fair points @Monty_Bagati
i wasn’t arguing that Katalon is technically better than Playwright. my point is that some organizations prefer to buy rather than build. whether that’s worth $3,000 depends on the organization
hi @dineshh my take, none of those is the main bet
the real bet is judgment. knowing what to test, what to skip, what’s actual risk vs noise
AI can generate 50 test cases. someone has to know which 5 matter
in 2027, I think a future ready QA spends 60% reviewing what AI produces, designing strategy around real user risk, and convincing the team what not to automate. the other 40% is tools
tools are table stakes. judgment is the moat
Team worried AI will replace their jobs. If AI can generate 80% of test cases automatically,
what should the remaining 20% human effort focus on?
Should we retrain them as ‘AI test validators’ who review AI output, or
shift them to exploratory testing that AI can’t automate?
What’s the career path for QA engineers in an AI-first workflow—do they become more technical (coding) or more business-focused (requirements analysis)?
hi @nimisha.r
you can follow this steps:
- open katalon studio settings/preferences
- navigate to
AI Configurationmenu - choose
Use personal OpenAPI keyon the dropdown - put your OpenAPI secret key and choose your
Model. you can generate your OpenAPI secret key here: https://platform.openai.com/api-keys
Viele Tester sind besorgt, dass KI den Bedarf an manuellen Tests verringern wird. Dennoch erfordern Aktivitäten wie exploratives Testen, Usability-Validierung und das Verständnis von Geschäftsabläufen nach wie vor menschliches Urteilsvermögen.
Welche manuellen Testfähigkeiten glauben Sie, werden in den nächsten 3–5 Jahren wertvoller, da die KI-Verbreitung zunimmt?
In real projects, finding a bug is often easier than understanding its root cause. Testers spend significant time analyzing logs, network requests, database records, and reproducing issues.
How do you see AI helping testers with bug investigation and root cause analysis, rather than just generating test cases and automation scripts?
Sure noted @snehass I am answering in sometime
I will answer wait
Why Katalon Over Playwright/Cypress
Playwright has end-to-end testing (JavaScript/TypeScript, fast execution, great debuggers).
- Why should we invest in Katalon instead?
- What does Katalon offer that Playwright doesn’t—especially for non-technical QA engineers who can’t code?
- Is it just the UI recorder, or are there real advantages like AI test generation, TestOps reporting, or cross-browser parallel execution that Playwright can’t match?
At what team size does Katalon become more cost-effective than Playwright + custom infrastructure?
My Recommendation:
If you’re just starting, use the built-in Katalon AI Assistant because the integration is already available and requires minimal setup. If you want custom AI workflows inside your automation framework, then call the OpenAI APIs directly using Katalon’s Web Service layer. Katalon officially supports OpenAI integration through its AI Assistant, so both approaches are valid.
Option 2: Call OpenAI APIs Directly from Katalon
If your goal is to build custom AI-powered automation (test generation, data generation, validation, defect analysis, etc.), you can call OpenAI APIs using Katalon’s built-in Web Service keywords.
- Create a POST Request Object in Object repository in Katalon.
- Set endpoint: https://api.openai.com/v1/responses or any …
- Add Authorization header: Authorization: Bearer YOUR_API_KEY
- Build the JSON request body.
- Send the request using:
-
WS.sendRequest() -
Parse the AI response and use it in your test execution.
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HI @nimisha.r
Please refer below steps to integrate Open AI with Katalon studio.
YOu can access OpenAI in Katalon via 2 options
1St options: Use Katalon’s Built-in AI Assistant ( this is the easiest)
To integrate OpenAI with Katalon Studio, you need to configure your personal OpenAI API key inside Katalon’s built-in Katalon AI Assistant. This allows you to generate test scripts, explain code, and build custom keywords natively using GPT models.
Steps:
-
Obtain an OpenAI API key from the OpenAI platform.
a) Log into your account on the OpenAI Platform.
b) Navigate to the API keys section and click Create new secret key.
c) Go to your profile settings to note down your Organization ID -
Configure the API Key in Katalon Studio
-
Open your Katalon Studio application.
-
Open the top menu and click on Windows:
- Windows: Go to Window > Katalon Studio Preferences.
-
Expand the Katalon category on the left side menu, and click on the AI configuration and then click on the dropdown AI Services configuration
-
Locate the dropdown or option for the AI provider and change it to Use Open-AI compatible provider (or Personal OpenAI Key depending on your exact version).
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Enter the required details and hit Apply
This approach allows you to:
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Generate test cases from requirements.
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Generate Groovy code snippets.
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Explain existing automation code.
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Refactor scripts.
-
Get troubleshooting suggestions
Refer the katalon document please refer below link
Katalon AI Assistant Preferences | Katalon DocsHow to Use the OpenAI Integration
Once connected, you can leverage OpenAI directly inside your testing workspace through two primary methods:
1. Script Mode (Code Generation & Explanations)
-
Generate Code: Open a test case in Script mode, write a natural language prompt as a comment, right-click the text, and choose Katalon AI Assistant > Generate Code.
