Performance Testing: Types, Metrics and How to

Delivering high-quality software requires more than just a bug-free application and it demands efficient performance across all functionalities. Performance testing ensures that software can handle real-world usage, making it a critical step in the development process.

This article covers the fundamentals of performance testing, including its types, tools, examples, and more.

What is Performance Testing?

Performance testing is a type of software testing that evaluates how an application performs under various conditions, such as different user loads, data volumes, and network environments.

The main goal is to identify performance issues and ensure the application meets speed, stability, scalability, and responsiveness requirements by simulating various conditions.

For example, assume you are developing a gaming app. Before releasing it, you must run a performance test to ensure that it operates at expected quality levels as it is intended, irrespective of the hardware or software environment it is in. Within the test, the app must:

• Load and play at high speed
• Render all visuals and text as intended
• Supports multiple players – if it is a multiplayer game
• Uses minimal memory

Why should you do Performance Testing

Performance testing, also known as application performance testing, ensures that software meets specific performance requirements.

For example, it can verify whether an application can handle thousands of users logging in or performing various actions simultaneously.

This process enables to identify and fix errors and ensures the app remains stable under various conditions. Performance testing improves reliability, reduce bugs, and support your app’s market claims.

Performance Testing Example Scenarios

Assume an example of testing an e-commerce app’s performance. It is important to evaluate how well it handles peak traffic and user demands.

The below table demonstrates an example of how load, stress, and other performance testing types ensure stability and optimize user experience.

When should you conduct Performance Testing?

Performance testing should be conducted at various stages of the software development life cycle.

• Before major releases or significant updates
• When adding new features that could affect performance
• Regularly in environments that mirror production
• Prior to anticipated high traffic or user growth
• Early in development to catch potential issues quickly

Types of Performance Testing

There are several types of software performance testing used to determine the readiness of a system:

• Load Testing: Measures system performance under varying load levels, AKA the number of simultaneous users running transactions. Load Testing primarily monitors response time and system stability while the numbers of users or operations increase on certain software.
• Stress Testing: Also known as fatigue testing, it measures how the system performs in abnormal user conditions. It is used to determine the limit at which the software actually breaks and malfunctions.
• Spike Testing: Repeatedly evaluates how the software performs when subjected to high traffic and usage levels in short periods.
• Endurance Testing: Also known as soak testing, it evaluates how the software will perform under normal loads over a long period of time. The main goal here is to detect common problems like memory leaks which can impair system performance.
• Scalability Testing: Determines if the software is handling increasing loads at a steady pace. This is done by incrementally adding to the volume of the load while monitoring how the system responds. It is also possible to keep the load at a steady level while playing with other variations like memory, bandwidth, etc.
• Volume Testing: Also known as flood testing because it involves following the system with data to check if it can be overwhelmed at any point and what fail-safes are necessary.

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What to measure during Performance Testing?

The following metrics or Key Performance Indicators (KPIs) help evaluate the results of system performance testing:

• Memory: The storage space available and/or used by a system while processing data and executing an action.
• Latency/Response Time: The duration of time that passes between when a user enters a request and when the system starts to respond to that request.
• Throughput: The number of data units processed by the system over a certain duration.
• Bandwidth: The amount of data per second capable of moving across one or more networks.
• CPU interrupts per second: The number of hardware interrupts experienced by a system while it processes data.
• Speed: The time in which a web page loads with all elements – text, video, images, etc.
• Resource Utilization: CPU, memory, disk, and network usage.
• Scalability: How well the application performs as load increases.
• Concurrent Users: The maximum number of simultaneous users, the app can support.

Quick Tip: Run website speed tests on SpeedLab for free. Simply enter the URL, and get a detailed report on your website speed as it runs on real browsers, devices, and operating systems.

Performance Testing Process

The performance testing process ensures software operates efficiently under various conditions. Here’s a streamlined approach:

1. Identify The Right Test Environment and Tools

Start with identifying a test environment that accurately replicates the intended production environment.

Instead of dealing with the many inadequacies of emulators and simulators, testers get accurate results using a real device cloud that offers real devices, browsers, and operating systems on-demand for instant testing.

