operates under a specifqc
category, from monitoring CPU usage to identifying API response
time. These toolstogether are a performance profqle of your
application.
Let’s look at the main tools that can help you accelerate your mobile
performance testing, plus concrete examples for each one.
Android Profiler / Xcode Instruments
Purpose:
These are platform-native tools you can use to determine the
CPU, memory, network, and energy usage for iOS and Android
apps.
Android Profqler (Android Studio): provides visuals of app

12. performance on physical devices and emulators.
Xcode Instruments (iOS) also provides vital tools for
identifying performance issues (e.g., Time Profqler,
Allocations, Energy Log).
Example:
You are trying to test a fqtness tracking app and want to learn
why it is draining the battery so quickly. Using Xcode’s Energy
Log or the Battery tab in Android Profqler shows that background
GPS updates and UI rendering tasks are always running, even
when you have the screen off.
Best for:
These tools will provide good insight into diagnosing UI lags,
memory leaks and battery-sucking background processes.
Firebase Performance Monitoring
Purpose:
A real-time, cloud-based performance monitoring tool for
Android and iOS apps, part of the Firebase suite by Google.
Features:
Tracks app startup time, network latency, screen
rendering speed, and custom traces.
Monitors performance across devices, geographies, and
OS versions.
Example:
In an e-commerce app, you use Firebase Performance
Monitoring to measure API latency. You discover that product
listing APIs are slow only in certain regions due to CDN
misconfqguration — something you’d miss in local testing.
Best for:
Live performance tracking post-deployment and identifying
issues at scale.
Charles Proxy / Wireshark

13. Purpose:
These tools capture and analyze network traffqc. They help
identify large payloads, ineffqcient API calls, and insecure data
transfers.
Charles Proxy is more UI-friendly and great for mobile
debugging.
Wireshark is a powerful packet sniffer used for deep-dive
network analysis.
Example:
In a social networking app, Charles Proxy reveals that your app
is fetching high-resolution profqle pictures even on slow
networks. By introducing adaptive image loading, you improve
loading speed and reduce data usage.
Best for:
Inspecting API performance, data compression, security, and
bandwidth consumption.
JMeter with Mobile Emulators
Purpose:
Apache JMeter is a load testing tool that can siBlate
thousands of concurrent users and assess backend/API
performance under stress.
Use with Emulators:
You can combine JMeter with mobile emulators or devices to
mimic real app interactions and observe backend behavior under
load.
Example:
For a food delivery app, you use JMeter to siBlate 1,000 users
placing orders within 5 minutes. Backend logs reveal delayed
order processing and overloaded database writes, prompting you
to optimize the API.
Best for:
Load testing and backend performance validation in mobile

14. applications.
Best Practices for Efficient Mobile
Performance Testing
Performance testing for mobile is not only about picking the right
tools; it is also about developing the right design/testing habits in
your development and testing ecosystem so that poor performance
does not accidentally fqnd its way to production. Some proactive best
practices teams can adopt to ensure they deliver a high-quality app
experience from day one are:
Let’s explore the performance test best practices and examples for
each.
Start Early in the Development Cycle
Why it matters:
Waiting until the end of development to start performance
testing often leads to last-minute fqxes or unresolvable issues.
Instead, integrate performance checks early (Shift-Left Testing)
to catch bottlenecks before they escalate.
Example:
In a fqntech app, an API call for fetching user transactions is slow
but isn’t noticed until the fqnal build. Had the team tested earlier,
they could have optimized the backend logic when it was easier
and cheaper to fqx.
Pro Tip:
Use lightweight profqling tools in your daily builds to track
regressions continuously.
Test on Real Devices and Networks

15. Why it matters:
EBlators and siBlators are helpful in early development, but
they don’t reflect real-world performance. Real devices vary
in screen size, RAM, CPU, and network modules, which directly
affect app behavior.
Example:
Your team tests an app on high-end devices only. Once released,
users on low-end devices report lag and crashes due to memory
constraints. Testing on a range of actual devices helps avoid this.
Pro Tip:
Maintain a test lab with low-end, mid-range, and flagship
devices across Android and iOS.
Simulate Real-World Scenarios
Why it matters:
Users don’t operate in controlled environments. They switch
networks, Bltitask, receive calls, and use low battery modes.
SiBlating these conditions reveals how your app handles
interruptions and constraints.
Example:
You siBlate a 3G network with packet loss for a ride-sharing
app. You fqnd that map updates freeze intermittently, indicating a
need for better offline handling or retry logic.
Pro Tip:
Use tools like Network Link Conditioner (iOS) or Android’s
Network Profqler to replicate network variability.
Automate Performance Tests Where
Possible
Why it matters:
Manual performance testing is time-consuming and
inconsistent. By automating performance test scripts, you can

