DEV Community: Maesi

Your Test Suite Shouldn't Depend on APIs You Don't Control

Maesi — Sun, 07 Jun 2026 10:00:00 +0000

You know this feeling. A test passes locally, fails in CI, you rerun it, it passes again. Somewhere a third-party API was slow, or returned something unexpected, or was just briefly down. You move on, but it happens again next week.

And that's before you even get to the scenarios you can't test at all: rate limits, timeouts, malformed responses. You can't make a live API produce those on demand. So those code paths go untested.

Mocking solves both problems. Mokapi gives you a spec-validated mock server driven by your OpenAPI or AsyncAPI specs. Every response conforms to the contract. Every request gets validated. Your tests run against something you fully control.

The CI Setup

Mokapi runs in Docker during your test job. Your app points at it instead of the real API. That's the whole switch.

- name: Start Mokapi
  run: |
    docker run -d --name mokapi \
      -p 80:80 \
      -v ${{ github.workspace }}/mocks:/mocks \
      mokapi/mokapi:latest /mocks
    sleep 5

- name: Run Tests
  run: npm test

- name: Stop Mokapi
  run: docker stop mokapi

Your mock specs live in /mocks in the repo, versioned alongside the code. When the API contract changes, the spec changes, the mock changes, and the tests run against the updated contract automatically.

Simulating the Scenarios That Matter

This is where it gets useful beyond just removing the external dependency. With Mokapi's JavaScript API you can control behavior at runtime without restarting anything.

import { on, sleep } from 'mokapi'

export default () => {
  let delay = undefined

  on('http', (request, response) => {
    if (request.key === '/simulations/delay') {
      switch (request.method) {
        case 'PUT':
          delay = request.query.duration
          break
        case 'DELETE':
          delay = undefined
          break
      }
    }
  }, { track: true })

  on('http', (request, response) => {
    if (delay) {
      sleep(delay)
    }
  }, { track: () => delay !== undefined })
}

Your test sends a PUT to enable a delay, runs the scenario, sends a DELETE to reset. Same pattern works for forcing 500s, 429 rate limits, or any other condition you want to test against.

The track: true on the first handler is worth understanding. Mokapi's dashboard shows every HTTP request with the response and all JavaScript handlers that ran for it. The first handler only updates internal state without touching the response, so Mokapi wouldn't know it was relevant unless you tell it explicitly. track: true keeps it visible in the dashboard, which matters when you're debugging why a test behaved unexpectedly.

What You Actually Get

Consistent test results because nothing outside your control can break them. Coverage of error scenarios that a live API can't reproduce on demand. Faster pipeline runs with no network latency. And because Mokapi validates against the spec, contract violations get caught in CI before they reach production.

Full Walkthrough

The complete guide on mokapi.io covers the full GitHub Actions setup, the JavaScript simulation API, and how to structure your mock specs for a real project.

👉 Read the full guide here

Happy to answer questions in the comments.

API Contracts Break Silently. Here's How to Catch Them the Moment They Do.

Maesi — Sat, 06 Jun 2026 13:00:00 +0000

A backend developer renames a field. The tests pass. It ships. Three days later the mobile app is crashing and nobody knows why.

This is how API contract drift works. It's not a discipline problem. It's a tooling problem. When nothing enforces the contract at runtime, you're relying on everyone to manually keep specs, code, and clients in sync across multiple teams and deploy cycles. That doesn't scale.

I built a validation layer into Mokapi to fix this. The idea is simple: Mokapi sits between your client and backend, validates every request and response against your OpenAPI spec, and gives you an immediate, actionable error the moment something violates the contract.

The Forwarding Script

One JavaScript file is all it takes:

import { on } from 'mokapi'
import { fetch } from 'mokapi/http'

export default async function() {
  on('http', async (request, response) => {
    const url = getForwardUrl(request)

    if (!url) {
      response.statusCode = 500
      response.body = 'Failed to forward request: unknown backend'
      return
    }

    try {
      const res = await fetch(url, {
        method: request.method,
        body: request.body,
        headers: request.header,
        timeout: '30s'
      })

      response.statusCode = res.statusCode
      response.headers = res.headers

      const contentType = res.headers['Content-Type']?.[0] || ''
      if (contentType.includes('application/json')) {
        response.data = res.json()  // triggers response validation
      } else {
        response.body = res.body
      }
    } catch (e) {
      response.statusCode = 500
      response.body = e.toString()
    }
  })

  function getForwardUrl(request) {
    switch (request.api) {
      case 'backend-1':
        return `https://backend1.example.com${request.url.path}?${request.url.query}`
      case 'backend-2':
        return `https://backend2.example.com${request.url.path}?${request.url.query}`
      default:
        return undefined
    }
  }
}

request.api contains the info.title from your OpenAPI spec and is used here purely for routing to the right backend URL.
The choice between response.data and response.body is what controls validation: setting response.data tells Mokapi to validate the response against the spec schema, while setting response.body skips validation entirely. So for JSON responses you want validated, use response.data. For anything else, response.body passes it through as-is.