The easiest way to test in real user conditions is to run performance tests on real browsers and devices. Instead of dealing with the many inadequacies of emulators and simulators, testers are better off using a real device cloud that offers real devices, browsers, and operating systems on-demand for instant testing.

By running tests on a real device cloud, QAs can ensure that they are getting accurate results every time. Comprehensive and error-free testing ensures that no major bugs pass undetected into production, thus enabling software to offer the highest possible levels of user experience.

Whether manual testing or automated Selenium testing, real devices are non-negotiable in the testing equation. Opt for a cloud-based testing infrastructure in the absence of an in-house device lab (regularly updated with new devices and maintains each of them at the highest levels of functionality). Start running tests on 2000+ real browsers and devices on BrowserStack’s real device cloud. Run parallel tests on a Cloud Selenium Grid to get faster results without compromising on accuracy. Detect bugs before users do by testing software in real-world circumstances.

Users can sign up, select a device-browser-OS combination, and start testing for free. They can simulate user conditions such as low network and battery, changes in location (both local and global changes), and viewport sizes and screen resolutions.

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2. Define Acceptable Performance Levels

Establish the goals and numbers that will indicate the success of performance tests. The easiest way to do this is to refer to project specifications and expectations from the software. Accordingly, testers can determine test metrics, benchmarks, and thresholds to define acceptable system performance.

3. Create test scenarios

Craft tests to cover a number of scenarios in which the software’s performance will be challenged by real-world usage. Try to create a few test scenarios that accommodate multiple use cases. If possible, automate tests for quick and error-free results.

4. Prepare the Test Environment and Tools

This requires Configuration Management, which is essential to programming the environment with relevant variables before tests are executed. Ensure that the testers have all necessary tools, frameworks, integrations, etc. at hand.

5. Run the Performance Tests

Execute the previous constructed test suited. Adopt parallel testing to run tests simultaneously so as to save time without compromising the accuracy of results.

6. Debug and Re-Test

Once test results are in and bugs have been identified, share them with the entire team. Consolidate the bugs and send them to the right developers to be fixed. If possible, QAs can fix a few basic bugs in order to save time and teamwide back-and-worth.

When the performance shortcomings have been resolved, re-run the test to confirm that the code is clean and the software performs optimally.

Top Performance Testing Tools

With the increasing importance of performance testing in software development, selecting the right tool is essential for effective testing.

Here are the top 20 performance testing tools, each offering unique capabilities to help you ensure your application’s stability, speed, and scalability under various conditions.

• Apache JMeter: Open-source tool for load and performance testing on web applications and APIs.
• Gatling: Scalable, open-source tool for testing web apps’ performance using asynchronous I/O.
• BrowserStack App Performance: Real-device testing for mobile apps, evaluating speed and stability.
• LoadRunner: Enterprise-grade tool for simulating user traffic across various applications.
• BlazeMeter: Cloud-based platform that supports JMeter, Gatling, and Selenium for load testing.
• Locust: Python-based, open-source tool for measuring web applications’ performance under load.
• K6: Developer-centric tool that focuses on load testing for APIs and web applications.
• Apache Bench: Lightweight tool for quick benchmarking of web servers.
• NeoLoad: Enterprise tool for load testing complex applications with advanced scenario capabilities.
• Tsung: Distributed tool for stress-testing web applications and network protocols.
• WebLOAD: Enterprise solution for load testing web and mobile apps with complex scenarios.
• Selenium: Common tool for automating browser tests across web applications.
• LoadNinja: A cloud platform using real browsers for accurate load testing results.
• Dynatrace: Has AI monitoring for real-time application performance insights.
• Artillery: Lightweight tool for load testing HTTP-based services and APIs.
• New Relic: Comprehensive observability platform for application and infrastructure performance.
• AppDynamics: APM solution providing end-to-end visibility of application performance.
• Sitespeed.io: Open-source tool focusing on web performance optimization.
• Puppeteer WebPerf: Automation tool for performance testing on Chrome-based browsers.
• Siege: An Open-source benchmarking tool for testing web server response under concurrent traffic.

