API design-first principles and automated testing with Apidog: a detailed guide

APIs are the backbone of modern software. 

They power everything from simple mobile apps to huge enterprise systems and enable smooth communication between different services. 

But designing and maintaining APIs isn’t easy.

You have to ensure consistency across API endpoints, manage changes without breaking integrations, and run reliable tests to catch any issues early.

And this is where Apidog comes in. 

With its design-first approach and powerful automation tools, Apidog will help you build, test, and maintain APIs more efficiently. 

In this article, we’ll explore how Apidog simplifies API development, from mocking responses to automating tests.

Let’s dive in!

The design-first approach to API development

Most teams build APIs the wrong way. 

They jump straight into coding and figure out the API structure as they go. This leads to inconsistencies, missing fields, and constant rework.

A design-first approach solves this. 

Instead of treating API design as an afterthought, you define your API before writing a single line of code. This ensures clarity, consistency, and fewer mistakes.

Apidog makes API design structured and repeatable. Here’s how:

• Define requests and responses upfront – Every API call is documented before implementation.
• Reuse objects across multiple APIs – Keeps data structures consistent.
• Mock responses instantly – Frontend teams can start testing without waiting for backend development.
• Generate API documentation – Ensures non-Apidog users stay aligned.

Imagine a payments API. You start with a simple structure:

{
  "transaction_id": 123456,
  "amount": 50.25,
  "currency": "USD"
}

But, a few months later, another developer modifies it:

{
  "transactionId": "123456",
  "amount": 50.25,
  "currency_code": "USD"
}

Small changes like renaming fields or switching data types can break integrations. If the frontend team isn’t aware, their code crashes.

With Apidog, these changes can’t go unnoticed. It enforces predefined structures and highlights inconsistencies before deployment.

A design-first workflow removes uncertainty. Instead of guessing how an API should behave, you have a clear blueprint for API development:

Here’s why that matters:

• Better collaboration – Backend, frontend, and QA teams speak the same language.
• Fewer errors – Strict request/response definitions prevent inconsistencies.
• Faster development cycles – Mock servers let frontend teams work independently.

In short, it removes dependencies, speeds up development, and ensures everyone is on the same page.

And that’s key to successful development.

Benefits and ROI of using Apidog

APIs are mission-critical. A single failure can break features, crash your app, or disrupt business operations.

Apidog helps teams build, test, and maintain APIs faster while reducing costs and errors. 

Without a structured approach, API development is messy and slow. 

You waste time fixing inconsistent endpoints, handling late-stage changes, and manually debugging issues.

Let’s say you have a fintech app and need a loan approval API. Without Apidog, the frontend and backend teams wait for each other, slowing development.

But, with Apidog, they:

• Define the API contract upfront – Agree on a structure before coding.
• Use mock servers – Frontend developers can test the UI while backend developers build the API
• Automate testing – You can verify every update instantly

So, instead of two months of back-and-forth, the API is ready in three weeks.

Another major benefit of using Apidog is improved test coverage.

Manually testing APIs isn’t scalable. Developers too often test only happy-path scenarios and miss edge cases.

But, with Apidog, you can ensure you test every scenario and automate:

• Regression tests – Catch breaking changes early.
• Error handling tests – Verify how APIs behave under failure conditions.
• Dynamic variable tests – Ensure responses match expected formats.

Also, Apidog prevents unexpected contract-breaking updates.

Now, APIs evolve, that’s undeniable – but these changes must be controlled.

Let’s say you have a banking API.

It previously returned:

{
  "balance": 1500.75
}

After an update, it accidentally changed to:

{
  "balance": "1500.75"
}

Your app’s frontend will now break because it receives a string when it expects a number.

But, Apidog’s schema validation will immediately flag the issue and prevent a major production failure.

Another major plus of using Apidog is better collaboration between different teams.

API development requires constant coordination between frontend, backend, and QA teams. 

And if they don’t have a single source of truth to go on, they’ll work slower and unnecessarily waste time.

With Apidog, everyone can:

• Work from the same API definition – No more confusion over changes.
• Use mock servers to test early – Frontend and backend can work in parallel.
• Share test reports – Instant visibility into API failures.

This means that they’re on the same page from day one.

And from our experience, this doesn’t just improve efficiency – it saves real money.

Here’s a couple of our findings after using Apidog:

• Regression testing time drops from 30-40 minutes to 3 minutes
• Significant drop in effort needed when manually testing
• Fewer productions bugs, which means lower post-release support costs

If you handle frequent API updates, these savings add up fast.

And that’s a pretty compelling reason to try Apidog out.

How to design, test, and debug APIs with Apidog

Next, we’ll show you how to use Apidog to build reliable APIs.

Use mock servers for front-end development

APIs and frontend development don’t always move at the same speed. 

Backend teams need time to build, test, and deploy APIs. Meanwhile, frontend teams need something to work with right now.

Mock servers fix this problem. A frontend team can develop and test against realistic API responses without waiting for the backend to be finished.

Picture this: you’re building a travel booking app. 

