Tools: Powerful How Gatling Uses AI To Support Performance Tests

Posted on Feb 11

• Originally published at gatling.io

AI is showing up everywhere in software testing. Scripts get generated faster. Results get summarized automatically. Dashboards promise insights without effort.

But performance testing isn’t like unit tests or linters.

When systems fail under load, teams need to know what was tested, how traffic was applied, and why behavior changed. That’s why many engineers are skeptical of AI in performance testing, not because

AI is useless, but because black-box automation erodes trust where it matters most.

TL;DR: AI can help performance testing, but only if teams stay in control.

This article looks at where AI genuinely helps in performance testing, where it doesn’t, and how teams can adopt AI-assisted tools without giving up control, explainability, or engineering judgment. It also explains Gatling’s approach: using AI to reduce friction and speed up decisions, while keeping performance testing deterministic and test-as-code.

So, if AI is taking the world by storm, why don’t all developers use AI performance testing yet?

However, in practice, skepticism often fades once teams see AI reduce manual setup work and free time for investigating real performance issues without taking ownership away from engineers.

AI is changing how teams design and analyze performance tests under load. Gatling’s approach is to help teams reason about that behavior faster, without turning performance testing into a black box.

Source: Dev.to