Success Metrics for Performance Testing

Success metrics for performance testing assess the app’s readiness and identify areas for optimization.

Here are some of the key success metrics to consider:

• Response Time: It measures how fast the application responds to user requests.
• Throughput: It determines the number of transactions processed within a given timeframe.
• Resource Utilization: It also tracks CPU, memory, disk, and network usage to assess efficiency.
• Error Rate: It monitors the frequency of errors during testing to measure app stability.
• Scalability: It tests the app’s ability to handle increasing user loads and data volume.
• Concurrent User Load: It evaluates the number of users the app can support simultaneously.
• Peak Performance: It measures maximum performance under heavy load conditions.

Challenges in Performance Testing

Here are some common challenges in performance testing:

• Realistic Environment Setup: It’s often costly and complex to fully recreate production conditions for testing.
• Crafting Test Scenarios: Creating the exact user scenarios and load patterns require precise planning and expertise.
• Resource Demands: Performance tests require high investments in hardware, software, and time.
• Data Consistency: Ensuring consistent and relevant data across various tests can be challenging.
• Tool Integration: Aligning testing tools with diverse tech stacks can bring compatibility challenges.
• Result Analysis: It takes experience to analyze complex data and pinpoint root causes effectively.

Best Practices for Performance Testing

Follow these best practices when running a system performance test:

• Start at Unit Test Level: Do not wait to run performance tests until the code reaches the integration stage. This is a DevOps-aligned practice, part of the Shift Left Testing approach. This reduces the chances of encountering errors in the latter stages.
• Remember that it is about the User: The intention of these tests is to create software that users can use effectively. For example, when running tests, don’t just focus on server response; think of why speed matters to the user. Before setting metrics, do some research on user expectations, behavior, attention spans, etc.
• Create Realistic Tests: Instead of overloading servers with thousands of users, simulate real-world traffic that includes a variety of devices, browsers, and operating systems.
  Use tools like BrowserStack to test on actual device-browser combinations that match your audience. Also, start tests under existing load conditions, as real-world systems rarely operate from a zero-load state.
• Set Clear, Measurable Goals: Define specific performance goals based on user expectations and business requirements. It includes response times, throughput, and acceptable error rates.
• Automate Where Possible: Make use of automation tools to run performance tests, especially in continuous integration and continuous delivery (CI/CD) pipelines.
• Monitor in Production: Use performance monitoring tools in the live environment to catch issues that might not appear in test environments. This ensures consistent performance.
• Analyze and Optimize: Continuously analyze test results and implement solutions to optimize, then re-test to confirm improvements.
• Prepare for Scalability: Test with different load levels to ensure the app can scale as needed, especially if user numbers are expected to grow rapidly.

Performance Testing vs Continuous Performance Testing

Conventional performance testing checks an app’s performance at certain specific points. On the other hand, continuous performance testing incorporates checks at each and every stage of development. This ensures ongoing monitoring and optimization.

Here is a quick comparison:

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Performance Engineering vs Performance Testing

While this article has covered much of performance testing, it is important to distinguish it from performance engineering.

Here are the main differences between performance testing and performance engineering.

Conclusion

Since every software, website, or app, aims to serve and delight users, Performance Tests are essential in any software development scenario.

By running tests on real devices, QAs can ensure they get accurate results every time. Complete and error-free testing ensures that no major bugs pass undetected into production, thus enabling software to offer the highest possible levels of user experience.

Whether you opt for manual testing or automated testing, real devices are non-negotiable. Opt for a cloud-based testing infrastructure like BrowserStack (regularly updated with new devices and maintains each of them at the highest levels of functionality).

Start testing on 3500+ real browsers and devices on BrowserStack’s real device cloud. You can simulate user conditions such as low network and battery, changes in location (both local and global changes), viewport sizes, and screen resolutions in real-world conditions.

With BrowserStack, you can also run parallel tests on a Cloud Selenium Grid to get faster results without compromising accuracy.

Try Real Device Testing for Free