16. run tests frequently (e.g., in CI/CD pipelines) and catch
regressions early.
Example:
In a news app, you automate a test to measure article load time
after scrolling. Every time the app is built, the test checks for any
slowdowns, ensuring new features don’t degrade speed.
Pro Tip:
Use tools like Appium with custom timers, or integrate
performance thresholds into your test automationframework.
Monitor Resource Usage Continuously
Why it matters:
CPU spikes, memory leaks, or battery drains often appear after
prolonged use, not in short tests. Continuous monitoring helps
catch these long-term issues.
Example:
In a Bsic streaming app, you notice via Firebase Performance
Monitoring that memory usage steadily increases over 2 hours of
use. This indicates a memory leak that could crash the app in real
scenarios.
Pro Tip:
Use tools like LeakCanary (Android) or Instruments (iOS) for
memory leak detection, and monitor trends over time.
here the flow chart of Continuous monitoring helps identify
memory leaks, CPU spikes, and battery drains

17. Set Performance Budgets and SLAs
Why it matters:
Just like code quality standards, you need performance
targets. Defqne measurable goals such as “Cold launch time \< 2
seconds” or “API response time \< 500ms.”
Example:
For an online shopping app, you defqne a budget of max 1.5s API
latency for checkout. During performance regression testing,
any result above this threshold blocks the release, ensuring
consistency.
Pro Tip:
Make performance part of your Defqnition of Done (DoD) and
track it in pull requests and CI builds.
Common Pitfalls to Avoid in Mobile
Performance Testing
Even with the right tools and intentions, performance testing can fall
short if certain critical mistakes are made. Many teams unknowingly
limit the effectiveness of their tests by skipping real-world
considerations. In this section, we’ll break down the most common
pitfalls and how to avoid them.

18. Relying Solely on Emulators
Why it’s a problem:
EBlators are great for quick tests and debugging, but they
don’t siBlate real hardware constraints like CPU throttling,
memory limitations, battery consumption, or network variations.
Relying only on them gives a false sense of app performance.
Relying only on them gives a false sense of app performance.
Example:
A team tests an image-heavy social media app on emulators,
where it runs smoothly. However, when users on older phones
start using the app, they report crashes and slow loading. The
eBlator didn’t reflect the device’s actual limitations.
How to avoid it:
Always include real-device testing (low-end, mid-range, and
flagship models) as part of your performance test plan.
Ignoring Background App Behavior
Why it’s a problem:
Many apps behave differently when running in the background or
after being resumed. Performance issues like memory leaks,
battery drain, or data sync failures often surface in these
states but are frequently missed.
Example:
A fqtness app tracks user steps. When users minimize the app
while walking, tracking pauses without notice. This wasn’t
caught during testing because testers only used the app in
foreground mode.
How to avoid it:
Test how your app behaves when:
Sent to background and resumed
Used alongside other apps
Subjected to system-level interruptions like calls or

19. notifqcations
Skipping Testing on Older Devices or
OS Versions
Why it’s a problem:
Older devices and OS versions often have limited RAM,
outdated processors, and different APIs. If you test only on
the latest models, you risk alienating a large portion of your user
base.
Example:
An education app runs flawlessly on Android 13 devices but
crashes frequently on Android 8 due to a deprecated library call.
Without backward compatibility testing, this issue went
unnoticed until negative reviews surfaced.
How to avoid it:
Maintain a test matrix that includes:
Legacy OS versions (e.g., Android 8–9, iOS 12–13)
Budget devices with low specs
Global market–popular brands (e.g., Xiaomi, Samsung A-
series)
Not Considering Different Network
Conditions
Why it’s a problem:
Assuming all users have fast, stable internet leads to serious
performance blind spots. In reality, many users experience poor
connectivity, high latency, or packet loss — especially in
emerging markets or rural areas.
Example:
An online payment app works well on Wi-Fi but frequently fails
transactions on 3G networks, leading to user frustration and
fqnancial loss.

20. How to avoid it:
Use network siBlation tools (e.g., Android Profqler, Charles
Proxy, Network Link Conditioner) to test:
Low bandwidth (2G/3G)
Intermittent connections
Offline scenarios
Integrating Performance Testing into
CI/CD
Modern mobile apps require fast release cycles, and performance
should not be compromised in the race to deploy. Integrating
performance testing into your CI/CD pipeline helps detect issues
before they hit production. This ensures each code change meets
both functionaland performance standards.
Let’s break it down into actionable strategies with examples.
Setting Up Automated Performance
Gates
What does it mean:
Just like unit or UI tests, you can add performance checks as
gates in your CI pipeline. If an app build exceeds predefqned
thresholds (e.g., launch time, API latency), the build fails or is
flagged preventing regressions from reaching users.
Example:
In a banking app, you defqne a gate that fails the build if login
time > 3 seconds or CPU usage > 80% during load. Every
comit triggers performance tests, and any violation blocks
deployment to staging.
How to do it:
Use tools like JMeter, Lighthouse CI, or custom Appium

