What Is Software Performance Testing?

Use Agentic AI to generate smarter API tests. In minutes. Learn how >>

Overview

Like a check engine light on system performance, load and performance testing tools test under regular and extreme loads to find any faults and ensure your application performs as expected under diverse operating conditions.

What Is Load & Performance Testing?

Companies are under pressure to deliver an excellent customer experience. You must ensure your software responds reliably, correctly, and consistently, regardless of usage load.

Performance testing assesses how an application behaves under specific conditions and analyzes the results so you can identify and address any bottlenecks or blockages that prevent smooth operation.

A load and performance testing strategy prepares your applications for unexpected demand. Load and performance testing tools ensure your system handles sudden bursts in traffic and delivers a superior user experience.

Why Is Load & Performance Testing Needed?

Most days, your application functions under normal conditions. But once in a while, the load peaks, or the performance of a dependent system dips.

Many organizations only perform tests against a subset of the potential conditions that users could experience. Agile teams recognize that they need to run performance tests against a variety of workloads to meet user expectations.

Performance and load testing tools are a check engine light on system performance. They test under regular and extreme loads to find any faults and ensure your application can take the heat. Test automation makes it easier and faster to run a combination of performance testing steps in parallel.

Benefits of Performance Testing

Performance testing provides numerous benefits. Here are some of them.

Verifies the Core Features of Your Software

A strong software foundation is key to software success. Ascertaining the performance of the software’s basic functions enables management to make essential decisions based on the success or failure of the software’s performance. Conduct performance testing as a critical subset of your overall application testing strategy.

Measures the Accuracy, Speed, & Stability of Your Software

Each test measures a particular aspect of the performance and generates testing results for analysis. This allows you to view the vital parts of the software under normal and peak resource usage, giving you data on how the program handles scalability. The data, gauged against known, baseline standards, allows developers to make intelligent decisions about needed changes.

Verifies Operation Under Stress

Software testing for load and performance often predicts behavior and can help DevOps plan for unexpected or unusual circumstances. It is important to find out early whether your organization can manage scalability. Using testing methodologies sooner rather than later helps testers maximize performance and hone capacity to manage demand.

Ensures User Satisfaction

A favorable first impression of potential customers is crucial. Research indicates almost half of all users rely on mobile and web service applications to load in two seconds or less. Your software has this tiny timeframe to make a good show to potential users before they sign off. Load time can accelerate as network capacities and connection speeds rise.

When you measure performance, it allows you to monitor how customers respond to the software. This provides an advantage because you can identify critical issues—before your customers do.

Identifies Inconsistencies & Helps Resolve Problems

Performance testing provides a cushion for developers before they release the software. Problems often magnify after release; this testing plan irons out any issues. It’s important that teams monitor performance testing across all business divisions, so decision-makers have open channels for communicating with IT, thus making sure software performance matches business strategies.

An adequate staging environment for testing allows more time for improving technology as opposed to fighting fires in avoidable, pre-existing issues. These issues can grow rapidly and simultaneously become expensive after the software is in the customer’s hands.

Types of Performance Tests

Load Testing

These tests simulate the approximate number of users that may use the application. When reproducing representative expected load and usage conditions, centered on response times, load tests help pinpoint possible bottlenecks. The test enables developers to assess whether applications meet expectations for responsiveness and stability based on the load generated.

Volume Testing

Also known as capacity testing. When testers use load testing, they populate large amounts of data in a database and monitor the behavior of the system overall. The purpose is to observe the performance of the software’s applications under various database volumes.

Stress Testing

This entails testers testing the application under extreme workflows, which enables the testers to observe how the application handles large volumes of data processing or high traffic. The idea is to identify the application’s breaking point.

Endurance Testing

Also known as soak testing. Testers use this test to ensure that the software can handle the specified load over extended amounts of time. This can also check to ensure that memory leak issues don’t occur after the application runs for long periods.

Spike Tests

Testers employing these testing measures seek to understand the implications to a system’s operations when activities on the system are above the norm. In contrast to stress tests, these tests consider the complexity of actions the system performs, and the number of users involved, which then yields an increase in the number of business processes the system generates.

