How to Improve Development Efficiency with Apidog's AI Test Case Generation

Writing API test cases is tedious work.

Happy path tests are manageable, but once you start thinking about error cases and edge cases, you're like "when is this ever going to end..." When you see an API with 10 parameters, the combinatorial explosion alone gives you a headache.

That's when I discovered Apidog had this "AI-powered automatic test case generation" feature. Honestly, I was skeptical at first—"this probably won't work"—but after trying it, it turned out to be way more practical than I expected.

The Walls I Keep Hitting When Writing Test Cases

Every time I write API tests, I get stuck on the same things:

• No clear criteria for when I can say "I've tested this properly"
• Spec changes happen, and updating test cases falls behind and gets postponed
• Everyone on the team writes tests differently, making reviews a pain
• Error cases and edge cases get the "I'll write those later" treatment, then I forget about them

The most frustrating part? Those detailed scenarios like "what if this parameter is null? What if it's an empty string? What about negative values?" If you seriously think through all of them, it's endless. But if you cut corners, it bites you in production.

If AI could create a draft, it would at least give me a starting point for "what should I be thinking about?" That's why I gave it a shot.

Simple to Use: Just Click a Button to Start Generation

After defining your API, open the "Test Cases" tab. There's a button in the middle of the screen that says "Generate with AI"—click it.

Then you get a screen where you can choose what types of tests to generate:

• Normal cases
• Error cases
• Boundary values
• Security

You can select all of them or just pick what you need. I started by selecting everything just to see what would happen.

Once generation finishes, you get a whole list of test cases. Go through them one by one—keep the useful ones by clicking "Accept," and remove the questionable ones with "Delete." Save just the ones you like and you're done. You can also export the test report to share with your team.

Things to Watch Out For

After actually using it, here are some things you should be aware of:

• Always review after AI generation If you use them as-is, you might get cases that don't match your business logic.
• Generation quality depends on the AI model Using high-performance models (like GPT-4 or Claude) gives you more practical cases.
• AI features are off by default First time using it, you need to enable it in the settings.
• Error cases and edge cases aren't perfect AI won't cover everything, so review is essential.

Setup Required: You Need an API Key

Apidog doesn't provide its own AI model. So you need to get an API key from an external AI service like OpenAI or Claude and configure it yourself.

The important thing here is that the quality of generated test cases depends on the model's performance. I tried using Gemini 3 Pro and got pretty practical cases. With free models, you might get more generic content.

My Impressions After Using It

Generated Content is Just a "Draft"

Using AI-generated test cases as-is is risky. You'll definitely need to adjust them to match your business logic. But it's way faster than starting from scratch.

Time Savings Are Real

You get 10-20 drafts in a few minutes. What would take 30+ minutes to write by hand took about 15 minutes including review. That's huge.

Not Perfect, But Practical

Especially for complex business logic cases, AI alone can't cover everything. But for basic happy paths and common error cases (null checks, type errors, etc.), it's totally usable.

How I Use It

Here's how I'm using it now:

• At project start or major changes: Use AI to create drafts and get the big picture
• Core logic sections: Write detailed tests manually
• Review and adjustment: Go over AI-generated cases and modify them to match business logic
• CI/CD integration: Incorporate generated cases into automated tests for continuous execution

What's especially convenient is that I spend less time thinking "what test cases does this API need?" Looking at the cases AI generates, I often realize "oh, I need this one too."

Conclusion: The Era of Building Tests Together with AI

What I realized from using this is that it's not about letting AI do everything, but collaborating with AI to build tests.

AI creates the draft, so I can focus on verifying specs and business logic. As a result, both test quality and work speed improved.

If you're spending too much time writing API tests, or if thinking through error cases and edge cases feels like a chore, this is worth trying. It's not perfect, but it definitely improves efficiency.

If you found this article helpful, please share it. If your team has similar challenges, let me know in the comments. Let's figure out solutions together!