Without some form of performance testing, system performance will likely be affected by slow response times and inconsistent experiences, resulting in an overall poor user experience.
Determining if the developed system meets velocity, responsiveness, and resilience prerequisites while under workloads will help ensure a more favorable user experience.
Performance requirements must be identified and tested; typical parameters often include processing speed, data transfer rates, network bandwidth, workload efficiency, and reliability.
Tests for each performance parameter can be executed in a separate lab setting, or some cases, directly in production environments.
Why use performance testing?
Organizations can use performance testing as a diagnostic tool to locate computing or communications bottlenecks within an application's infrastructure.
Bottlenecks are a single piece or component within a system's overall function that negatively affects performance. The origin, conditions, or location of a software-related performance problem can be identified with performance testing, highlighting the specific point in the system an application might fail or lag.
An organization can also use this testing form to ensure it is prepared for predictable significant events, such as holiday sales for online stores.
Performance testing allows teams to compare two or more devices or platforms by verifying that a system meets project requirements and the specifications claimed by its manufacturer or vendor.
Performance testing metrics
Key performance indicators (KPIs) and metrics help organizations evaluate the current performance of their systems and applications. These metrics usually include the following:
- Throughput refers to the number of units of information a system processes over a predetermined time window.
- Memory translates to working storage space available to a processor, thread, or workload.
- Response time or latency refers to the time between a request entered by the user and the start of a system's response.
- Bandwidth involves the volume of data transported between workloads, usually across a network or between on-premises and cloud environments.
- CPU interrupts per second translate to the number of hardware interrupts a process is subject to per second.
How to conduct performance testing
Because development teams can conduct performance testing with different metrics, the actual process can vary greatly.
However, a generic process may look like this:
- Identify the testing environment, including test and production environments and testing tools.
- Identify and define acceptable performance criteria that should include performance goals and constraints for metrics.
- Plan performance tests. Test all possible use cases. Build test cases around performance metrics.
- Configure and implement a test design environment. Manage resources to fulfill the prerequisites for the test environment, then begin to implement it.
- Run the tests. The test also should be monitored.
- Analyze and retest. Apply analytics to the results, and after any changes or fine-tuning, retest to see any substantial changes in performance.
As executing test suites repeatedly can be cost-intensive, organizations must find testing tools that can automate the performance testing process while assuring development or QA teams that testing environments do not change between tests.
Types of performance testing
Companies can use different types of testing methods to determine performance. However, the two main performance testing techniques are load testing and stress testing.
Load Testing
Load testing helps developers comprehend the behavior of a system or application under a typical load value. The team simulates the expected number of concurrent users and transactions in the load-testing process to verify predicted response times and discover bottlenecks.
This test allows developers to determine how many simultaneous users an application can manage before it goes live. Furthermore, development teams can load test-specific features of an application, such as user creation forms, login portals, or checkout carts.
Continuous integration (CI) processes include load-testing phases where changes to a code base are immediately tested using automation tools, such as Jenkins.
Stress Testing
Stress testing places an application under higher-than-expected traffic so developers can see how it behaves above the threshold of its predicted capacity limits, enabling software teams to understand a workload's scalability.
Stress tests work by putting a strain on hardware resources to identify the possible breaking point of an application based on resource depletion. Resources could include CPUs, memory, hard disks, and solid-state drives.
System strain doesn't only affect performance; systems under heavy stress are also susceptible to memory shortages, data corruption, and security issues.
Stress tests can also provide information regarding the time it takes to restore the system to its expected performance, indicating how long it takes for Key Performance Indicators to return to typical operational levels after an event.
Stress tests can occur before a system goes live or in live production environments, where they are usually referred to as "chaos engineering" and are conducted with specialized tools.
Before significant events such as Black Friday for e-commerce applications, organizations might conduct stress tests simulating the expected load spike using the same tools and techniques.
Soak Testing
Often called endurance testing, this test simulates a constantly growing number of users to determine a system's long-term sustainability while test engineers monitor KPIs and check for failures. These tests also analyze system throughput and responsiveness after continuous use to determine if these metrics show any changes compared to the test's beginning.
Spike testing
This one evaluates the performance of a system under a rapid and substantial growth of end users, helping determine if a system can frequently endure an abrupt, intense workload increase over a short span. IT teams typically perform spike tests before a significant event in which a system will presumably experience higher-than-average traffic volumes.
Scalability testing
This test measures software's ability to scale up or down performance attributes in response to increased demand from end users.
Capacity testing
Similar to stress tests in regards to testing traffic loads based on the number of users, it focuses on whether an application or environment can support the amount of traffic it was explicitly designed to handle.
What is cloud-based performance testing?
Performance testing can also be carried out in the cloud, testing applications at a larger scale while maintaining the cost benefits of being in the cloud.
At first, organizations considered shifting performance testing to cloud services would facilitate the performance testing process while making it easier to scale.
However, when organizations began implementing this, they found different issues in cloud-based performance testing, as teams need in-depth, white-box knowledge on the cloud provider's side.
When moving an application from an on-premises environment to the cloud, teams may assume that the application will work identically to on-premises once it's implemented in the cloud, thus minimizing testing and QA and proceeding with quicker rollout processes.
With the application being tested on different hardware, testing may not be as accurate if hosted on-premises.
Software companies should then coordinate development and operations teams to evaluate security gaps, conduct load testing, assess scalability, and focus on user experience while mapping servers, endpoints, ports, and paths. Inter-application communication can be one of the most significant issues in moving an app to the cloud.
Cloud environments typically enforce higher security restrictions on internal communications than on-premises environments; this means that teams should build and maintain a complete map of servers, ports, and communication paths the application uses before moving the infrastructure to the cloud.
