AI as an Assistant for QA Engineers

As a QA, you often face repetitive tasks and mountains of documentation. AI can be a smart assistant that takes some of this load off your shoulders.

Here are everyday QA activities where AI shines, helping you:

✔️ Save time

✔️ Reduce human errors

✔️ Focus more on critical thinking and quality strategy

Everyday tasks AI can assist with:

Test case generation and optimization

Automated test script maintenance (self-healing)

Defect prediction and prioritization

Test data generation

Let's take a closer look at AI features that can be helpful for QAs.

1. Test Case Generation

QA engineers typically create test cases by reviewing the Product Requirements Document (PRD) using the described functionality, user flows, and acceptance criteria to define what needs to be tested. With AI, this process can be accelerated. AI can generate test cases directly from user stories, feature descriptions, or acceptance criteria. For example, tools like Katalon offer AI-powered capabilities that can help generate test cases automatically. Of course, these AI-generated cases still require human review to ensure accuracy, relevance, and alignment with the overall test strategy.

2. Test Code Generation

Chat GPT and other LLMs (Large Language Models) can generate test code for you.

Paired with developer-integrated environments (DIE) tools such as GitHub Copilot, they can:

Generate test code from simple comments in your IDE

Create complete automated test scripts from provided test inputs or scenarios

Suggest code improvements and refactor existing tests for better maintainability

For instance, tools like Katalon, TestRigor can assist with Test Code Generation

3. Code Explanation

One standout AI feature I’ve seen (so far) only in Katalon: simply highlighting any section or line of code and right-clicking on it and selecting StudioAssist -> Explain Code (screenshot below).

4. Self-Healing Tests

AI tools evaluate changes in the code base.

Able to fix, for example, label or ID changes to have stable test scripts.

Some tools monitor running tests and fix them on the fly.

Verify test coverage and supplement gaps if needed.

5. Natural Language Processing

NLP tools use AI to transform text into commands.

Single-step description: write “click login, enter [e-mail], enter password, submit” → NLP converts that into test commands.

Full user journeys: describe an end-to-end flow (signup → verify email → purchase) and the tool generates a complete automated script.

Speeds up test creation and makes automation accessible to non-developers or product folks.

Tools that support this: testRigor, testResults.io, Functionize, Virtuoso

1. Bug Report Creation

AI can help you generate clear, well-structured bug reports in seconds, pulling in all the details you’ve provided, such as:

Steps to reproduce

Expected vs. actual results

Screenshots or logs

Environment details

7. Visual Checks

Visual Checks = automated eye for your UI/UX

AI-powered visual tools compare screenshots, detect meaningful differences, and integrate with automation so your CI pipeline guards visual quality on every commit.

Save lots of time

Tools (quick notes — pick by needs): Applitools, Katalon, Kobiton, Browserstack, Lambdatest, and Ayespy.

🧠 What do you think?
Is AI making your life easier, or introducing more complexity?