Exploring Concurrent Users in Performance Test Modelling
Welcome to the second installment of our blog series on performance test modelling. If you missed the first part, you can catch up on the overview and links to all the articles in “Performance tests modelling (part 1).” In this blog post, we delve into the concept of “concurrent users,” its significance in performance testing, and why understanding its impact is crucial.
Understanding Concurrent Users
Concurrent users are a common metric used to gauge the load on a system, representing the number of users actively using the system at the same time. While stating a requirement like “The system should withstand 200 concurrent users” may seem straightforward, it raises several questions:
- What defines “concurrent”?
- How do you define a “user”?
- Are 200 concurrent users sufficient?
- Do we consistently have 200 concurrent users?
Defining “concurrent” is the first step in ensuring your test accurately reflects user behavior. Whether you count server-side sessions or base it on time intervals between user actions, clarity in your definition is key to setting up a meaningful test.
Understanding User Behavior
Modeling user personas in a test simplifies user interactions but may overlook the diverse real-world user actions. Understanding and accounting for the broad spectrum of user behaviors—whether it’s search engine bots, malware scanners, or other external requests—is critical to a comprehensive performance test.
Considering various user behaviors beyond the norm can uncover potential performance vulnerabilities that traditional tests might miss. By broadening your definition of “users,” you create a more robust testing environment.
Challenging User Volume Assumptions
Merely stating a requirement for handling a specific number of concurrent users isn’t enough. The basis for determining this number, whether rooted in historical data or marketing projections, must withstand scrutiny. A thorough review of this metric ensures your performance test aligns with realistic user scenarios and business objectives.
Accounting for Timing in User Analysis
In addition to user volume, understanding the timing of user activity is crucial. Whether it’s peak hours during business days or sporadic global usage, tailoring your test scenarios to match expected production usage is essential. Testing for scenarios that mirror real-world conditions ensures your performance tests provide actionable insights.
In Conclusion
Simply stating “N concurrent users” without context overlooks critical aspects of a performance test. By delving deeper into user definitions, behaviors, volume assumptions, and timing considerations, you create a more effective and insightful performance test model. Stay tuned for Part 3, where we explore how system architecture and test data impact performance test results.