No changes to your backend. No changes to your client. Just point the client at Mokapi instead.

What It Actually Catches

More than just a renamed field. Mokapi validates:

Request method, URL structure, headers, query parameters
Request body: required fields, data types, format constraints
Response status codes, headers, and body structure

A backend that starts returning a string where the spec says number. A client sending a request body missing a required field. A response body that doesn't match the documented schema. All caught at the boundary, with a message that tells you exactly what violated what.

Where to Use It

It's the same script in every context. Between frontend and backend during local development. Between microservices in CI. In Playwright tests so your test suite becomes a contract test suite automatically. In Kubernetes preview environments before changes reach staging.

Full Walkthrough

The complete guide on mokapi.io covers the full setup.

👉 Read the full guide here

Happy to answer questions in the comments.

Testing Email Workflows Without Email Server — With Playwright & Mokapi

Maesi — Fri, 05 Jun 2026 22:07:45 +0000

Email is one of those things that's genuinely hard to test. It goes out through an SMTP server, lands in a real inbox, and you have no programmatic way to check what arrived. So most teams either test it manually or just assert that sendEmail() was called and call it done.

Neither tells you if the subject line was right, the link was valid, or the HTML rendered correctly.

That's why I built email support into Mokapi. Your backend connects to Mokapi's SMTP server exactly as it would a real mail server. Mokapi captures the message. Your test fetches it over HTTP and asserts on the content. No real emails, no inbox polling, no external dependencies.

The Config

Unlike Kafka or HTTP, email doesn't have a standard specification format. So Mokapi uses a simple YAML config. This is all you need:

mail: '1.0'
info:
  title: "Email Workflows"
servers:
  smtp:
    host: :2525
    protocol: smtp

Point your backend at localhost:2525 and Mokapi starts capturing everything.

Want IMAP too, so you can preview emails in a real mail client during development? Add one line:

  imap:
    host: :1430
    protocol: imap

The Playwright Test

The test drives a real signup form, then fetches the captured email from Mokapi's HTTP API and asserts on it.

test('Email verification after signup', async ({ page, request }) => {
  const recipient = randomEmail();

  // Drive the signup form
  await page.goto('');
  const form = page.getByRole('form', { name: 'Sign Up' });
  await form.getByLabel('Email').fill(recipient);
  await form.getByLabel('Password').fill('SuperSecure123!');
  await form.getByRole('button', { name: 'Sign Up' }).click();
  await expect(form.getByText('Check your inbox to verify your email')).toBeVisible();

  // Fetch the email from Mokapi
  const mails = await request.get(
    `http://localhost:8080/api/services/mail/Email%20Workflows/mailboxes/${recipient}/messages?limit=1`
  );
  const mailList = await mails.json();
  expect(mailList[0]).toEqual(expect.objectContaining({
    subject: 'Verify your email address',
    from: expect.arrayContaining([expect.objectContaining({ address: 'noreply@example.com' })]),
    to: expect.arrayContaining([expect.objectContaining({ address: recipient })])
  }));

  // Check the body and verification link
  const res = await request.get(
    `http://localhost:8080/api/services/mail/messages/${mailList[0].messageId}`
  );
  const mail = await res.json();
  expect(mail.body).toContain(`verify?email=${encodeURIComponent(recipient)}`);
});

Random email per test run keeps things isolated. Playwright's request object handles the Mokapi API calls directly, so the same test that drives the browser also inspects the email. No extra HTTP client needed.

The IMAP Bonus

Automated assertions catch broken content. But they don't catch a layout that looks terrible in Outlook. Because Mokapi supports IMAP, you can connect any real mail client to localhost:1430 during development and see exactly what your users will see. It's the visual check your test suite can't do.

Full Walkthrough

This is the short version. The complete guide on my site covers the full config, the Express backend, all the Playwright helpers, and how it all fits together.

👉 Read the full guide here

Working repo: mokapi-email-workflow

Happy to answer questions in the comments.

Testing Kafka Workflows Without Kafka — With Playwright & Mokapi

Maesi — Wed, 20 Aug 2025 20:30:38 +0000

Event-driven systems are powerful — but testing them can feel heavy.

In event-driven systems, the contract between producers and consumers is everything. A field gets renamed. A schema evolves. A new required property sneaks in. Any of those can silently break a downstream service, and you won't find out until something blows up in production.

That's the problem I built Mokapi to solve. Define your Kafka topics in an AsyncAPI spec, and Mokapi enforces that contract automatically. Every message gets validated against the schema, no matter where it comes from. Change the spec and run your tests. If anything is out of line, you'll know immediately.

