A system can work perfectly under normal conditions and fail completely when demand increases. This reality affects companies of all sizes and segments: an e-commerce that crashes during Black Friday, a banking app that becomes unavailable on payday, or a streaming platform that can’t handle the spike in access during a highly anticipated series launch.
Performance testing exists precisely to prevent these situations. It evaluates how a system behaves under different usage conditions, identifying bottlenecks before users encounter them. According to Google’s 2024 Web Performance Report, 88% of users are less likely to return to a site after a bad experience, which demonstrates the direct impact of performance on retention and business.
This article presents the four main types of performance testing, explains when to use each one, and shows how to automate them efficiently. If you’re a QA professional or looking to improve your testing process, it’s worth the read.
What is Performance Testing
Performance testing evaluates a system’s ability to respond adequately under certain usage conditions. While functional testing verifies if the system works, performance testing verifies if it can handle the load.
The difference is simple: a functional test confirms that you can log in. A performance test confirms that a thousand people can log in simultaneously without the system slowing down or crashing.
The main metrics evaluated are:
- Response time: how long the system takes to process a request
- Throughput: how many requests the system processes per second
- Resource usage: how much CPU, memory, and network the system consumes
- Error rate: how many requests fail under a given load
With TestBooster.ai, you can create and automate performance tests using natural language, without needing to write complex code. The platform allows you to schedule recurring executions and centralize results in dashboards that translate technical metrics into business insights.
Main Types of Performance Testing
Performance tests are divided into types according to their objective. The most common types are:
Load Testing
Load testing evaluates how the system behaves under the expected volume of simultaneous users. It’s the test that simulates the application’s normal day-to-day operation, with typical usage load.
The objective is to validate whether the system can meet planned demands without degrading user experience. For example: if you expect 500 simultaneous users during business hours, the load test simulates exactly those 500 users accessing the system.
- When to use: before launches, after significant system changes, or to validate whether the current infrastructure supports expected growth.
- Expected results: the test identifies performance bottlenecks, components that consume excessive resources, and points where response time exceeds acceptable limits. Fixing these problems before real users encounter them prevents lost sales and dissatisfaction.
Stress Testing
Stress testing pushes the system to its limit and beyond. While load testing simulates normal conditions, stress testing progressively increases the load until the system fails.
The objective is to discover the breaking point: how many simultaneous users can the system really handle? How does it behave when overloaded? Does it recover on its own or require manual intervention?
- When to use: to plan scalability, understand safety margins, and prepare the team for critical scenarios.
- Expected results: the test reveals the exact point of failure, which component breaks first (database, application server, load balancer), and whether the system automatically recovers when the load decreases. This information is essential for infrastructure decisions and capacity planning.
Volume Testing
Volume testing evaluates the impact of large amounts of data on system performance. Instead of focusing on the number of users, this test focuses on the volume of information the system needs to process and store.
The objective is to verify whether the system remains performant when dealing with millions of records, large files, or massive read and write operations.
- When to use: in systems that continuously accumulate data, before large data migrations, or when planning storage architecture.
- Expected results: the test identifies performance degradation related to data growth, database indexing problems, and the need for archiving or partitioning strategies. According to the 2024 Continuous Performance Testing Benchmark, 40% of critical system problems only appear after prolonged periods of operation, which reinforces the importance of testing scenarios with large volumes.
Spike Testing
Spike testing simulates sudden and unexpected increases in load. Unlike stress testing, which increases load gradually, spike testing drastically elevates the number of users within seconds.
The objective is to evaluate how the system reacts to sudden spikes: can it absorb the impact? Does it degrade gracefully or simply crash?
- When to use: in systems that may have unpredictable demand (e-commerce during flash sales, news portals, entertainment platforms during live events).
- Expected results: the test shows whether the system maintains stability during spikes, how long it takes to recover, and whether users can access critical functionalities even under extreme pressure. Well-prepared systems degrade in a controlled manner, keeping essential functionalities available.
Quick Comparison
| Test Type | When to Use | What It Evaluates |
| Load | Before launches and to validate planned capacity | Behavior under expected normal user volume |
| Stress | To understand real system limits | Breaking point and recovery after overload |
| Volume | In systems that continuously accumulate data | Impact of large amounts of data on performance |
| Spike | To prepare systems with unpredictable demand | Reaction to sudden load increases |
How to choose the right test
Choosing the test type depends on business context and the risks involved. Critical systems generally need all types, while simpler applications can prioritize load and spike testing.
You can combine different tests for more comprehensive scenarios. For example: run a volume test to ensure the database handles millions of records, then a load test to validate that the system remains fast with that data volume.
Best practices in performance testing
Some precautions ensure more reliable results:
- Test in environments that simulate production: running tests in environments very different from the actual infrastructure generates results that don’t reflect production behavior.
- Monitor infrastructure metrics: beyond response time, track CPU, memory, disk, and network usage. Often the bottleneck is in the infrastructure, not the code.
- Repeat tests to ensure consistency: a single test can have variations. Execute multiple times to confirm that results are stable.
- Automate to run regularly: one-time tests help, but recurring tests capture degradations over time. Schedule automatic executions after deployments or at specific times.
- Document and compare results: maintain a history of executions to identify when performance began to degrade and correlate with system changes.
Test performance with TestBooster.ai
TestBooster.ai functions as a quality hub, executing not only performance tests but the entire company’s quality strategy on a single platform.
- Natural language creation: describe what you want to test in natural language, without needing to write complex code.
- Automatic scheduling: configure tests to run every night, after each deployment, or before important events. You don’t need to remember to execute them manually.
- Intuitive dashboards: results from all tests (functional, API, performance) appear in centralized reports that translate technical metrics into insights for managers and executives.
- Integration with functional and API tests: beyond performance, the platform allows testing functionalities and integrations, offering a complete view of quality.
- Holistic view: connect functional quality and performance in one place. See not only if the system works, but if it works well under pressure.
Discover TestBooster.ai and see how to automate your complete software testing strategy. Get in touch with our team.
