AI for API Testing: How I Used AI and Star Trek to Generate Better Test Cases

If you’re in the world of API testing, and even if you’re not, it’s very clear what you need to test.

The cool thing about APIs is that they are well-defined. REST APIs or GraphQL have schemas based on HTTP. gRPC is another protocol that has a schema definition for each case. These are contracts. Contracts are for letting a client know how to talk to your software. Since these clients are other computers, they need something they can comprehend.

So, who’s better at understanding schema and protocol definitions than computers? And who are these new computers that are taking over our lives?

Our Soon-To-Be-Overlord – AI!

This is where the concept of AI for API testing gets really exciting. If these models understand the language of APIs, we can ask them questions about test cases!

Let’s try, shall we?

Putting AI to the Test: An API Testing Experiment

Here’s a live example. There’s a site called stapi.co, which is a database about everything Star Trek. And of course, it has APIs to read from it.

So I went to my test subject, in this case, Claude Sonnet, and gave it this prompt:

I asked it to suggest test cases for a single API—the POST for searching characters—and I gave it the schema definition.

I’m guessing I’m not the first to ask, because the answers were good. The first one was about basic search, but the second suggestion zeroed in on a classic API testing challenge.

The schema defines pagination, but note the AI’s nuance: it didn’t just say test pagination, it specified testing it with large result sets.

Obviously, our model knows a critical part of modern API testing involves handling large amounts of data. It correctly directs you to where the real risk is. If you’ve been testing APIs for a while, you’d say—of course, that’s the interesting part. But for junior testers just looking at the API structure, this kind of guidance is invaluable.

Exploring a Core Tenet of API Testing: Error Handling

Let’s look at another suggestion that gets to the heart of robust API testing.

Error handling is code for “what happens when we feed the API the wrong food.” We might think about sending invalid inputs, but the AI correctly focuses on assessing the system’s reaction. Learning how the API behaves when things go wrong is a crucial part of our testing strategy.

There were also suggestions on using special characters, unicode, and even partial text searches. I really liked the partial text match suggestions because I did them as part of my “Exploration with Postman webinar” and even found a bug there!

More Confidence: Generate Contextual Test Data

I was impressed, but I wanted more. A good API testing plan needs concrete examples. So I gave it a follow-up prompt:

Give me concrete examples, gosh darn it!

So I got examples of searching for Kirk, Spock, and Picard. But then it got more interesting. For special characters, it suggested searching for T'Pol. For a name containing punctuation, it gave Worf, son of Mogh.

Another example was for testing recurring names—and this is for the Voyager fans—searching for Paris, which could be either Tom Paris or his father, Owen.

If you’re a Trekkie like me, you’d say these are good examples. But if you look at it from an API testing perspective, you’ll be even more impressed.

What do we mean by good examples? They are taken from the world of the tested system. Sure, I could invent Klingon-sounding names, but seeing real examples from the Star Trek universe in my tests gives me more confidence. When the tests pass, I trust the result. And if they fail, I’d know they definitely shouldn’t fail for this case.

The more your AI buddy knows about the system’s world, the better its suggestions for test cases will be, resulting in tests you trust more.

Beyond the Final Frontier: General API Testing Wisdom

Other suggestions were around schema structure, API versioning, and concurrent execution—topics that are less about Star Trek and more from the core playbook of professional API testing.

Not a bad start, right?

Yes, I’m sure you would have thought about all these yourself with your experience. And with more experience, you can probably come up with even more interesting cases. That’s okay. We’re not counting on our AI minion to do all the work for us.

But using AI for API testing, even for a little help, can not only save us time but also give us ideas we haven’t thought of, or ones we had but haven’t tested in a while.

Here’s my challenge to you: The next time you’re planning an API testing session, open your favorite LLM and give it a prompt. You aren’t replacing your expertise; you’re adding a powerful co-pilot to your QA process.

What’s the most surprising or useful test case an AI has suggested to you? Share your experience in the comments below!