21. scripts with timers.
Defqne performance budgets (e.g., max render time,
acceptable frame drops).
Include gates in Jenkins, GitHub Actions, GitLab CI, or
CircleCI pipelines.
Performance Regression Monitoring
What does it mean:
Even a small UI tweak or library upgrade can degrade
performance. Monitoring for performance regressions ensures
that changes don’t silently reduce app speed or responsiveness.
Example:
A food delivery app adds a new animation on the order
confqrmation screen. Post-deployment, screen rendering time
jumps from 400ms to 1200ms. Because this wasn’t monitored,
the issue wasn’t caught until user complaints.
How to do it:
Store historical performance test results (e.g., in InfluxDB,
Grafana).
Run baseline comparisons against previous builds.
Alert teams when key metrics (like launch time or frame
rates) worsen by X%.
Reporting and Alerting with CI Tools
What does it mean:
Automated test results are useless if they’re buried in logs. CI
tools should generate clear, actionable reports and alert
relevant teams when performance tests fail or metrics degrade.
Example:
Using GitHub Actions + Slack integration, your CI pipeline sends
an alert to the #qa-alerts channel whenever API latency goes
above 800ms in the latest build. The team is notifqed immediately

22. and can take corrective action before merging the PR.
How to do it:
Use plugins or scripts to generate HTML/JSON reports.
Integrate with Slack, Teams, or email for instant alerts.
Visualize trends in Dashboards (Grafana, Datadog,
Firebase).
Real-life example
Performance Improvements After Best
Practice Implementation
To truly understand the value of mobile performance testing, it helps
to see how applying best practices makes a real-world difference.
In this case study, we’ll walk through how a mobile team transformed
a sluggish, crash-prone app into a smooth, responsive experience by
simply integrating performance testing into their workflow.
Background
App Type:
E-comerce Mobile App (Android + iOS)
Initial User Rating:
3.2 on Play Store
Common Complaints:
Slow app launch
Crashes on low-end phones
The cart is taking too long to update
Drains battery quickly during browsing
Step 1: Baseline Performance Testing
The QA team began by measuring key metrics using tools like
Firebase Performance Monitoring and Android Profqler.

23. Metric Initial Reading
Cold Start Time 6.1 seconds
API Response Time (Cart API) 2.8 seconds
Memory Usage 290 MB average
Crash Rate 3.5% on Android 9 devices
Battery Consumption 8% in the 10-minute session
These values were clearly unacceptable, especially in competitive
markets.
Step 2: Implementation of Best Practices
The team applied the following performance best practices:
Added performance testing to CI/CD (Cold Start & Cart API
latency monitored with each build)
Tested on real devices across price segments (including Android
Go phones)
SiBlated 3G/4G networks to understand load time under real
conditions
Refactored memory-heavy screens using flatlists & lazy loading
Optimized backend APIs with gzip compression and caching
Step 3: Post-Implementation Results
After one month, here’s how the app’s performance improved:
Metric Before After
Cold Start Time 6.1s 2.3s
Cart API Response 2.8s 750ms
Memory Usage 290MB 160MB
Crash Rate 3.5% 0.8%

24. Metric Before After
Battery Usage 8% / 10 min 3%
Play Store Rating 3.2 4.3
Want your app to run faster and
smoother?
Our QA team can help find what’s slowing it down and fix it before your users
notice.
Key Takeaways
Real-device testing helped uncover hidden issues on low-end
phones
Performance budgets in CI/CD prevented regressions
SiBlating real-world conditions, like poor networks, revealed
crucial bottlenecks
Performance-focused refactoring signifqcantly improved UX
and app store ratings
Conclusion
Incorporating mobile performance testing best practices guarantees
that mobile apps perform fast, reliably, and are built for real-world
usage. Testing across devices, operating systems, and network
conditions helps to identify additional hidden bottlenecks that may
affect user experience. Integrating performance testing into CI/CD
pipelines is a good way to check regressions as early as possible and
maintain established quality with each release.
Talk to our Expert

25. Using tools such as Firebase, Android Profqler, and Lighthouse to
measure initial performance metrics such as launch time, memory
footprint, battery drain, and network latency can show teams which
key metrics are negatively impacting their applications’ performance.
If teams can set performance budgets, measure, and have
continuous measurement of those budgets, it offers better control
over the application’s behavior as the app continues to evolve.
SiBlating a device with background activity, including a power of
resources, and how device-specifqc behaviors impact performance
can help demonstrate application stability across various scenarios.
Monitoring regressions while avoiding common mistakes, such as
exclusively testing against eBlators and sources of value at the
limits with low-end devices while ignoring true network conditions
are signifqcant mistakes to avoid while trying to build a smooth and
consistent user experience. Monitoring performance regression for
production applications, along with time-sensitive and actionable
reporting, and real-time alerts of potential threats to performance,
gives teams better capacity to respond quickly in order to help
stabilize their applications.
These best practices should contribute and lead to and improve high-
performing mobile applications that help meet users’ expectations,
decrease churn, and differentiate them from competitive app
marketplaces. A focus on performance is more than just how quickly
applications respond to user input – quality, consistency,
responsiveness, and clear communication contribute to high
performance.
 
Originally Published By:- JigNect Technologies