Performance testing, best practices, metrics & more

What is performance testing?

Performance testing is the practice of evaluating how a system performs in terms of responsiveness and stability under a particular workload. Performance tests are typically executed to examine speed, robustness, reliability, and application size. The process incorporates performance indicators such as:

• Browser, page, and network response times
• Server request processing times
• Acceptable concurrent user volumes
• Processor memory consumption; number and type of errors that might be encountered with app

Why is system performance testing important?

Performance testing will help you ensure that your software meets the expected levels of service and provides a positive user experience. Applications released to the public in the absence of testing could suffer from a damaged brand reputation, in some cases irrevocably so. Test results will highlight improvements you should make relative to speed, stability, and scalability before your app goes into production.

The adoption, success, and productivity of applications depends directly on the proper implementation of performance testing. While resolving production performance problems can be extremely expensive, the use of a continuous optimization performance testing strategy is key to the success of an effective overarching digital strategy.

The use of a continuous optimization performance testing strategy is key to the success of an effective overarching digital strategy.

When is the right time to conduct performance testing?

Whether it’s for web or mobile applications, the life cycle of an application includes two phases: development and deployment. In each case, operational teams expose the application to end users of the product architecture during testing. Development performance tests focus on components (web services, microservices, APIs).

The earlier the components of an application are tested, the sooner an anomaly can be detected and, usually, the lower the cost of rectification. As the application starts to take shape, performance tests should become more and more extensive. In some cases, they may be carried out during deployment (for example, when it’s difficult or expensive to replicate a production environment in the development lab).

Performance testing types

Different types of performance testing are conducted throughout the development lifecycle to ensure that the application meets performance requirements and user expectations. Here are the primary types of performance testing:

• Load tests simulate the number of virtual users who might use an application. In reproducing realistic usage and load conditions based on response times, this test can help identify potential bottlenecks. It also enables you to understand whether it’s necessary to adjust the size of an application’s architecture.
• Unit tests simulate the transactional activity of a functional test campaign; their goal is to isolate transactions that could disrupt the system.
• Stress tests evaluate the behavior of systems during peak activity. These tests significantly and continuously increase the number of users during the testing period.
• Soak tests increase the number of concurrent users and monitor the behavior of the system over a more extended period. The objective is to observe whether intense and sustained activity over time shows a potential drop in performance levels, making excessive demands on the resources of the system.
• Spike tests seek to uncover implications to the system operations when activity levels are above average. Unlike stress testing, spike testing considers the number of users and the complexity of actions performed (hence the increase in several business processes generated).
• Volume tests focus on assessing the system’s ability to handle a large volume of data. They evaluate how the application performs when subjected to a significant amount of data input, such as large databases or files, ensuring that performance does not degrade as data volume increases.
• Endurance Tests: Endurance tests assess the system’s stability and performance over an extended period under a consistent workload. They aim to uncover any memory leaks, performance degradation, or other issues that may occur when the application runs continuously for hours, days, or even weeks.
• Compatibility tests s assess the application’s performance across different environments, platforms, devices, and configurations. They ensure that the application performs optimally on various operating systems, browsers, network conditions, and hardware setups, providing a consistent user experience across different environments.
• Regression tests assess whether recent changes to the application code have impacted its performance negatively. They help ensure that new features, updates, or fixes do not introduce performance regressions or degrade the overall system performance compared to previous versions.
• Scalability tests evaluate how well the application can scale up or down to accommodate changes in workload or user demand. They assess the system’s ability to maintain performance levels as the number of users, transactions, or data volume increases or decreases, helping identify scalability limitations and bottlenecks.
• Endurance tests assess the system’s stability and performance over an extended period under a consistent workload. They aim to uncover any memory leaks, performance degradation, or other issues that may occur when the application runs continuously for hours, days, or even weeks, ensuring long-term reliability and robustness.
• Resilience tests evaluate the application’s ability to withstand and recover from failures or disruptions gracefully. They simulate various failure scenarios, such as network outages, server crashes, or database failures, to assess how the application responds and recovers without data loss or significant downtime.
• Reliability tests assess the system’s stability, availability, and resilience under real-world conditions, simulating failure scenarios and adverse conditions. They validate the system’s ability to maintain consistent performance and functionality over time, ensuring reliable operation in production environments.

What is the difference between load testing and performance testing?

Load testing and performance testing are often used interchangeably. The terms are also sometimes used as though they’re at odds. Both stances are wrong. Load testing is one of many types of performance testing; the most important ones also include unit, stress, soak, and spike tests.

What does performance testing measure?

Performance testing can be used to analyze success metrics like response times and potential errors. With these performance results in hand, you can confidently identify bottlenecks, bugs, and mistakes – and decide how to optimize your application to eliminate the problem(s). The most common issues highlighted by performance tests are related to speed, response times, load times, and scalability.

• Load time: Excessive load time is longer than the time required to start an application. Any delay should be as short as possible – a few seconds, at most, to offer the best possible user experience.
• Response time: Poor response time elapses between a user entering information into an app and the app’s response to that action. Long response times significantly impact the user experience.
• Scalability: Limited scalability occurs when an app has poor adaptability to differing numbers of users, such as when the app performs well with just a few concurrent users but deteriorates as user numbers increase.
• Bottlenecks: Bottlenecks are obstructions in the system that decrease the overall performance of an application. They are usually caused by hardware problems or poorly written code.

