Load tests are essential tools for understanding how a system or application behaves under specific capacities. These tests mimic real-world usage scenarios, allowing us to assess the performance of systems effectively. Analyzing the results of load tests is a crucial step in understanding and improving the performance of an application or system. Here are the steps followed in this analysis process:
Introduction of Test Environment
The characteristics of the environment where load tests were conducted should be outlined. This includes details about the hardware, software, network configuration, and other relevant factors. Additionally, information about test scenarios and load profiles should be provided.
Determination of Metrics
Metrics to evaluate performance need to be identified. These metrics may include response times, transaction rates, CPU and memory usage, among other performance indicators. Metrics related to user experience, such as availability and accessibility, should also be considered.
Data Collection and Analysis
Data collected during load tests should be gathered and analyzed. This involves continuous monitoring and recording of the identified metrics. Visualizing data with graphs and analyzing trends can help identify performance issues.
Identification of Performance Issues
The analysis process should be used to identify performance issues within the system. These issues may manifest as longer-than-expected response times, high memory consumption, or excessive CPU usage. A thorough investigation is necessary to determine the root causes of these problems.
Reporting of Findings
The results of the analysis should be presented in a detailed report. This report should include identified performance issues, their root causes, and proposed solutions. Additionally, recommendations for future tests may be provided.
According to Performance Test Syllabus, the following data is analyzed in the performance test report.
Status of simulated (e.g., virtual) users
This needs to be examined first. It is normally expected that all simulated users have been able to accomplish the tasks specified in the operational profile. Any interruption to this activity would mimic what an actual user may experience. This makes it very important to first see that all user activity is completed since any errors encountered may influence the other performance data.
Transaction response time
This can be measured in multiple ways, including minimum, maximum, average, and a percentile. The minimum and maximum readings show the extremes of the system performance. The average performance is not necessarily indicative of anything other than the mathematical average and can often be skewed by outliers. The 90th percentile is often used as a goal since it represents the majority of users attaining a specific performance threshold. It is not recommended to require 100% compliance with the performance objectives as the resources required may be too large and the net effect to the users will often be minor.
Transaction failures
This data is used when analyzing transactions per second. Failures indicate the expected event or process did not complete, or did not execute. Any failures encountered are a cause for concern and the root cause must be investigated. Failed transactions may also result in invalid transactions per second data since a failed transaction will take far less time than a completed one.
Hits (or requests) per second
This provides a sense of the number of hits to a server by the simulated users during each second of the test.
HTTP responses
These are measured per second and include possible response codes such as: 200, 302, 304, 404, the latter indicating that a page is not found.
CONCLUSION
The analysis of load test results entails crucial steps including defining the test environment, determining performance metrics, data collection and analysis, identification of performance issues, root cause analysis, and reporting findings. In the reporting process, key parameters such as test scenarios, performance metrics (response times, transaction rates, CPU and memory usage, network traffic), data analysis and graphs, identified performance issues, and root causes should be considered. These parameters serve as important guidelines to understand system performance and drive improvements.
Loadium provides testers with metrics after running tests so they can track and monitor test results. Start testing now to see your own test metrics.
Thank you for reading this article. To stay updated with our latest posts, follow our blog and visit our social media accounts.
