ChatGPT in test automation: How to use it effectively

Of late, we’ve all heard the chatter about how tools like ChatGPT have gotten really good at coding and can replace entire teams of developers. The very first use cases that I saw within the teams I worked in were to use it for test case generation.

Most developers look at test writing as tedious and thus have been relying on large language models (LLMs). But there’s more to it than that.

In this post, we’ll explore all the nuances of how we can leverage the power of tools like ChatGPT in various aspects of test automation.

Test automation with ChatGPT: An overview

In the simplest of terms, ChatGPT is a large language model trained on millions of lines of code, such as Selenium scripts, Cypress tests, JUnit assertions, PyTest fixtures, and basically every testing framework that’s ever lived on GitHub.

You can ask it to write a test, it’ll reference this massive pattern library, and then it’ll spit out something that would usually look reasonable enough!

ChatGPT is really good at pattern matching. It does a great job with code generation based on stuff that it has seen before. It also handles converting natural language to code. Additionally, it is also great at explaining complex test logic and handling syntax or conversions across different frameworks.

But ChatGPT fails at certain things. For instance, it doesn’t have the ability to analyze your entire codebase, thus it has no understanding of the architecture of the application. It also misses nuanced knowledge like business logic, edge cases, or what might be most crucial to test in the overall application. Thus, it’s bad with end-to-end testing.

ChatGPT also cannot run tests in your codebase. It can’t debug something that’s flaky while you work on the codebase. It cannot connect to your CI/CD pipeline either.

Thus, ChatGPT is super helpful for stuff that generates boilerplate code, and even brainstorming test scenarios, but you can’t rely on it to design your entire test strategy!

Benefits of using ChatGPT for test automation

After having used ChatGPT for a while, here are things I noticed that actually work:

ChatGPT is great at all the repetitive code setup, such as form validation tests, API endpoint tests, and basic CRUD stuff

Dramatically faster boilerplate creation

ChatGPT is great at all the repetitive code setup, such as form validation tests, API endpoint tests, and basic CRUD stuff. Earlier, it would have taken quite some time to set up the basics to have a functional test suite. Time savings are monumental when you add up all the setup code, such as imports, fixtures, teardown, etc.

Reduced learning curve for new frameworks

Testing frameworks keep evolving. Every single one has its own quirks. With ChatGPT, you don’t need to delve into all these nuances to get working code very quickly. You save the time you’d have spent on parsing through documentation and iterating through trials and errors.

Better documentation

If you paste cryptic test code and ask ChatGPT to explain, it adds meaningful comments, breaks down what assertions are checking, and tells you what edge cases are covered. This is super helpful with legacy code.

Multi-language support

ChatGPT doesn’t care what language you throw at it. Need Python tests? Done. JavaScript? Sure. Java? No problem. It’s equally competent (or equally incompetent, depending on your perspective) across all of them. Really cuts down that context-switching headache.

Instant code examples

Instead of digging through documentation for hours or clicking through 15 Stack Overflow threads, you just get working code examples instantly. Super valuable when you’re under time pressure or dealing with less common testing scenarios where the docs are basically nonexistent.

Practical use cases for ChatGPT in test automation

Let’s get specific about what you can actually use ChatGPT for in your day-to-day testing work.

Writing sample test cases with ChatGPT

This is the obvious use case, and honestly, it works pretty well. You describe what you want to test in plain English, and ChatGPT spits out code. An important thing to remember is that ChatGPT gives you a starting point, not finished, production-ready code. You’ve still got to do work.

Unit testing with ChatGPT

LLMs are honestly pretty good at unit tests. Unit tests are narrow in scope. One function, clear inputs, predictable outputs. Contracts are explicit. LLMs have seen literally millions of unit test examples from every public repo on GitHub. Every pattern imaginable. They’ve basically memorized how unit tests work.

Writing test automation in any framework

ChatGPT can generate test code for pretty much any testing framework you throw at it. Quality varies depending on how common the framework is, but for mainstream frameworks, it’s pretty solid. You can even ask it to convert tests from one framework to another. Super useful when you’re doing migrations.

Creating test data with ChatGPT

Test data creation is tedious. ChatGPT handles this quite well. Tell it what data structure you need—JSON, CSV, database seeds, whatever—and it just generates appropriately formatted test data. Need 100 variations of email addresses to test your validation logic? Need sample profiles with different attribute combinations? Done and done.

Data isn’t perfect, to be fair. It can be repetitive or unrealistic in subtle ways. But it’s way faster than crafting it manually, and it’s good enough for most testing purposes.

Limitations of ChatGPT in QA

Here’s what ChatGPT absolutely cannot do, and you have to understand these limits to save yourself some disappointment.

No understanding of your application

ChatGPT has never seen your codebase. It has zero clue about your architecture, your business logic, your edge cases, or what actually matters to test.

For example, imagine you ask it to give you tests for a checkout flow. It’d give you something very generic. Not the code that is specific to your promo code stacking rules, or your fraud detection system, etc.

Test strategy requires understanding the business and ascertaining which tests matter the most. ChatGPT isn’t great at this.

