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  • in JMeter
  • 8 min read

Performance Testing and Artificial Intelligence (2/2)

Summary

AI can be a powerful support tool in performance testing, but it cannot replace the judgment and structure a tester brings.
This second part of our blog post serie compares how AI and a performance tester approach script creation and results analysis, using JMeter as the reference tool.
The study shows that AI can generate workable test plans and meaningful data insights, though often lacking context, flexibility, and refinement.
Human expertise remains vital for modelling realistic user journeys, interpreting trends, and spotting nuances AI misses.
Used together, AI + tester expertise provides a stronger, more efficient workflow for performance testing.
Relying solely on AI, however, risks overlooking critical issues that impact reliability, scalability, and user experience.

Table of Contents

  • in JMeter
  • 11 min read

Performance Testing and Artificial Intelligence (½)

Summary

AI is becoming central in software delivery, but relying on it alone for performance testing can limit accuracy and real insight.
A balanced approach is needed: AI can help, yet human expertise remains essential for defining requirements, assessing risks, and understanding real system behaviour.
This first part of a two blog post serie compares the methodology of performance testers with the output produced by ChatGPT, focusing on requirements gathering and risk assessment.
A fictional application is used to evaluate how both approaches differ in depth, relevance, and business awareness.
The analysis shows strong overlaps but also highlights where AI oversimplifies or lacks contextual judgment.
Used wisely, AI strengthens performance testing—but replacing human reasoning is not realistic or effective.

Table of Contents

  • in JMeter
  • 10 min read

Generating Quality Data

The problem with test data is that it can become stale very quickly. This is either through its use from testing or from the fact that it is naturally aging in the test environments.

This is not just an issue for performance testing, although the volumes of data sometimes required for performance testing do make it harder. This also affects functional testing as well as batch testing and business acceptance testing amongst others.

Now we have previously written posts on how after completion of performance testing you leave data created by the execution of your tests which may be of use to other members of your test or development community. And we have also discussed how you can use existing data in your performance testing environment in your tests in the most effective manner.

But in both cases, this is during or after your performance testing takes place, for performance test to be executed you need quality data in your test environments. This is also true for functional, batch and user acceptance testing, really its true for any type of activity that wants to use data in the test environments.

  • in JMeter
  • 12 min read

Performance Test Results Trend Analysis

In this post we are going to look at how you can spot trends in your performance test results and use this trend analysis to help shape the way you address performance testing. Performance testing can generate a large volume of performance test data and using this data to define your future performance testing coverage, scope and volumes can really add benefit to your non-functional testing process.

To use data to support performance testing you are going to need to store it in a way that makes it accessible and comparable. The ideal solution and one we will discuss in this post is to store this data in a database. This post will look at the positive benefits of storing lots of performance data, especially data that spans a significant period of time, and we will look at ways of using this data to define performance test coverage and identify areas of performance concern.

  • in JMeter
  • 8 min read

Business Testing in Production

In this blog post we are going to look at using JMeter to support business testing in production. This is a slightly different topic to the one discussed in this post on testing in production. The one in the link above is around running your performance testing in production for reasons that are discussed in that post. This post is going to focus on how you can leverage your performance testing tools to support this activity, as discussed above we will focus on JMeter in some of the examples. But any load testing tool, or even functional testing tool, can provide the same benefits.