Because Mokapi acts as a real Kafka broker on localhost:9092, your service doesn't need to change at all. No special test mode. No stubbed clients. Just your actual code running against a mock that holds it to the contract.

The Idea Behind Mokapi

Mokapi lets you define Kafka topics with an AsyncAPI spec and interact with them over a simple REST API. No broker. No Zookeeper. Your service connects to localhost:9092 exactly like it does in production, but Mokapi is answering. The application code doesn't change at all.

In this article I'm using Mokapi's HTTP API to inject test messages, but Mokapi actually supports several other ways to produce messages too, including a JavaScript API and OpenAPI-backed endpoints with custom handlers.

And in all cases, Mokapi validates every message against your AsyncAPI schema by default. You're not just checking that a message arrived, you're checking it's valid according to the contract.

A Real Workflow Test

Here's the scenario: a backend consumes a command from document.send-command, processes it, and publishes a result to document.send-event. The test acts as the foreign system that sends the command and verifies the outcome.

I use Playwright as the test runner (yes, the browser tool, but it's great for async workflows even without a browser).

test('Kafka document send workflow', async () => {
  const documentId = 'doc-' + Date.now();
  let startOffset = -1;

  await test.step('Record current offset', async () => {
    startOffset = await getPartitionOffset(TOPIC_EVENT, 0);
  });

  await test.step('Produce command', async () => {
    await produce(TOPIC_COMMAND, {
      key: documentId,
      value: { documentId, recipient: 'alice@mokapi.io', ... }
    });
  });

  await test.step('Wait for event and assert', async () => {
    let record;
    const timeout = Date.now() + 5000;

    while (Date.now() < timeout && !record) {
      const records = await read(TOPIC_EVENT, 0, startOffset);
      record = records.find(x => x.value.documentId === documentId);
      if (!record) {
        startOffset += records.length;
        await new Promise(res => setTimeout(res, 200));
      }
    }

    expect(record).not.toBeNull();
    expect(record.value.status).toBe('SENT');
  });
});

The offset tracking is the thing people miss the first time. Without it, you might pick up a stale message from a previous run and get a false positive. Record the offset before you produce, then only read from there.
The backend itself runs completely unchanged, connecting to localhost:9092 as normal. It doesn't know Mokapi is there. That's kind of the whole point.

Why I Think This Matters

The backend isn't mocked. Your actual consumer and producer code runs against a spec-validated broker. You're testing your logic, not simulating it. And because Mokapi starts in seconds with no infrastructure dependencies, this works fine in CI without any special setup.

Full Walkthrough

This is the short version. I wrote a complete guide on my site covering the full AsyncAPI spec, the Node.js backend, all the Playwright helpers, and how to extend this pattern to more complex workflows.

👉 Read the full guide here

Working repo: mokapi-kafka-workflow

Happy to answer questions in the comments if you run into anything.

Acceptance Testing with Mokapi: Focus on What Matters

Maesi — Tue, 08 Jul 2025 11:24:48 +0000

In today’s fast-paced development cycles, it’s crucial to ensure your software meets real user expectations. While unit tests validate internal code, acceptance testing answers the bigger question: Does the system behave as users expect? This post explores the value of acceptance tests and how Mokapi supports it by simulating APIs, validating specifications, and enabling robust, realistic test cases.

Why Acceptance Testing

Software testing is not merely a box to check—it is a fundamental process to answer one critical question: Is our software releasable?

Among the various levels of testing, acceptance testing offers the most direct insight into whether the software meets business and user expectations. It bridges the gap between the world of users and the inner workings of the code by turning expectations into precise, executable checks on system behavior.

The Nature of Acceptance Tests

At its core, an acceptance test is an executable specification of how a system should behave, written from a user’s perspective. It is not concerned with how the system is implemented, but with what it does—its behavior and its outcomes.

While unit tests focus on individual components, acceptance tests focus on the system's intent. They clarify what should happen when a user interacts with the software under certain conditions.

When we get the level of abstraction right, acceptance tests become clear, precise, and maintainable. They reflect scenarios that matter to users, expressed in a way that is both easy to read and easy to execute. They serve as living documentation that evolves with the system and its requirements.

Solving Ambiguity and Ensuring Reproducibility

One of the most challenging aspects of software development is not writing code—it is understanding the problem clearly and expressing it precisely. Programs are, after all, specifications of what we want the system to do. But unlike natural languages, which are rich in context and ambiguity, software requires unambiguous clarity.

This is where acceptance testing shines.

By expressing requirements in an executable form, acceptance tests remove ambiguity. They define exactly what needs to happen. Rather than relying on vague documentation or human interpretation, developers, testers, and stakeholders can rely on concrete, shared understanding. The tests describe behavior, not implementation details, which makes them robust to changes in how the system is built, as long as it continues to behave as expected.

