Performance testing vs. load testing vs. stress testing

This is a guest post written by Nikki Siapno, co-founder of Level Up Coding, a company that is transforming programming education with visual content.

Testing spans every corner of the complex and fast-paced world of software development, and it plays a key role in ensuring the quality of any given system. Testing not only helps confirm that systems are functioning as they should, but also helps improve their reliability, performance, and security.

Good testing practices contribute to a codebase’s reliability and maintainability, which in turn allows programmers to develop quickly and ship confidently. A thorough understanding of the system’s intended behavior is essential for effective testing, as it helps confirm that the system is aligned with user expectations and business needs.

There are many different approaches and methods for testing, and each has its own strengths and distinct focus areas. In this article, we’ll dive into three types of tests that every complete test suite should have: performance tests, load tests, and stress tests.

Performance testing

It’s important to evaluate an application’s efficiency to ensure it stays performant under various conditions in production. This is where performance testing shines. The goal of performance testing is to identify and eliminate bottlenecks while verifying that the system meets the predefined performance criteria.

Performance testing involves testing the system under different conditions while monitoring its stability, performance, and resource usage. Throughput, latency, response times, and CPU usage are common metrics that are used to determine the outcome of a performance test. These metrics help development teams understand how well the system can handle real-world conditions and identify areas for improvement.

There are several best practices for performance testing that can help generate accurate results and actionable insights. For instance, the conditions and workloads in which you’re testing the system should represent real-world usage. Performance testing should also be done incrementally, with a gradual increase in workload or change in conditions. This approach allows the development team to identify performance thresholds and bottlenecks at each increment. Finally, documentation regarding the performance testing process, testing conditions, and results should be kept up-to-date for future reference and analysis.

Load testing

Load testing is a subset of performance testing, and it plays a vital role in ensuring a system’s scalability. The goal of load testing is to validate the system’s ability to scale its resources and processes during periods of peak user activity. Load testing focuses on the capacity and scalability of the system, whereas performance testing evaluates the system’s overall efficiency and behavior.

Load tests are performed in the same way as performance tests. Testing is kicked off with a baseline load test to evaluate how the system handles regular activity. The workload is then incrementally increased to identify performance and scalability limits. Metrics such as CPU, memory, and network bandwidth are monitored and compared against performance goals. As with performance testing, realistic workloads should be used to reflect real user activity. Otherwise, the results may not be relevant and the issues that are identified may not be of real concern.

Load testing helps development teams determine ahead of time whether their system can maintain reasonable performance standards during high levels of activity. Potential bottlenecks can be removed and inefficient components can be corrected well before they reach production.

Stress testing

Stress testing is another important subset of performance testing. During stress testing, the system is pushed beyond its normal operating conditions to evaluate its stability and fault tolerance. This can involve introducing resource constraints or deliberate errors, running the system on extremely high workloads, and more.

By limiting system resources, development teams can evaluate and adjust how the system processes tasks based on momentary constraints, which can boost its fault tolerance. Introducing errors and deliberately failing components is another method of stress testing that can improve the system’s resilience, as it allows teams to more easily assess and improve recovery mechanisms.

Subjecting the system to edge-case scenarios is a great way to ensure it can withstand the most demanding conditions. Some other scenarios that are worthwhile for testing include extended processing periods, extremely large data inputs and payloads, and peak user traffic. With the simple addition of stress testing, development teams can build a culture that encourages defensive programming, which leads to higher quality and more resilient systems.

How do I get started?

When it comes to creating and implementing a test suite, the first step is to understand your objectives. This involves defining the scope of your tests, the requirements of each component, and the key performance metrics and goals for your system. Then, you can move on to investigating user behavior and usage patterns to define test scenarios. You can also inflate the behaviors and patterns you’ve identified to formulate edge cases for your stress tests.

A thorough understanding of common performance bottlenecks can help you create test scenarios that are effective at detecting problems. Specific bottlenecks vary from system to system based on their characteristics and typical user activity. That being said, some common culprits to look out for are inefficient algorithms, slow database queries, poor allocation of resources, or flaws in how the infrastructure is designed or implemented.

Once a general plan has been determined, you need to decide how to create your tests. This involves choosing tools and deciding how the test environment will be configured and implemented. It’s important to select tools that align with your testing objectives and test scenarios, which is why it’s crucial to go through the in-depth planning of the previous step. The key factors to consider are the technologies that the tools support, their ability to generate sufficient load, their reporting features, their monitoring capabilities, and their cost.

An appropriate testing environment should replicate the production stack and configuration. Automated data and load generation should also be implemented with mechanisms that incrementally increase volume during testing. Additionally, it’s crucial to ensure that the testing environment is entirely isolated from production data and processes so as to not impact live activity. This can be achieved with sandboxing and virtualization. Finally, the system should roll itself back to an initial state after a set of tests is complete.

After the testing environment has been initialized and testing scripts have been developed, it’s time to formulate processes for analyzing test results, documenting findings, and carrying out actionable changes. As the system evolves, teams should keep an eye on ways to improve the tests, testing environment, and processes. Test suites should be regularly updated to ensure that they remain relevant and optimized for the current state of the codebase.

Evaluating test results

What you do with test results is just as important as how you implement the tests themselves. It’s best to use a systematic approach when analyzing the results of performance, load, and stress tests, which can help ensure that effective system improvements can be made. This involves determining the success criteria ahead of time, focusing on key metrics that contribute to system requirements and test objectives, performing statistical analysis to identify patterns, and using visualization tools.

It’s easy to get bogged down with the large amounts of data that these tests can produce, so it’s important to determine exactly what constitutes a passing test result—and to only focus on key metrics. This approach makes it easier to identify potential changes that will have the largest impact on the system and business goals. Visualization tools can also help identify trends and anomalies and assist with communicating results with the rest of the team. Identifying patterns over time can help determine which components of a system are at high risk of becoming a bottleneck, which can drive efforts to improve the system’s performance, scalability, and fault tolerance.

Wrapping up

Performance testing, load testing, and stress testing play a crucial role in software development. They not only guarantee that issues are not introduced to production, but also help teams identify areas for improvement. Implementing these tests, following best practices, and engaging in critical analysis can help teams create systems that are optimized for performance and efficiency.

Remember that performance optimization is an ongoing, iterative process. Your test suite and techniques should evolve along with your system. The insights that are revealed by performance tests can help teams gain a competitive edge in today’s technology-driven world.

Learn about performance testing with Postman.