Did you know that the projected market trends of app testing is expected to rise to a whopping $781.70 billion by the year 2029? That’s right! The usage of software products is only set to increase from this point on, which makes performance testing of digital products a cornerstone for organizational success.
As users, we keep seeing some apps running smoothly while some crash at the first sight of heavy traffic. The answer lies in their performance testing approach. Mobile app performance testing helps identify performance-related vulnerabilities and bottlenecks to ensure high-quality applications.
This post will take a detailed look at performance testing, what it is, its types, how to conduct it, top app performance testing tools, and some examples.
What is Performance Testing?
Performance testing refers to a specialized segment of app testing responsible for assessing an application’s resource usage, scalability, stability, speed, and responsiveness under a specific workload.
The objective of performance testing is to identify and eliminate any performance bottlenecks in digital products, which contributes to creating an impressive user experience. Once you’re done with functional testing, it’s time for performance monitoring and testing for performance in general.
Why is Mobile App Performance Testing Important?
Performance testing is important to improve user engagement, increase ROI, and ensure that users can easily navigate and access the overall interface. It also helps in the early identification and resolution of potential issues before app launch. By identifying and addressing performance issues promptly, it ensures reliability and, therefore, enhances app robustness.
Since automated performance testing can verify its performance against set criteria, it helps validate market claims by showcasing that the application is capable of handling the expected load and delivering optimized performance and speed.
It also helps identify weak links within the technology stack to ensure expected deliverables and increased responsiveness. All in all, performance testing validates features, measures, quality attributes, and provides end-user satisfaction.
When To Conduct Performance Testing
Organizations should conduct performance testing at different ADLC stages, such as before significant updates or major releases, adding new features capable of impacting performance, and mirroring production environments. You should also test for performance before anticipated user growth or high traffic and in the early stages of development, to quickly cache potential problems.
Performance Testing Metrics
Performance testing metrics or key performance indicators (KPIs) help organizations evaluate their current app performance. Some of the most common ones include:
- Memory- Available working storage space for the workload or a processor
- Throughput- Number of data units processed by a system over a certain time frame.
- Latency or response time- Time elapsed between a request and the response of a system to it.
- Average load time- The average amount of time every request takes for delivery.
- Bandwidth- Data volume per second moving between workloads or across a network.
- Error rate- Percentage of requests resulting in an error.
- Peak response time- Longest time the application takes to fulfill a request.
Read More: 60+ Data Metrics Tracked
How to Perform Mobile App Performance Testing
1. Decide On The Appropriate Tool and Test Environment
Identify the appropriate test environment that’s an accurate replica of the product environment you intend for. The tool you were using should preferably offer a wide array of features under a single umbrella and should offer integrations for other project management tools.
It goes without saying that it should have an AI-powered test assistant or at least some way to leverage AI and ML to automate testing further and streamline it.
2. Establish Acceptable Performance Levels
Gather numbers and establish goals indicating the success of your performance testing cycle. The best approach is to refer to set software expectations and project specifications to determine thresholds, benchmarks, and test metrics to define appropriate system performance.
3. Requirement Gathering Or Analysis
Conduct a meeting between the customer and the QA team to gather any business and technical requirements. Collect information such as hardware and software requirements, application functionality, app usage, test requirements, architecture, technologies, database, and intended users.
4. Test Planning and Design
The QA team uses the PoC information and requirements to create a test plan and test design. These contain data about hardware, workload, and test environment.
5. Test Scenario Creation
This step involves crafting tests that cover a wide variety of scenarios in which software’s performance is likely to be challenged in the real world. The best approach is to create scenarios that accommodate as many use cases as possible.
6. Test Configuration Preparation and Running Performance Tests
Manage configuration and ensure the QA team has access to all the required integrations, frameworks, and tools before running tests. Start by executing a test suite. Run tests in parallel so that you can save time without making any compromises in outcome accuracy.
7. Test Result Analysis and Reporting
For every single result, assign a meaningful and unique name. Include the reason for test failure, performance comparison, and modifications that concern the test environment in the test result summary.
After reducing the correct conclusion for the performance testing cycle, the testing team obtains a clear, deviation-free conclusion by simplifying test results. It’s followed by offering clarity to the development team and true, detailed information about the analytics.