Examples of performance testings
Here are examples of real-world performance testing scenarios:
- Load Testing for E-commerce Platforms: During Black Friday sales, an e-commerce site like Amazon performs load testing to ensure it can handle millions of users making purchases simultaneously without crashing.
- Stress Testing for Social Media Apps: Platforms like Twitter conduct stress tests to simulate extreme situations, such as viral trends or global events, to ensure the app remains stable under intense user activity.
- Scalability Testing for Cloud Services: Netflix runs scalability testing to ensure its streaming service can handle the growing number of users, especially during peak hours, without performance drops.
- Endurance Testing for Banking Systems: Banks like Chase use endurance testing to evaluate how their systems handle long-term usage, ensuring no slowdowns or memory leaks in services like online transactions.
- Spike Testing for Ticketing Websites: Ticketing platforms such as Ticketmaster perform spike testing to simulate the sudden surge in traffic when tickets for a popular concert go on sale, ensuring the system can handle the load without crashing.
These examples illustrate how different industries use performance testing to maintain smooth, efficient operations under various conditions.
Performance testing challenges
These aspects make performance testing more challenging:
- Many tools only have support for web applications.
- Free versions of tools often work less effectively and efficiently than paid variants, while some paid tools are expensive.
- Tools may have limited compatibility.
- Complex applications can be challenging to test for specific tools.
- Monitoring resources is crucial; teams should watch for performance problems like CPU, memory, network utilization, disk usage, and operating system limitations.
Performance testing tools
Depending on their needs and preferences, IT teams can employ various performance test tools, including:
- JMeter, an Apache performance testing tool, can execute load tests on web and application services while providing flexibility in load testing and covering areas such as logic controllers, graphs, timers, and functions. The platform also supports integrated development environments (IDEs) for test recording on browsers or web applications and CLI mode for load testing Java-based operating systems.
- LoadRunner tests and measures the performance of applications under load and can simulate thousands of end users while recording test output for further analysis. As part of the simulation, the software emulates the interaction between application components and end-user actions, similar to key clicks or mouse movements. LoadRunner also includes versions geared toward cloud use.
- NeoLoad, developed by Neotys, provides load and stress tests for web and mobile applications and is specifically designed to test apps before release for DevOps and continuous delivery. An IT team can use the program to monitor web, database, and application servers. NeoLoad can simulate millions of users and performs tests in-house or via the cloud.
Five common performance testing mistakes
Some usual mistakes can lead teams to less-than-reliable results when performance testing:
- Development teams and software companies often need to allocate more time for testing.
- Developers are not involved in performance testing.
- Testing environments are too dissimilar to production systems.
- Applications need to be sufficiently tuned for testing.
- There is no troubleshooting plan for diagnosing issues and errors found during testing.
Performance Testing Misconceptions
These common misconceptions about Performance Testing can lead to inconsistent results or frequent failures to follow performance testing best practices.
Teams must do performance testing at the end of software development.
Anticipating and solving performance issues must be an early part of software development and is vital when working with Continuous Integration. Implementing solutions earlier on the project schedule will be more cost-effective than developing and rolling out significant fixes at the end of software development.
More hardware can fix performance issues.
Adding processors, servers, or memory in the Cloud computing age is very simple. Still, it also increases the cost without necessarily solving issues, while more efficient software will run better and prevent potential problems even when the hardware is expanded or upgraded.
The testing environment is close enough.
Differences between the elements, configurations, and modules can significantly affect system performance, which makes conducting performance testing in a test environment as similar as possible to the production environment a best practice.
While it may not be possible to test the application in the exact production environment, teams should strive to match hardware components, operating systems, additional applications used on the system, database engines, and database configurations.
What works now will work across the board.
Be careful about extrapolating results. Don't take a reduced set of performance testing results and assume the application will behave the same when conditions vary.
The opposite is true, only inferring minimum performance and requirements based on load testing. Development teams should verify all assumptions through extended performance testing.
One performance testing scenario is enough.
Even if available resources limit the amount of testing that can occur, only some issues can be detected in the same performance testing scenario.
Unexpected problems can arise outside of well-planned and well-designed performance testing, which makes monitoring production environments crucial for detecting unpredicted performance issues.
Testing individual parts is equal to testing the whole system.
Isolating functions for performance testing is essential, but individual component testing differs from system-wide assessment and evaluation.
The whole system is more than the sum of its parts, and interaction between other modules is inherent to software development.
While it may not be feasible to test all system functionalities, a complete-as-possible test must be designed using the resources available, with particular attention to aspects of the application that need to be tested.
What works for others will work for us.
If a specific group of users experiences problems or performance issues, only assume the same will be valid for some users.
Employ performance testing to ensure the platform and configurations work as intended.
The software development team is too experienced to need performance testing.
Lack of experience is only one of the reasons behind performance issues; even development teams who have previously created issue-free software make unexpected mistakes as an ever-growing number of variables come into play.
A whole load test tells everything.
Starting at a lower load and scaling up may seem unnecessarily slow, but it produces more accessible results that are more efficient to troubleshoot.
While it may be tempting to test the whole system at full load to find all the performance issues at once, that kind of test reveals so many performance issues that it takes a lot of work to focus on individual solutions.
Wrapping up
Performance testing is an affluent area with a multitude of concepts and directions for development teams, as it not only tracks the work of the loaded system as a whole but also helps optimize the operation of its modules.
To achieve the best results, we're always ready to provide you with high-quality performance testing services to prevent failures in any software product's work and ensure the stable functioning of all its components.
Ready to start your company's journey in performance testing? Don't hesitate to drop us a line here!
