Common performance testing fallacies can significantly impact the effectiveness and accuracy of performance testing efforts. Here are some notable fallacies:
Inadequate User Think Time:
This fallacy involves not incorporating realistic user think times in test scripts. Real users do not make back-to-back requests without pauses, and failing to simulate this can lead to unrealistic load scenarios and inaccurate performance metrics[1][3][4][6].
Using a Blank Data Set:
Testing with a significantly smaller dataset than what is used in production can lead to misleading results. For example, a query that runs quickly on a small dataset may perform poorly on a larger, production-sized dataset[1].
Failing to Test for Performance:
Assuming that performance testing is unnecessary because the application is expected to perform well can lead to severe issues. Performance testing should be an integral part of the development process to identify potential bottlenecks and performance issues early[2].
Lack of Methodology:
Not having a structured approach to performance testing can result in incomplete or ineffective tests. A well-defined methodology helps ensure that all critical aspects of performance are tested systematically[2][3][6].
Overloading Load Injectors:
Using a single load injector to simulate a large number of users can lead to inaccurate results due to the injector itself becoming a bottleneck. It is better to use multiple load generators to distribute the load[1].
Testing Only Over a LAN:
Conducting performance tests only over a Local Area Network (LAN) and ignoring wider network conditions can lead to an underestimation of real-world performance issues. Testing should include various network conditions to simulate real user environments[1].
Ignoring Script Errors:
Overlooking errors in test scripts can lead to invalid test results. Even if response times and throughput appear acceptable, script errors can skew the results and hide underlying issues[1][4].
Late Focus on Performance Testing:
Delaying performance testing until the end of the development cycle can result in discovering performance issues too late, making them more costly and time-consuming to fix. Performance testing should start early and be integrated throughout the development process[3][6][10].