8. Debugging and Retesting
Once you’re done identifying bugs after obtaining test results and sharing them with the rest of the team, it’s time to consolidate bugs and forward them to developers to get them fixed.
Of course, QAs should fix some basic bugs to save some extra time. Rerun tests after resolving performance shortcomings to confirm optimal software performance and a clean code.
Examples of App Performance Testing
Some real world examples of performance testing include well-known companies like Amazon, Facebook, Netflix, Uber, and Salesforce. They leave no stones unturned when they analyze app performance which is one of the biggest reasons behind their success.
Amazon performs load testing before its holiday sales, such as Prime Day or Black Friday, by simulating heavy searching, browsing, and other scenarios to prevent crashes and ensure seamlessness in the UX.
Facebook load tests check its messenger platform by simulating the real-time sending of billions of messages to ensure timely message delivery and low latency despite peak usage during events like New Year’s Eve.
Netflix tests its infrastructure’s performance by simulating excessive demand across various regions to check server scalability and smooth streaming for millions worldwide without delays or buffering.
Uber conducts application performance monitoring on mobile by checking how the app performs across various regions, networks, and devices to ensure crash-free, quick bookings and real-time ride tracking.
Salesforce conducts multi-tenant SaaS performance testing under stress. It tests the CRM amid major project updates to avoid slow response times or downtime that could negatively impact critical processes.
Top Mobile App Performance Testing Tools
Choosing the correct performance app testing tool can make or break your product. Let’s examine the top 5 mobile app performance testing tools that are gaining immense popularity.
1. Pcloudy
Pcloudy allows users to monitor their apps’ performance using ML-powered anomaly detection across more than 60 performance metrics. It facilitates optimum testing of UI, device, Network, and API performance and helps validate complex workflows across integrated systems.
Users can also monitor user journeys, third-party SDKs, and real-time API responses with audio QoE and perceptual video. Seamless integrations with your ecosystem and a secured Enterprise-grade platform are the cherries on top!
2. Apache JMeter
Apache JMeter is a leading open-source performance and load testing tool for web applications, databases, and APIs. It supports many protocols, such as FTP, JDBC, and HTTP, and is appropriate for scripting complex scenarios. Testers and developers looking for an open-source solution offering flexibility can choose this tool for their performance testing needs.
3. HP LoadRunner
HP LoadRunner offers a simulation for numerous live users with the help of its virtual user generator. This feature allows the tool to determine software application behavior by subjecting it to actual real-life loads, which contributes to its popularity in the market.
4. Gatling
Gatling, another popular open-source performance testing tool simulates user actions with the help of virtual users. It also helps developers in evaluating the stability and dependability of an application and promotes efficient load generation with non-blocking and asynchronous I/O principles. The visually compelling nature of its reports enhances the analysis of performance metrics and improves the precision of identifying bottlenecks.
5. OpenText Core Performance Engineering
OpenText Core Performance Engineering runs cloud-based performance tests without requiring load generators or controllers. It accurately simulates various user experiences across multiple devices or apps and enables quick resolution of issues by leveraging shift left testing to delve deep into DevOps and Agile methodologies. Users can create tests using a CSV, REST API, or HAR file.
UX gets compromised within nanoseconds, which makes performance testing mandatory. Organizations that proactively invest in performance testing can prepare to embrace greater delivery speed, user satisfaction, and product stability.
Pcloudy is a tool that offers deep analytics, cloud access to numerous devices, and powerful integrations for enterprise-grade performance testing that organizations can integrate into their teams’ workflows.
Sign up for their 30-day free trial now!
FAQs on Performance Testing
What’s the difference between performance testing and functional testing?
While functional testing checks that features continue to work as expected, performance testing is responsible for checking system behavior under some kind of stress.
Can we automate performance testing?
Yes, performance testing can be automated with tools like Pcloudy, Apache JMeter, and so on. AI agents further help streamline automated performance testing, which can be especially beneficial in CI/CD pipelines where regular performance checks are needed.
What causes compromised application performance?
The most common causes of poor application performance include inadequately optimized database queries or code, memory leaks, network latency, server configuration problems, and inadequate caching strategies.
