Most of us have rejoiced in a fun-filled online game of Ludo with our family and friends. It’s fascinating how the Ludo King game app had around 15.47 million Android downloads in 2024. While the game invoked a deep sense of nostalgia, one of the primary reasons behind the explosive success of the app is its smooth-as-butter performance.
This confirms performance testing as one of the most prominent factors that play a crucial role in the success of a mobile application. But what kind of performance tests are the most appropriate for different apps? In this post, we’ll look at the primary types of performance testing with examples and testing tool suggestions.
Performance testing is a non-functional testing practice used to evaluate application behavior under specific workloads. The primary goal is to ensure that the system adheres to performance benchmarks for stability, scalability, responsiveness, and speed.
Core Types of Performance Testing and Examples
Performance testing is the answer every organization needs to one question: Is their application capable of performing optimally under pressure? Well, as long as they leverage a combination of the appropriate types of performance testing, the entire process is a breeze. Let’s look at the core performance testing types with some examples.
Load Testing
Load testing checks system functioning under projected virtual users concurrently performing tasks over a stipulated amount of time. You should perform testing while determining whether a system can support a certain number of anticipated concurrent users. It involves configuring tests and then simulating various user scenarios, focusing on various aspects of a system.
Example:
An e-commerce platform simulating thousands of users simultaneously placing orders on their mobile app can help verify the correct working of inventory updates, payment gateway, checkout flow, and other similar factors without any degradation and performance issues. It also helps ensure that the server can manage normal traffic during their marketing campaigns or everyday operations.
Stress Testing
Stress testing is responsible for checking a system’s upper limits by making it undergo extreme loads and checking its recovery while it returns to normal. KPIs include response time and throughput. It also looks for data corruption, security problems, slowdowns, and denials of service. All you need to do is define a test case with a high number of concurrent virtual users.
Example:
Suppose a video streaming platform suddenly gets more than 100,000 viewers concurrently, which is way beyond the load it expects on a regular basis. Such a simulation helps in observing system behavior under a lot of pressure. The organization aims to identify points of failure like broken video playback, server timeouts, memory crashes, etc., to ensure that even if the system fails, it does so gracefully.
Endurance Testing
Endurance testing, also known as soak testing, involves performance measurement where the workload is normal over a long time. The goal is to determine whether long-running tasks give rise to issues.
Example:
For instance, a SaaS analytics dashboard simulates 1,000 active users for 48 hours continuously to check for long-term problems such as system overheating, slow database queries, or memory leaks. Conducting such a test helps uncover issues that might get overlooked during shorter test runs but are very capable of degrading performance as time goes by.
Spike Testing
Spike testing floods an application with extreme and sudden decreases and increases or spikes in the load to determine the speed of system scaling and recovery or whether spikes in traffic end up giving rise to performance issues and producing bottlenecks. Such tests are useful in situations where the nature of the traffic is unpredictable, sudden, or large, such as in a PR appearance, concert ticket, sales, product drop for something limited edition, etc.
Example:
A ticket booking app experience has a 10x spike in users within a few seconds while a flash sale occurs. Spike testing uses this scenario to check whether the system can avoid crashes or 503 errors, maintain response times, and scale automatically in a sudden influx scenario.
Volume Testing
Volume testing measures how a system handles extensive data and identifies system response when it’s loaded with enormous data. It’s important for data-heavy systems such as databases to ensure their functionality and optimal performance under heavy data loads. As a result, volume testing minimizes risk associated with data loss, ensures scalability, and helps uncover problems with large data loads.
Example:
Here, we are taking the example of a CRM system that has more than a million customer records and trying to check the performance of the filter, search, export, and similar operations. Such a simulation helps evaluate the database performance under enormous data sets to ensure the efficiency of queries and avoid UI freezes or timeouts.
Recovery Testing
Recovery testing evaluates a system’s ability to recover from unexpected events, errors, or failures. It induces failures through different methods by simulating reasons behind possible outages, such as error-filled updates, a network disruption, and hardware failure.
Example:
In this example, a database disconnection occurs during the submission of transactional forms. After database restoration, the system undergoes evaluation to test its recovery capabilities without the need for manual restart, corrupted sessions, or data loss. This is crucial to maintaining trust in healthcare, banking, or other similar high-stakes applications.
Scalability Testing
Scalability testing measures an app’s ability to scale up or down during unexpected increases in transactions, data volume, or users. It uncovers the point where an app is simply unable to scale beyond certain levels of load. You can perform scalability testing on the database, hardware, or software to determine server scaling and auto scaling effectiveness.
Example:
For example, an e-learning platform increases its user load to 10x concurrent learners to check its auto-scaling capabilities, and the original servers automatically spin up. Such a test ensures infrastructural growth and performance stability despite increased demand.
Capacity Testing
Capacity testing measures the maximum number of users handled by an application, simultaneously, exercising performance benchmark preservation, mostly in service-level agreements or SLAs. The goal here is to check the maximum load it can handle while keeping its performance optimal. It determines the maximum load capacity where features and functionalities continue to perform as per expectations, also referred to as the safety zone.
Example:
Here, we are taking the example of a learning management system (LMS) undergoing testing by incrementally adding extra users starting from 100 and increasing them until the response time is larger than an acceptable 2-second-long threshold. Here, the maximum number pre-degradation provides clarity on realistic usage limits and contributes to better infrastructural planning.
