I’m curious about the “AI-powered testing” feature included in the paid plans of Katalon Studio.
Is this similar to the recent agentic coding trend, where AI automatically generates test code?
If anyone is currently using this feature, could you share how you typically use it in practice?
Are there any guides, documentation links, or community resources where I can learn how to use this feature?
here are all AI features in action Pioneering Generative AI in Test Automation with Katalon
Please let me know if you need more information.
Well , here are some of them
1- You can generate code from a plain english inside the IDE
2- You can get to know what the code is doing in plain english,
3- You can fetch any solution from Katalon Docs using the AI Ask feature
4- You can generate test cases using Katalon Agents.
5- You can create task specific agents for your project
6-You can create API test cases through Katalon AI
7- You can utilise Katalon MCP servers
8- You can add external MCP servers in katalon
Short answer: Katalon’s “AI‑powered testing” in paid plans is an AI assistant meant to accelerate and improve test creation and maintenance — it’s an assistive, productivity feature, not an autonomous agent that runs your whole test engineering function and writes production tests end‑to‑end without human oversight.
What it is (high level)
AI helps generate, suggest and maintain test assets: e.g., propose test steps, create or improve locators, suggest assertions, and help craft test data or test case templates. It’s designed to reduce manual effort and repetitive tasks, not to fully replace human test design/review.
Typical behaviors: generating test steps from a page or a flow description, suggesting element locators or selectors, proposing assertions for validations, and offering suggestions for flaky tests or locators that need maintenance.
How this differs from the “agentic coding” trend
Agentic coding typically refers to autonomous agents that take high‑level goals and perform multi‑step actions (explore, modify code, run tests, iterate) with minimal human supervision. Katalon’s AI features are focused on assisting testers inside the test authoring and maintenance workflows — assisting you to create or improve test code and objects — rather than running long autonomous loops on your codebase and CI pipeline without human checkpoints.
In short: Katalon’s AI is an assistant that speeds tasks and suggests improvements; you still review, validate and control what goes into your test suite.
How people typically use it in practice (common workflows)
Test case generation from requirements or flows: provide a flow/description or point the assistant at a page and accept generated step suggestions, then refine and save as a test case.
Locator / selector suggestions: when an element is flaky or changed, the AI can suggest more robust selectors (or alternative attributes) to stabilize tests.
Creating assertions and verifications: AI suggests which fields to verify and proposes assertion code/snippets that you can review and accept.
Test data scaffolding: generate sample test data sets (valid/invalid values) to exercise edge cases quickly.
Test maintenance support: when tests fail due to UI changes, AI helps identify likely root causes and suggests fixes for elements or steps.
Productivity in scripting: generate starter Groovy/Katalon code snippets (built‑in keywords usage) you can paste into your test case or custom keyword and adapt.
Best practices when using the AI features
Always review generated steps and code. Treat suggestions as drafts — validate them with a local run.
Use AI for repetitive/setup tasks and for ideas; keep complex business logic and edge case assertions under human control.
Combine with recording and object repository: let AI suggest selectors, then verify them in multiple environments/breakpoints.
Keep test data and secrets out of prompts; follow your org’s security and data policies.
Where to learn more (docs & community)
