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Load Testing Blog

OctoPerf v16 - A major UI makeover

In 2022, OctoPerf went through a major UI makeover. The goal was not only to move to a more modern tech stack, but above all to offer a smoother, more flexible experience to our users. Since then, this new UI has been warmly welcomed by the thousands of people using OctoPerf every day.

Over time, we’ve kept listening to you: your positive feedback, your suggestions, your ideas for improvement. All of that has helped shape the brand-new version of the OctoPerf UI that we’re excited to share with you in this short demo video, freshly rolled out to production on the SaaS. It's also available for On-Premise users.

  • 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.

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  • 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.

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AI in Performance Testing: MCP server Integration with OctoPerf

Some topics are just too trendy to overlook, and AI in testing is definitely one of them. A few weeks ago, we shared a blog post introducing the integration between an MCP server and OctoPerf, highlighting the many benefits it brings.

To illustrate this in action, we recently hosted a webinar led by Thomas Pitteman, performance testing expert at Adeo and OctoPerf power user. In this short 20-minute session, Thomas walks through the concept behind the integration, demonstrates several prompts to launch a test directly from Claude, and shows how to query the AI for insights and recommendations on the results.

The webinar wraps up with a Q&A session, which we chose to keep, so you can hear answers to questions you might be wondering about too.

Sharing variables between Virtual Users

Summary

Sharing dynamic values across Virtual Users is often required in realistic load-testing scenarios. AMQP provides a simple and reliable way to exchange data between scripts by pushing values into a queue and retrieving them later. The tutorial demonstrates how one Virtual User can publish generated IDs to RabbitMQ, while another consumes them on demand.

CloudAMQP is used as the example broker, but the method works with any AMQP-compatible system. OctoPerf integrates easily with these HTTP endpoints, allowing both insertion and retrieval of queued values. Combined with JSON extractors, this approach makes cross-VU coordination straightforward and scalable.

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