The frontend team needs to display flight details, pricing, and user reservations. But the backend isn’t ready yet.

Without a mock server, the frontend team has no way to test real interactions and has to wait for the backend team to finish, which significantly slows down development.

But, with a mock server:

• The frontend gets real-looking data
• You can start testing immediately
• You will iterate and get feedback faster

So, how does this work?

It’s simple. Apidog allows you to define API responses and serve them as if they were real. So, you can:

• Generate realistic data – Use regex patterns to return structured, dynamic responses.
• Customize API behavior – Simulate different scenarios (success, failure, timeouts).
• Start testing immediately – You use the mock API just like you would the real one.

And this can be a lifesaver if you’re doing regular demos.

Demos show progress, keep everyone on the same page, and keep stakeholders engaged. But showing half-finished screens with broken API calls? Not a good look.

Mock servers make sprint demos feel real. 

You can show a working app with realistic responses, demo multiple scenarios, and get early feedback.

In short, don’t think of mock servers as just a temporary fix. 

They’re a core tool you need for faster, better software development.

Automate API testing with Apidog

APIs don’t break on their own. But even a small change in an endpoint can trigger unexpected failures across multiple services.

Manual testing can’t keep up. It’s slow, repetitive, and prone to human error. 

Apidog automates key parts of the testing process, helping you catch issues faster and reduce manual effort. With it, you can:

• Organize and execute tests – Run multiple test cases in a structured way.
• Get real-time feedback – See test results instantly, debug on the spot.
• Use custom scripts – Go beyond basic assertions with JavaScript preprocessing and postprocessing.
• Perform regression testing – Catch unexpected changes in minutes.

Let’s say a subscription billing service updates its API and you assume nothing changed in the response format.

But when an Apidog test runs, it flags this response change:

{
  "user_id": 123,
  "plan": "premium",
  "status": "active"
}

After:

{
  "userId": "123",
  "planType": "premium",
  "account_status": "active"
}

One small change. Three broken integrations.

But, because Apidog caught it early, you can fix the issue before it reaches production.

However, not all API tests are this simple. 

Sometimes, you need dynamic testing that can handle changing data between requests – like session tokens, timestamps, or randomized values.

This is where Apidog shines. 

With custom scripts, you can generate dynamic inputs, store values, and reuse them across multiple test cases. 

This flexibility means no testing scenario is out of scope, giving you full control over how you validate your API.

Let’s say you’re testing a hotel booking API.

Instead of hardcoding values, a preprocessing script generates a unique booking request:

let checkIn = "2024-06-01";
let checkOut = "2024-06-05";
let reservation = {
  "guest_name": "John Doe",
  "room_type": "Deluxe",
  "check_in": checkIn,
  "check_out": checkOut
};
pm.environment.set("reservation", JSON.stringify(reservation));

Each test run creates a fresh request with unique values.

Once a booking is made, you want to extract and validate the confirmation details:

let response = pm.response.json();
pm.test("Check reservation details", function () {
    pm.expect(response.room_type).to.eql("Deluxe");
    pm.expect(response.check_in).to.eql(pm.environment.get("checkIn"));
});

This ensures that the API returns correct, expected data.

And that’s key if you want to ship faster and with confidence.

Handle errors and debug APIs

APIs fail in unexpected ways. A missing field, a timeout, or an invalid response can break your entire system.

Catching these errors early saves a lot of time and money.

Apidog helps you debug faster with detailed logs, structured error messages, and try-catch handling.

Some errors don’t crash tests but still cause failures later. A try-catch block ensures that even unexpected issues are logged and understood.

Here’s an example – an e-commerce API processes thousands of orders per minute. And a product’s price should always be a number.

You run a test, and the API response suddenly looks like this:

{
  "order_id": 56789,
  "total_price": "99.99"
}

The price is a string instead of a number. And without proper handling, the test fails without explanation.

A try-catch block logs what went wrong:

try {
    let response = pm.response.json();
    pm.expect(response.total_price).to.be.a("number");
} catch (error) {
    console.error("Data type mismatch:", error);
}

Now, instead of getting a generic test failure, the console shows you exactly what happened.

And that’s important because generic error messages waste time. A message like “Test failed” means nothing on its own.

That’s why Apidog provides clear, structured error messages that explain why a test failed.

Here’s another example – a financial API requires authentication tokens. So, the system should reject requests with an expired token.

An automated test sends a request and expects this error:

{
  "error": "Token expired"
}

Instead, the API returns:

{
  "error": "Unauthorized"
}

A vague “Unauthorized” message like this makes debugging harder, since the expected failure isn’t happening correctly.

Apidog’s error logs highlight the issue immediately:

Expected: "Token expired"  
Received: "Unauthorized"

No guesswork. The problem is instantly clear.

Also, sometimes seeing what happens step by step makes debugging much easier. 

Apidog’s console logs track every API call, request, and response.

For example, a payments API confirms successful transactions through webhooks. A test runs, but the webhook doesn’t trigger.