Scalability Testing

Bottlenecks occur when there isn’t enough capacity to handle the workload. Testers use scalability testing processes to discover the software application’s ability to scale up to handle user load increases. This helps them plan for any addition to your software system’s capacity.

Performance Testing Best Practices

Here are some best practices for performance monitoring and testing.

Thoroughly understand the application
What are its capabilities, intended use cases, and the situations under which it is supposed to thrive?
Understand different test types
Thoroughly understand the different types of tests and how and when to use them.
Consider all possible environments and devices
Take into consideration all possible product environments and devices, including browsers and operating systems.
Use DevOps
Involving DevOps brings together the test plan and development teams. This helps to identify errors quicker and resolve them sooner.
Set up environment and tools
Establish your test environment and tools for validating application performance. Isolate it from the user acceptance testing (UAT).

Define criteria
Define base operating criteria for success and extend them to extreme conditions under which operation should be maintained.
Design performance tests
Leverage API testing scenarios for performance testing and customize them as needed.
Configure, perform, collect
Configure the performance testing environment, perform the tests, and collect the results for analysis.
Review, identify, retest
Review the results, identify performance areas to address, and repeat the process (retest) as needed.
Triage the performance problems
Involve all concerned parties: testers, developers, and operations people.

How to Get Started & Examples

The application you build has both functional and nonfunctional testing requirements. Load and performance testing best practices are a necessary aspect of software development but especially so when load can vary with sudden swings in demand and network traffic.

Get a head start by using your existing API test scenarios as a basis for performance testing.

Test Case Examples

Verify that the response time is 4 seconds or less when 1,000 users or virtual users simultaneously use the website.
Verify that the application’s response time under load is in a range that is acceptable during slow network connectivity.
Observe the maximum number of users the application can support without crashing.
Record the database’s execution time when it reads or writes 500 records at a time.
Check the database and application server’s memory and CPU usages during peak load situations.
Check the application’s response time during low, moderate, and high load conditions.

Using these test cases in actual performance testing scenarios, you will find that specific numbers replace vague terms such as “heavy load” and “acceptable range.” Testers set the performance criteria numbers taking into consideration the application’s technical landscape and the project’s business requirements.

When Should Teams Apply Performance Testing?

Modern software development emphasizes building performance into applications from the start, rather than addressing it later in the life cycle. In other words, apply shift-left principles to performance testing. Identifying and fixing performance issues early prevents costly redesigns down the line. To support Agile practices and frequent releases, automating performance tests and running them continuously is crucial.

While it’s impractical to run all performance tests during continuous integration (CI) builds, a balanced approach works best. Run essential performance tests during CI builds, and schedule more intensive tests less frequently, based on time and resource availability. The following table shows some examples.

Test Frequency		Test Type
Every CI build		Smoke, unit performance
Daily or nightly		Load
Once a week		Endurance, stress

By using a shift-left performance testing process like this, teams can strike the perfect balance—catching performance regressions quickly while managing resource usage efficiently.

Accelerating Performance Testing With Service Virtualization

Service virtualization simplifies and accelerates performance testing by reducing the complexity and overhead associated with traditional real-world performance test environments. Leveraging automation techniques results in fewer operating expenses and reduced lab costs. Development teams test earlier and more thoroughly with virtualization.

Service virtualization simulates services, such as APIs, systems owned by other teams, or systems under development, that must be available for testing but are often tied up with production use. To simplify the process of creating the virtual assets that power service virtualization, tools like Parasoft Virtualize create proxies.

Proxies act as intermediaries between applications and backend systems, monitoring communication with otherwise invisible dependencies. This allows service virtualization tools to analyze traffic, link requests to responses, and interpret data automatically. By applying service definitions in a virtualized environment, virtual assets can be created faster and more accurately.

Automation simplifies building and configuring virtual services to support test cases, making deployment seamless within test environments. Service virtualization reduces the cost and complexity of creating performance testbeds. Service virtualization tools also provide: