Beyond Speed: Understanding Performance Testing
About Me
Hey there! I'm Matt Gilbert, and I've been passionately working in the software testing space for over a decade. I earned my Bachelor of Science degree in Software Development from Western Governors University, and since then, I've had the opportunity to work in various industries like Insurance, Tech Startups, SaaS, and Healthcare, as well as contract work. I am also the creator and founder of ClearInsite - a comprehensive web testing platform that helps identify issues in websites across accessibility, performance, security, SEO, content, and usability. It automatically scans websites, provides detailed reports with severity ratings, and offers AI-powered recommendations to improve user experience and ensure compliance with web standards.
In my posts, you can expect to read about everything from beginner to advanced testing concepts, as well as insights on leadership, technical testing approaches, team management strategies, and generative AI and its application in software testing. I regularly work with industry-standard tools as well as constantly learn new ones, and I'm passionate about sharing practical knowledge that you can apply to your own testing challenges.
Throughout my career, I've gained extensive experience in a wide range of testing techniques, such as API testing, Integration, Performance, Accessibility, UI, Usability, Mobile, and Contract testing. In addition, I've honed my skills in Test Automation Framework development using Python, Typescript, Java, and C#. You can find me on LinkedIn, where I regularly share insights about software testing. Let's connect!
What is Performance Testing?
Performance testing is extremely valuable, yet unfortunately, not often done well. This form of testing helps us verify that our software can handle real-world conditions while maintaining responsiveness and stability. There is a big need for specialized performance testing now more then ever. Applications will continue to grow more and more complex and user expectations will rise - simply ensuring functional correctness is no longer enough. Follow along below as I attempt to explain my take on performance testing and how best to implement it.
At its core, performance testing examines how a system behaves under various conditions. Unlike functional testing that verifies "does it work?", performance testing answers questions like "how well does it work?" and "will it continue to work under stress?"
Think of performance testing as evaluating a car. Functional testing ensures all parts work correctly, while performance testing measures how it handles at different speeds, on various terrains, with different passenger loads, and during extended trips.
Key Types of Performance Testing
Load Testing
Load testing examines system behavior under expected usage conditions. It answers the very basic question: "Can our application handle the normal load it will face in production?"
When to deploy: Before any major release, after significant architecture changes, or when preparing to onboard new users.
Example: Imagine an e-commerce website preparing for normal business operations. Load testing would simulate typical user patterns - browsing products, adding items to carts, and completing purchases - at volumes matching average daily traffic. The goal is to ensure that response times remain within acceptable thresholds during routine operation.
Stress Testing
Stress testing pushes systems beyond normal operating capacity to identify breaking points. It helps answer the question: "What happens when our system is overwhelmed?"
When to deploy: Before anticipated traffic spikes (like sales events), when determining scaling needs, or to understand failure states and the ability of the system to recover from them.
Example: For a ticket-booking system launching concert tickets, stress testing would simulate traffic far exceeding expectations - anywhere from 3-10x the anticipated load is a good place to start. This number will be dependent on how much traffic is expected, but it’s a good rule of thumb to go slightly beyond that and to prepare for the unexpected. This helps identify which components fail first and how the system degrades, allowing developers to implement graceful failure mechanisms rather than complete crashes as well as to scale up the system and implement more compute power prior to going live.
Spike Testing
Spike testing examines how systems respond to sudden, dramatic increases in load.
When to deploy: Before planned high-traffic events or for applications that naturally experience traffic spikes.
Example: A news website might experience sudden traffic surges when breaking news occurs. Spike testing would simulate this by rapidly increasing virtual users from baseline to peak usage within seconds or minutes, then observing response time degradation and recovery patterns, as well as cost associated.
Endurance Testing (Soak Testing)
Endurance testing verifies system stability over extended periods of continuous operation.
When to deploy: For systems expected to run continuously with minimal downtime or when hunting memory leaks and resource consumption issues.
Example: A hospital patient monitoring system must operate 24/7 without failure. Endurance testing would run the system under normal load for several days, monitoring for memory leaks, database connection issues, or performance degradation that might only appear after extended use.
Volume Testing
Volume testing examines system behavior when processing large amounts of data.
When to deploy: Before major data migrations, for systems handling growing datasets, or when database schemas change.
Example: A financial reporting application might need to process years of transaction data. Volume testing would populate the database with representative data volumes and then measure how report generation times scale with increasing data size.
Scalability Testing
Scalability testing evaluates how effectively a system can scale up or out to meet increasing demand.
When to deploy: When planning infrastructure investments, evaluating cloud migration strategies, or designing auto-scaling policies.
Example: For a SaaS application, scalability testing might incrementally increase load while adding application servers, measuring how performance metrics improve with each new resource. This helps determine optimal scaling strategies and identify bottlenecks that don't resolve through horizontal scaling.
Integrating Performance Testing into Development
Performance testing isn't just for pre-release validation. Many teams integrate it throughout the development lifecycle:
Early architecture validation: Simple load tests on key components during design
Continuous integration: Basic performance checks with each build
Production monitoring: Real-user performance metrics compared against test predictions
Bonus
Read this far? Congrats! I am running a promotion the whole month of May, offering 10% off ClearInsite services. Simply use code WELCOME10 at checkout.
Outro
Thanks for reading! If you have any questions about this article or any of my past articles, feel free to reach out on my LinkedIn. I’d love to hear your thoughts!