Benchmarking Testing vs. Performance Testing in Applications: Know the Difference!

In the world of software testing, two approaches often get confused: Performance Testing and Benchmark Testing.

Performance Testing evaluates how well an application performs under various conditions. The goal is to ensure that the system meets its performance requirements, such as speed, responsiveness, and stability. There are several types of performance tests, including load testing, stress testing, scalability testing, and endurance testing.

Key Aspects of Performance Testing: 

1. Throughput: Measures the number of requests the system can handle in a given time period.
2. Latency: Measures the response time for requests.
3. Error Rate: Tracks the number of failed requests.
4. Resource Utilization: Measures how efficiently system resources (CPU, memory, network) are being used. 

Benchmark Testing involves comparing the performance of an application or system against a predefined standard or a reference point. It aims to measure the system’s performance against specific KPIs to assess whether it meets the expected requirements or how it stacks up against competitors.

Key Aspects of Benchmark Testing: 

1. Standardized Metrics: Often compares performance against industry standards, best practices, or previous versions of the system.
2. Consistency: Measures how consistently a system performs under controlled conditions.
3. Competitive Analysis: Can also be used to compare your application with competitors or other systems to see how it fares in terms of performance.

Benchmark Testing and Performance Testing can look very similar because they both involve measuring system performance. However, the key difference is in your goal, methodology, and how you interpret results. 
While both involve measuring a web application's performance, they serve different objectives. 

Benchmark Testing is often a natural next step after Performance Testing. When conducting performance tests, we gather key metrics such as response time, throughput, CPU usage, and memory consumption. These initial results serve as a baseline for future tests.

Once we establish a performance baseline, Benchmark Testing ensures that subsequent versions of the application do not degrade in performance. If performance starts to degrade, benchmarking helps detect regressions early. 

Here's how we differentiate Performance Testing to Benchmark Testing, along with key indicators for each, using a common Tool (e.g. JMeter). 
 

1. The test simulates extreme conditions (e.g., high user loads, stress tests).
2. The goal is to ensure that the application meets predefined performance criteria (e.g., response time < 2s), and to identify bottlenecks (e.g., when response time degrades or when the server crashes).
3. The script uses assertions to validate whether the system meets specific thresholds.
4. The load is dynamically increased to test how the system scales.
5. System failures are analyzed. (e.g., memory leaks, server crashes).

Example:  
"API response time should be ≤ 500ms under a load of 1000 users." 
"An automated load test checks that CPU usage stays below 70% under a simulated user load." 

✔Focus: Pass/fail based on the required performance values. 

1. The test script runs at regular intervals (e.g., daily, weekly) to compare results over time.
2. The goal is to compare the application’s performance against previous benchmarks (industry standards, competitors, or historical data.)
3. The system is NOT pushed to failure, but instead, the normal operational performance is measured.
4. JMeter reports show trends rather than just pass/fail results.
5. Multiple KPIs are monitored simultaneously, such as response time, throughput, and error rates. 

Example: "Our web app should perform as fast or better than Competitor X in loading a product page." 
✔ Focus: Comparative analysis rather than just accomplishing internal thresholds. 

Example Scenarios: How JMeter Scripts are Used in Practice 
 

• Goal: Find out how many users the system can handle before slowing down.
• Test Case: Increase users gradually from 500 to 10,000 and measure the system’s breaking point.
• Expected Output: System should handle 5,000 users with response time < 1s. If it degrades, optimize. 

JMeter Example Script for Load Testing

Figure 1.JMeter script for Load Testing

Figure 2. Performance Testing Graph

The figure above illustrates how response time increases as the number of users grows. The red dashed line represents an acceptable response time threshold (1s). When the number of users exceeds ~2000, the response time degrades significantly, indicating potential bottlenecks. 

 
 Benchmark Testing Example 

• Goal: Ensure that a web app maintains consistent performance over time.
• Test Case: Run a JMeter test with 500 users every week and compare response times.
• Expected Output: If response times increase by more than 10% from the last test, investigate. 

JMeter Example Script for Benchmark:

Figure 3. JMeter script for Benchmark Testing 

Figure 4. Benchmark Testing Graph 

The Figure above shows how response time fluctuates over multiple test runs (weeks). The red dashed line marks a 10% degradation threshold from the initial response time. If the response time consistently exceeds this line, it indicates a potential performance regression that requires investigation. 
 

Instead of manually comparing JMeter reports, you can use scripting to automate performance comparisons. Here’s an example using Python to extract and compare JMeter results dynamically: 

Figure 5. Python script for comparing JMeter results. 

Figure 6. The status after comparing the results. 

By incorporating this script into a CI/CD pipeline (such as GitHub Actions, Jenkins, GitLab CI/CD, or Azure DevOps), you can automate performance comparisons after every deployment. This means: 

• Instant Feedback: The script will run automatically after performance tests, detecting slowdowns in real-time.
• Automated Alerts: If a performance degradation is detected, the pipeline can trigger Slack alerts, emails, or even rollback mechanisms.
• Historical Tracking: The script compares weekly reports, ensuring you track long-term performance trends across multiple releases.
• Fail Fast Approach: If performance exceeds the acceptable threshold (e.g., response time degrades by more than 10%), the pipeline can fail the deployment, preventing bad performance from going live.

While Benchmark Testing and Performance Testing with assertions both assess software performance, they serve different purposes and approaches. Here’s a breakdown of their key differences:

Table 1: Performance Testing vs Benchmark Testing.