Moreover, acceptance tests are reproducible. They can be run automatically, as often as needed, to ensure the software continues to meet its defined expectations. This reproducibility is crucial for modern CI pipelines, where tests are run continuously to catch regressions early

A Contract of Trust

In a collaborative development environment, acceptance tests serve as a contract between the business and the development team. When everyone agrees on the specifications encoded in the tests, there is no room for misinterpretation. The business gets what it asked for, and developers have a reliable guide to follow.

This contract is especially important in agile and iterative processes, where requirements evolve and features are delivered incrementally. With acceptance tests in place, the team always has a clear picture of what "done" means.

Making Software Releasable

Acceptance testing answers a key question: Is the software ready to release? It gives us confidence that the features meet user needs and work correctly. It also checks that we built what was actually requested. Most importantly, it does this in a way that’s automated, repeatable, and reliable.

By aligning development with user intent, removing ambiguity, and validating results, acceptance testing becomes an essential practice—not just for verifying software, but for clearly understanding and specifying it in the first place.

The problem with acceptance testing and 3rd party APIs

Unstable – External APIs can fail randomly, whether due to bugs or release workflows.
No test environment – Many APIs don’t offer a dedicated environment for testing.
Uncontrollable state – It’s often impossible to set up the external system in the desired state for your tests.

These limitations can make acceptance testing brittle and unreliable—unless you can simulate the external system reliably.

How Mokapi Supports Acceptance Testing

That’s where Mokapi comes in. It lets you simulate realistic API behavior in a way that’s fast, accurate, and under your full control.

Acceptance testing becomes significantly more effective with the right tools—especially in complex environments with multiple APIs, microservices, and evolving interfaces. Mokapi was developed to address precisely these challenges. It provides a powerful, flexible, and specification-oriented approach to API simulation, enabling high-quality acceptance testing in a wide variety of scenarios.

Acceptance Testing Across Boundaries

Mokapi makes acceptance testing easier across flexible system boundaries—whether you're focusing on a single microservice or validating your entire architecture—by mocking the APIs they depend on.

Powerful Flexibility for Real-World Scenarios
With Mokapi, you're not limited to ideal cases. The JavaScript-based mock handlers enable the simulation of a wide variety of real-world scenarios, including negative tests, edge cases, and error conditions—all essential for developing robust software. You can programmatically control responses based on request data, headers, or business logic, giving you precise control over your mock's behavior during a test.

This flexibility helps you answer not only the question "Does it work?" but also "Does it work when third-party systems don't."

Specification-Driven Confidence

One of Mokapi's key strengths is its close alignment with API specifications. Mocks are continuously validated against OpenAPI or AsyncAPI definitions, ensuring they accurately reflect the APIs being simulated. This becomes especially valuable as APIs evolve.

When a provider releases a new API version, it can be easily updated in Mokapi. If mock data or API calls no longer conform to the updated specification, Mokapi returns errors that can be caught through acceptance testing. This supports automated API version updates and acts as an early warning system for potential production issues.

Smart Mock Customization with Patch Mechanism

Mokapi supports a patching mechanism for both API specifications and mock data. Patching the specification helps you adopt new API versions more easily while keeping track of your own modifications to the original spec. Patching mock data, on the other hand, allows you to avoid over-mocking—keeping your tests stable and focused, so changes are only required when they’re truly relevant.

Here's an example:

import { on, patch } from 'mokapi';
import { fake } from 'mokapi/faker';

export default function() {
    // Open the OpenAPI pet store specification, resolving all $ref references
    const api = open('pet-store-api.yaml', { as: 'resolved' });

    // Generate a random pet object based on the OpenAPI schema
    const pet = fake(api.components.schemas.pet);

    on('http', (request, response) => {
        // Use the generated pet but override the name to 'Odie'
        response.data = patch(pet, { name: 'Odie' });

        // Alternatively, patch Mokapi's autogenerated response and just set the name
        // response.data = patch(response.data, { name: 'Odie' });
    });
}

This example demonstrates how you can generate valid mock data that conforms to the API specification and then customize only the parts relevant to your test—such as setting a specific pet name—without redefining the entire response structure.

Making Acceptance Testing Sustainable

By combining flexible mocks, specification validation, and a smart patching mechanism, Mokapi makes acceptance testing more resilient, more focused, and easier to maintain. It helps your team test confidently—whether you're building a new feature in isolation, validating complex integrations, or preparing for a production release.

Mokapi doesn’t just enable acceptance testing—it makes it practical, maintainable, and deeply aligned with your evolving system and its requirements.

Ready to get started?

Learn how to set up acceptance tests with Mokapi in your CI/CD pipeline:

👉 Running Mokapi in a CI/CD Pipeline
👉 Mokapi Documentation