Ensuring long-term performance and stability in software testing is just as important as handling peak loads. Endurance testing is a critical evaluation method that measures how an application performs under a sustained workload over an extended period. This type of testing helps uncover issues like memory leaks, system slowdowns, and resource exhaustion—problems that might not be evident in shorter tests.
As businesses rely more on cloud-based applications, AI-driven systems, and large-scale platforms, endurance testing has become essential for maintaining software reliability. In this guide, we’ll explore endurance testing in detail.
Endurance testing is a type of performance testing that evaluates a system’s stability and reliability over an extended period under a sustained load. It helps determine if an application can handle continuous usage without performance degradation, memory leaks, or crashes.
Unlike stress testing, which pushes a system to its limits, endurance testing maintains a normal workload over a prolonged duration to detect slow performance issues that might not appear in short-term tests.
This testing is crucial for applications expected to run continuously, such as banking systems, healthcare applications, and cloud-based platforms. It ensures that software remains responsive, efficient, and free from issues like memory exhaustion or system failures over time.
Endurance tests are conducted to find unusual behavior in the system that occurs over extended periods when loads are high. Your system may run just fine during typical situations but may crumble in certain areas during high peak volumes, and that’s where endurance testing comes in.
Before we describe every endurance test with example scenarios, let’s look at what you should monitor when running endurance test cases:
Endurance testing follows a structured approach to evaluate an application's stability under continuous load. Here’s how it works:
Trending technologies often introduce increased complexity, dynamic scalability, and new challenges in software development. Endurance testing in software testing becomes essential to validate the long-term performance, stability, and reliability. Below are some points that support this change in endurance testing.
AI has significantly influenced endurance testing in software development by enhancing automation, efficiency, and the ability to handle complex scenarios. Below are some key ways AI has impacted the endurance testing area:
Here are the key considerations for planning and designing endurance tests to ensure software reliability and long-term performance.
Here’s an endurance test with an example situation many of us understand: memory leakage.
A memory leak occurs when weak programming code fails to release objects that are no longer used. Available memory wears down gradually over time, which can slow down your application and, worse, stop it entirely from working.
The goal of running an endurance test is to prevent such scenarios. In endurance testing, your QA team runs your system for an established amount of time under specific amounts of load to see if any leakage in memory occurs.
There are many reasons why endurance testing should be included in your QA test strategy. When best practices are applied, your team can expect endurance testing to:
By replicating genuine user interactions and continuous engagement, endurance testing precisely depicts software performance in a real-world scenario.
It has the potential to identify and address potential issues before they impact users, reducing the likelihood of disruptive failures or reputation damage.
Put simply, an endurance test is a subtype of load testing. Load testing, when leveraging the cloud, is meant to test your application by increasing the load on your system constantly and steadily until it reaches the threshold limit. Endurance testing furthers this concept to ensure your software behaves as expected when under heavy loads for extended periods.
Here’s a breakdown to help you remember the difference:
| Aspect | Endurance Testing | Load Testing | Stress Testing |
|---|---|---|---|
|
Purpose
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Evaluates system stability over extended periods under a normal load.
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Measures system performance under expected load conditions.
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Determines the system's breaking point by applying excessive load.
|
|
Focus Area
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Detecting memory leaks, slow performance degradation, and resource exhaustion.
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Identifying response time, throughput, and performance bottlenecks.
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Finding the maximum capacity before failure or instability.
|
|
Load Level
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Normal expected load over a long period.
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Normal to peak expected load for a limited duration.
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Load beyond system capacity to test failure points.
|
|
Duration
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Long-term (hours, days, or weeks).
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Short-term (a few minutes to a few hours).
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Short-term (until the system crashes or becomes unstable).
|
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Key Metrics
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Memory usage, CPU utilization, and database connections over time.
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Response time, latency, throughput, and error rates.
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Failure points, system recovery time, and server limits.
|
|
When to Use
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For applications that must run continuously without degradation.
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To validate system performance under typical and peak loads.
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To test system robustness and recovery under extreme conditions.
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Use Cases
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Cloud applications, banking software, and healthcare systems that must operate 24/7.
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E-commerce platforms handling peak shopping traffic.
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Online ticket booking systems during flash sales.
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The best way to run endurance test cases is with the support of endurance testing tools. Endurance testing already requires long periods for testing, so running these tests manually can take your QA team too much time to complete without a guarantee of test accuracy.
There are many things to consider when choosing a test automation tool for endurance testing. Our experts at QASource recommend checking out these testing tools for your QA strategy.
Endurance testing comes with several challenges that can impact test accuracy and effectiveness. Here’s a look at these challenges and how QASource can provide solutions.
Challenge: Over time, the software may not release memory properly, leading to resource exhaustion, slow performance, or crashes.
Solution: Our engineers use advanced monitoring tools to detect memory leaks and analyze resource usage patterns, ensuring efficient memory management.
Challenge: Systems might work well initially but slow down after extended usage due to inefficient database queries, cache issues, or excessive logging.
Solution: We conduct continuous performance monitoring and use tools like JMeter, LoadRunner, and New Relic to detect gradual slowdowns.
Challenge: Creating a sustained, realistic load that mimics user behavior over a long period is complex.
Solution: We design custom test scripts and leverage cloud-based performance testing tools to effectively simulate real-world traffic and workflows.
Challenge: Since failures often occur after hours or days of testing, pinpointing the exact cause can be difficult.
Solution: Our AI-driven analytics and log analysis techniques help correlate failures with system events, making debugging faster and more efficient.
Challenge: Long-duration tests generate massive logs and performance data, making it hard to analyze trends.
Solution: We utilize automated log analysis tools and custom dashboards to process and visualize performance trends, helping businesses make informed decisions.
Challenge: Cloud-based and microservices architectures add complexity as multiple services interact over time.
Solution: Our expertise in cloud testing and distributed system monitoring ensures robust test coverage across all application components.
Challenge: Running endurance tests for long durations requires significant infrastructure and resources.
Solution: Our cost-optimized endurance testing solutions, including cloud-based test environments, reduce infrastructure costs while maintaining high-quality testing standards.
Endurance testing is crucial to ensuring software reliability and performance. Subjecting software to heavy workloads and prolonged usage helps uncover vulnerabilities and optimize performance. This ensures reliable, high-performing applications that sustain long-term use. Embrace endurance testing as a fundamental aspect of your software testing strategy to ensure the robustness and reliability of your applications.