Limited context window

ChatGPT’s context window is limited. I learned this the hard way when I pasted our entire 2000-line test file and asked it to refactor everything. Big mistake. It missed half the important details and made changes that broke dependencies three functions down.

It works much better on focused tasks. Think of ChatGPT like someone who can only look at a couple of pages at a time. Perfect for specific questions, but terrible for big-picture architectural work.

ChatGPT just pattern-matches based on code it saw during training and has no feedback loop to know if stuff actually runs in the real world

Cannot execute tests

ChatGPT generates code but has literally no clue if it actually works. Last week, it gave me some test code that looked perfect. The syntax was right. Logic seemed sound. Compiled without errors. Then I ran it, and nothing worked.

ChatGPT just pattern-matches based on code it saw during training and has no feedback loop to know if stuff actually runs in the real world.

Potential for incorrect or outdated code

ChatGPT’s training data has a cutoff date. So it might give you code for an old version of a framework, use deprecated APIs, or recommend patterns that are no longer best practice or even work anymore. It can also generate plausible-looking code that’s subtly wrong. Always verify everything before trusting it.

Security and privacy concerns

Pasting your company’s code into ChatGPT means sending it to OpenAI’s servers. For organizations with strict data policies, this is a nonstarter. Some companies explicitly prohibit using ChatGPT with any production code or proprietary information.

Check your organization’s policies before you accidentally leak something sensitive in a ChatGPT prompt.

Cannot replace testing expertise

ChatGPT handles mechanical tasks well. But it doesn’t replace the actual thinking that goes into designing a good test strategy. Things like what should be tested or why does a particular edge case matter or how do you know if your tests are actually effective and not just passing because you wrote them wrong—those questions require human expertise. Real experience.

ChatGPT is a tool that makes experienced testers more productive. It doesn’t magically turn beginners into experts.

Best practices for using ChatGPT in test automation

Let’s go over some best practices for incorporating ChatGPT into test automation.

Be really specific about what you want

Vague prompts get you vague code. “Write a login test” gets you something generic that probably won’t work for your app. Instead, be detailed. The more context you provide—framework, specific behaviors, edge cases—the better the output you get.

Think of it like delegating to a junior developer who’s smart but doesn’t know anything about your project. You gotta spell it out.

Never trust it blindly

Always review ChatGPT’s code before using it. Check that assertions actually validate what they’re supposed to. Verify selectors make sense. Look for error handling. Make sure it follows your team’s conventions. Then actually run the tests and confirm they work. Don’t skip this step.

Use it for scaffolding, then customize

ChatGPT gives you a starting point, not a finished product. Let it generate the test structure, imports, setup, teardown, and basic flow. Then you go in and add your business logic, your specific assertions, your actual selectors from your app. Refine it until it’s testing what actually matters for your use case.

Iterate when it’s not quite right

Working with ChatGPT might involve some back-and-forth and tuning the prompts over multiple iterations to get exactly what you want.

As James Marcus Bach mentions in Lessons Learned in Software Testing: A Context-Driven Approach, “If you have bad tests, automation can help you do bad testing faster.” If you do not iterate and fix the bad tests, you’d end up with more slop which would then be an actual developer’s headache to fix!

Combine ChatGPT with purpose-built testing tools

ChatGPT can help you write tests faster, but it cannot maintain tests when the app changes or catch visual regressions, or integrate with CI/CD. This is where purpose-built platforms come in. Tricentis’s AI-powered testing solutions handle the stuff ChatGPT can’t.

ChatGPT writes tests faster. Tools like Tricentis help you maintain them at scale and actually get value from your testing investment. Both have their place.

Keep up with how AI changes

AI tools evolve fast. Stay informed about new features, updated models, and emerging best practices. What works well now might change. What doesn’t work now might become possible. Adjust your approach as the technology improves.

By understanding all the capabilities of AI tools, QA teams can significantly enhance their productivity without compromising on quality

The bottom line

ChatGPT is genuinely useful for test automation when used appropriately. It accelerates test development, reduces learning curves for new frameworks, assists with documentation, and helps with debugging.

But it’s not magic! It will probably not replace your entire QA team or solve all your testing woes. It doesn’t have the ability to understand your application end-to-end. It can’t execute tests, and sometimes generates code that looks good but actually doesn’t work when you run it.

Use ChatGPT as an AI assistant that handles repetitive tasks and provides coding support, but always rely on your team’s expertise for team strategy, coverage decisions, and quality assessment.

And when you need enterprise-scale test automation with AI capabilities that go beyond code generation and extend to self-healing tests, risk-based optimization, and comprehensive platform support, explore solutions built specifically for these use cases.

By understanding all the capabilities of AI tools, QA teams can significantly enhance their productivity without compromising on quality. That’s the sweet spot!

Ready to explore AI-powered testing beyond code generation? Learn more about Tricentis’s AI-powered testing solutions and see how intelligent test automation can transform your testing practice and not just make you write code faster.

This post was written by Deboshree Banerjee. Deboshree is a backend software engineer with a love for all things reading and writing. She finds distributed systems extremely fascinating and thus her love for technology never ceases.