Peak Testing
Peak testing checks system performance under the maximum load it expects and identifies its behavior and handling when you push its limit. Some of its benefits include enhanced user experience and competitiveness, minimized crash-related risks, and maximum capacity.
Example:
A Bank tests their app by simulating a payday rush by creating a scenario in which a majority of users simultaneously log in to pay bills, transfer money, or check their balance. Such a test ensures that all mobile logins, real-time transactions, and similar services remain responsive and accelerated even when there’s a monthly peak.
Performance Testing vs. Load Testing vs. Stress Testing
Now that we have discussed the different performance testing types with examples in detail, let’s move on to some specific distinctions between performance testing, load testing, and stress testing. This table represents crisp details offering clarity on performance testing vs. load testing, vs. stress testing.
| S No. | Key Aspect | Performance Testing | Load Testing | Stress Testing |
|---|---|---|---|---|
| 1. | Definition | Used to evaluate application stability, scalability, and speed under different conditions. | The performance testing subset is meant to measure the app’s handling capabilities for varying user load. | Performance testing subset to evaluate app behavior under extreme conditions. |
| 2. | Goal | Overall assessment of system performance across varying load levels. | Determines the handling of traffic by the application under test. | Identifies the breaking point and failure recovery abilities of an app. |
| 3. | Area of Focus | Speed, scalability, responsiveness, and resource usage. | Throughput, response time, and stability under normal scenarios for app usage. | Error handling, stability, and behavior beyond everyday scenarios. |
| 4. | Level of Load | Encompasses normal, high, and peak load scenarios. | Normal or expected traffic conditions. | Spiked or extreme traffic |
| 5. | Result | Recognizing areas of optimization and performance bottlenecks. | Facilitates resource tuning and capacity planning. | Checks for stability, robustness, and recovery mechanisms. |
Top Performance Testing Tools
Performance testing tools use control variables to simulate real-world scenarios, including hardware configurations, data volumes, network bandwidth, concurrent users, etc. Let’s take a look at the top performance testing tools and the unique capabilities they offer.
1. Pcloudy
Besides offering all basic performance testing features and being one of the most powerful performance testing tools for 2025, Pcloudy’s latest version 7.1 has been rolling out more upgrades than ever! For instance, the Object Spy upgrade allows seamless access to object properties, effortless interaction with backend app elements, and precisely manages custom XPaths.
You can also unlock critical real-time insights like network latency, memory, and CPU usage. Want to stay ahead of the competition curve? PCloudy has added support for a wide array of browsers and devices (Opera and Samsung Galaxy S25 & S25 Ultra). While all performance testing tools offer the necessary insights and action steps, Pcloudy cranks it up a notch by going beyond just key metrics and basics.
How to Test An App for Its Performance on PCloudy:
Step 1: Simply go to the MyData section and upload the app you want to test for its performance.
Step 2: Click on ‘App Performance Testing’.
Step 3: Go to the performance testing module. Here, you’ll get access to run your performance tests.
Step 4: Select the application and establish the device connection. The app will launch once you select the option to record.
Step 5: Mimic user journey by performing actions such as making transactions, adding items to cart, and so on.
Step 6: Go to the reports section opens up the performance dashboard.
2. Apache JMeter
Apache JMeter uses both dynamic and static resources to evaluate apps and simulates different load conditions on networks, objects, groups of servers, or individual servers to check their performance and resilience under different scenarios. Users don’t require extensive scripting to create test plans.
The tool offers centralized control over load injectors and effortlessly integrates with CI/CD pipelines. Some of the protocols it supports include HTTP, FTP, SMTP, HTTPS, POP3, TCP, IMAP, UDP, SOAP, JMS, REST, JDBC.
3. Gatling
Gatling is one of the most popular open-source tools that checks for performance by creating virtual users with simulations of user behavior. It helps developers in evaluating app throughput, dependability, and scalability under different load conditions.
Some of its key features include asynchronous and non-blocking I/O principles to generate load, a declarative DSL that defines test scenarios, and visually compelling reports to improve precision, performance, and metric analysis.
Conclusion
Performance testing helps QA teams discover bottlenecks instead of dealing with them after they have reached production. Some of these bottlenecks include concurrency and threading issues, poorly tuned APIs, memory leaks, and lack of optimization in database queries.
Making performance testing a primary area of concern makes sense, especially in a world where the fastest and smoothest applications are shaping user expectations. This business imperative validates the organizational infrastructure and ensures that it can support immense future growth by bridging the gap between user satisfaction and QA.
FAQ
When should organizations do performance testing?
Ideally, organizations should integrate performance testing in the earlier phases of the development cycle by adopting a shift left testing approach and regularly repeating it. This is especially applicable before marketing campaigns, infrastructural changes and major releases.
Who’s responsible for conducting performance testing?
Performance testing involves DevOps Engineers, app developers, product owners, and site reliability engineers (SREs), making it a cross-functional responsibility. However, QA teams lead performance testing cycles.
What are the most significant risks if an organization doesn’t conduct performance testing?
If you skip performance testing, you can leave security loopholes, cause unplanned downtime, degrade the quality of user experiences, lose revenue, and damage a brand’s reputation.