What is the performance testing process?

While testing methodology can vary, there is a generic framework you can use to identify weaknesses and ensure that everything will work properly in various circumstances.

1. Identify the testing environment. Before you begin the testing process, it’s essential to understand the details of the hardware, software, and network configurations you’ll be using. Comprehensive knowledge of this environment makes it easier to identify problems that testers may encounter.
2. Identify performance acceptance criteria: Before conducting the tests, you must clearly define the success criteria for the application, as it will not always be the same for each project. When you are unable to determine your success criteria, it’s recommended that you use a similar application as the benchmark.
3. Define planning and performance testing scenarios. To carry out reliable tests, it’s necessary to determine how different types of users might use your application. Identifying key scenarios and data points is essential for conducting tests as close to real conditions as possible.
4. Set up the testing environment. Begin by configuring the testing environment to mirror the production setup. This includes setting up servers, databases, and network configurations to closely replicate real-world conditions. Ensure that the application under test (AUT) is deployed in this environment. Integrate monitoring tools to collect performance metrics during testing.
5. Implement test design. Develop test scripts and scenarios based on predefined objectives and acceptance criteria. These scripts should emulate various user interactions and system behaviors. Ensure that the test design aligns with identified key scenarios and data points for realistic testing. Cover different types of tests such as load testing, stress testing, and scalability testing.
6. Run and monitor tests. Execute the prepared test scripts in the configured environment. Monitor system performance metrics in real-time to evaluate response times, throughput, and resource utilization. Keep a close eye on the test environment for any anomalies or performance bottlenecks. Continuously observe test progress and note any deviations from expected behavior.
7. Analyze, adjust and re-do the tests. Analyze and consolidate your test results. Once the necessary changes are completed, tests should be repeated to ensure the elimination of any other errors.

Does performance testing require coding?

A question that troubles many aspiring performance testers is whether performance testing requires coding. Performance testing doesn’t require coding because there are performance testing tools that choose a codeless approach. On the other hand, some tools rely on coding. Whether you choose a codeless or code-based approach depends on many factors, but primarily the coding experience and knowledge of your team members.

Performance testing does not require coding because there are performance testing tools that choose a codeless approach.

What are the characteristics of effective performance testing?

Realistic tests that provide sufficient analysis depth are vital ingredients of “good” performance tests. It’s not only about simulating large numbers of transactions, but also anticipating real user scenarios that provide insight into how your product will perform live. Performance tests generate vast amounts of data.

The best performance tests are those that allow for quick and accurate analysis to identify all performance problems and their causes. With the emergence of Agile development methodologies and DevOps process practices, performance tests must remain reliable while respecting the accelerated pace of the software development life cycle.

To keep pace, companies are looking to automation, with many choosing NeoLoad – the fastest and most highly automated performance testing tool for the design, filtering, and analysis of testing data.

Performance testing success metrics

Clearly define the critical metrics you will be looking for in your tests. These metrics generally include:

• Memory usage: Use of a computer’s physical memory for processing.
• Network bandwidth: Number of bits per second used by the network interface.
• Disk I/O busy time: Time the disk is busy with read/write requests.
• Private memory: Number of bytes used by a process that cannot be shared with others.
• Virtual memory: Amount of virtual memory used.
• Page faults: Number of pages written or read to disk to resolve hardware page defects.
• Page fault rate: Overall processing rate of faulty pages by the processor.
• Hardware interrupts: Average number of hardware interruptions the processor receives/processes each second.
• Disk I/O queue length: Average read/write requests queued for the selected disk during a sampling interval.
• Packet queue length: Length of the output packet queue.
• Network throughput: Total number of bytes sent/received by the interface per second.
• Response time: Time taken to respond to a request.
• Request rate: Rate at which a computer/network receives requests per second.
• Pooled connection reuse: Number of user requests satisfied by pooled connections.
• Max concurrent sessions: Maximum number of sessions that can be simultaneously active.
• Cached SQL statements: Number of SQL statements handled by cached data instead of expensive I/O operations.
• Web server file access: Number of access requests to a file on a web server every second.
• Recoverable data: Amount of data that can be restored at any time.
• Locking efficiency: Efficiency of table and database locking mechanisms.
• Max wait time: Longest time spent waiting for a resource.
• Active threads: Number of threads currently running/active.
• Garbage collection: Rate at which unused memory is returned to the system.

The goal of each performance tester is to prevent bottlenecks from forming in the Agile development process.

Why automate performance testing? For more Agility!

Digital transformation is driving businesses to accelerate the pace of designing new services, applications, and features in the hope of gaining/maintaining a competitive advantage. Agile development methodologies can provide a solution. Despite the adoption of continuous integration by Agile and DevOps environments, performance testing is typically a manual process.

The goal of each performance tester is to prevent bottlenecks from forming in the Agile development process. To avoid this, incorporating as much automation into the performance testing process where possible can help. To do so, it’s necessary to run tests automatically in the context of continuous integration and to automate design and maintenance tasks whenever possible.

The complete automation of performance testing is possible during component testing. However, human intervention of performance engineers is still required to perform sophisticated tests on assembled applications. The future of performance testing lies in automating testing at all stages of the application lifecycle.