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Explain Code: Highlight any complex Groovy script block, right-click, and select StudioAssist Explain Code to quickly understand test logic.
If you wan to to refer video about how to integrate and use ( though it covers only the Katalon AI support)
https://www.youtube.com/watch?v=8YDd_E3T0RA&t=535s
-
-
Sometimes I get surprised with the counter answers on why to choose katalon over playwright or cypress that katalon has a recorder option.
HI Sneha
This is generic question and answer may differ from person to person. From my practical experience, I’ve seen the biggest productivity gains when AI is used as an accelerator rather than an autonomous tester.
You can create 100 test script in one go, but whether all those are useful or not it depends upon how well you communicate with the AI tools. Where I currently see AI creating more work than value is when teams expect it to generate production-ready automation without sufficient context.
-
AI often lacks understanding of application-specific business rules, dependencies, security requirements, and exception handling.
-
The generated tests may look correct but can miss critical validation points.
-
and many more.
But surely AI has its countlesssbenefits like
-
Test case generation from requirements or user stories - You can can quickly generate positive, negative, boundary, and alternate flow scenarios.
-
Instead of starting from a blank page, one can start with an 80% complete draft and refine it.
-
YOu can for sure significantly reduce analysis and documentation effort, which is also an integral part of testing
So in my view AI delivers the highest value in testing when it acts as a “co-pilot” that accelerates skilled testers, not as a replacement for testing expertise. The most successful teams I’ve seen use AI to reduce repetitive work, while human testers continue to provide business context, risk analysis, and quality judgment.
In fact, a significant portion of Playwright’s marketing effort is effectively reduced, as any discussion around Katalon almost inevitably leads users to also consider and discuss Playwright.
Migrating 300 test cases from Selenium (Java) to Katalon.
What’s the realistic timeline:
(a) 3 months with 2 QA engineers,
(b) 6 months with 1 engineer + AI assistance, or
(c) something else? Can AI automatically convert Selenium Java code to Katalon Groovy, or do we need to re-record everything?
What % of Selenium tests can be ported automatically vs. requiring manual rewrite? What’s the biggest pain point teams face during migration (selector changes, custom keyword, reporting gaps)?
I believe I already addressed this in another response, but here’s a concise recap.
I completely agree with the premise of your question. In many real-world projects, creating test cases or executing automation is only a small part of the effort. A significant amount of time is actually spent understanding why something failed.
This is one area where I believe AI can provide substantial value for testers.
Today, when a test fails, a tester may need to examine screenshots, execution logs, browser console logs, API requests and responses, database records, and application logs across multiple systems. This investigation can easily take much longer than the test execution itself.
AI can help by acting as an intelligent analysis assistant.
For example:
1)) Log Analysis
- Instead of manually searching through thousands of log lines, AI can summarize relevant errors, identify patterns, and highlight the most likely failure point. It can correlate application logs, automation logs, and server logs to reduce investigation time.
2. Failure Classification
- AI can help determine whether a failure is likely caused by a test script issue, environment instability, test data problem, network issue, or an actual application defect. This can help teams prioritize investigations more effectively.
- Cross-System Correlation
- Modern applications often involve UI, APIs, databases, third-party services, and cloud infrastructure. So, AI can connect information from multiple sources and identify relationships that may not be immediately obvious to a tester.
- Root Cause Suggestions
- By analyzing previous incidents, defect history, and execution patterns, AI can suggest probable root causes. For example, if similar failures in the past were caused by expired authentication tokens or backend service outages, AI can surface those possibilities immediately.
5. Faster Reproduction Guidance
- AI can analyze the failure context and suggest reproduction steps, required test data, or specific conditions that may have triggered the issue.
One practical example from test automation is when a test fails with a generic “Element Not Found” error. Traditionally, a tester would manually review screenshots, execution logs, network activity, and application behavior.
An AI-powered analysis could instead provide a summary such as below (this is just example):
The locator appears valid. The API responsible for loading customer data returned a 500 response. Similar failures occurred in the last three executions and were linked to a backend service outage.
So, here you can save a significant amount of investigation time.
And even this is true, AI cannot fully replace human analysis anytime soon. As Root cause analysis often requires understanding business logic, system architecture, and application behavior—areas where human experience remains essential.
My view is that AI’s biggest opportunity in testing may not be generating more test cases, but helping testers spend less time gathering evidence and more time making informed decisions. If AI can reduce a two-hour investigation to fifteen minutes by surfacing the most relevant information, that is a very meaningful productivity gain for QA teams.
Lastly, I would emphasize that the quality of responses from AI tools largely depends on how effectively you communicate with them. Providing relevant, clear information—something that improves with experience as a tester in a project—directly leads to better and more accurate outputs from AI.
The timeline depends much more on the quality of the existing Selenium framework than on the number of test cases.
As per me there are lots of challenges which one get to know when start work in real-time, but script conversion is usually the easiest part; framework migration, stabilization, and maintenance of existing functionality consume most of the effort and time.