Instead of leaving you blindly guessing, the logs show:

[INFO] Sending payment request...  
[SUCCESS] Payment processed.  
[ERROR] Webhook failed - response status: 500 

The webhook call returned a server error. So, the issue is on the backend, not in the test itself.

And with this insight you can fix the problem immediately.

Also, some errors aren’t obvious. Sometimes, multiple teams need to work together to fix them.

Apidog makes debugging a team effort by:

• Sharing test logs – Letting developers and testers see the same error reports.
• Flagging inconsistencies – Detecting when API responses change.
• Providing real-time feedback – Allowing immediate test reruns after fixes.

In a nutshell, if you use Apidog, debugging isn’t a bottleneck anymore.

Use advanced API testing techniques

Basic API tests catch simple issues. But APIs are complex. They handle dynamic data, large payloads, and edge cases.

That’s where advanced testing techniques come in. Apidog provides powerful scripting tools so you can handle even the trickiest scenarios.

A great example are custom scripts for complex validations.

Basic assertions will work for simple checks. But real-world APIs require more flexibility.

Let’s say you need to validate a hotel reservation API.

The API returns a list of available rooms. Each room should have a price, type, and availability status.

Basic assertions can’t check every object in the response. However, a custom script can:

let rooms = pm.response.json().rooms;
rooms.forEach(room => {
    pm.test("Room has valid details", function () {
        pm.expect(room.price).to.be.a("number");
        pm.expect(room.type).to.be.a("string");
        pm.expect(room.available).to.be.a("boolean");
    });
});

Instead of manually verifying every response, Apidog automates it.

Also, Apidog will help you handle JavaScript number limitations.

JavaScript can handle large numbers using BigInt, but JSON doesn’t support it. That’s why many backends return large numbers as strings.

For example, a banking API might return transaction IDs like this:

{
  "transaction_id": 9876543210123456789
}

Since JSON lacks BigInt support, it rounds the number to:

{
  "transaction_id": 9876543210123457000
}

This breaks transaction tracking. So, what’s the solution?

Storing large numbers as strings. Apidog allows you to store values as strings to preserve accuracy:

let response = pm.response.json();
pm.environment.set("transaction_id", response.transaction_id.toString());

This ensures your tests check the correct transaction.

Apidog also helps with response structure validation.

Now, APIs change over time. New fields get added. Old ones get removed. And a small change can break integrations.

A payments API used to return this:

{
  "user_id": 123,
  "plan": "premium",
  "status": "active"
}

Now, the response has changed:

{
  "userId": 123,
  "subscription": "premium",
  "account_status": "active"
}

Your frontend will expect the old structure. If you don’t update it, it will fail.

A response validation script will help you catch this:

let response = pm.response.json();
pm.test("Check API response structure", function () {
    pm.expect(response).to.have.property("user_id");  // This will fail
});

This way, you can flag the issue before it reaches production.

Remember, automated testing is good. But, smart testing is better, and Apidog makes it possible.

Integrate Apidog into your development pipeline

APIs don’t exist in isolation. They are part of a bigger system – frontend apps, backend services, and CI/CD pipelines.

CI/CD pipelines ensure that every code change is tested before deployment. APIs must pass these tests to prevent breaking other services.

Automating API testing in the pipeline will help you catch issues before they hit production. 

And with Apidog, you can do this without slowing down releases.

Let’s say you update a banking API to support new currency types. The backend team then pushes the change, assuming it’s safe.

But Apidog’s automated pipeline test flags an issue:

[TEST FAILED]  
Expected: "currency": "USD"  
Received: "currency": {"code": "USD", "symbol": "$"}  

Here, a small format change broke existing integrations. But, instead of learning this after deployment, you can catch the issue before it affects users.

But, automated testing isn’t always easy. You might face:

• Restricted API access – Security policies might block external testing tools.
• Flaky tests – Unstable environments which cause false failures.
• Slow test execution – Large test suites which delay deployments.

The solution? Mocked test environments.

If you replace real data with test values, you can keep tests running without exposing sensitive data.

Also, you can use Apidog to validate response formats in the pipeline.

APIs evolve over time. But response formats must always stay consistent.

Apidog validates your API contract in every pipeline run. And if the structure changes unexpectedly, it instantly flags the issue.

So, instead of breaking your app, you can fix the issue before deployment.

APIs power critical business functions. A single breaking change can cause downtime, loss of revenue, or security risks.

Testing APIs before they go live reduces the risk of expensive failures. While no test can catch everything, early testing will help you identify critical issues before they reach production.

And Apidog makes that process fast, reliable, and repeatable.

Conclusion

Building and maintaining APIs is never just a one-and-done process. 

APIs evolve, requirements change, and unexpected issues pop up. 

The key isn’t just getting them to work – it’s making sure they stay reliable in the long run.

Apidog helps you do exactly that. No more firefighting last-minute bugs or dealing with broken integrations.

With the right approach, you can turn API development from a bottleneck into a competitive advantage.

After all, the teams that build, test, and iterate faster are the ones that win.