How to Test JavaScript Apps: From Unit Tests to AI-Augmented QA

As a software engineer, you should always be open to the challenges this field brings. Two months ago, my project manager assigned me a task: write test cases for an API. I was super excited because it meant I got to learn something new beyond just coding features.

Now, if you’re thinking “writing test cases isn’t my job as a frontend or backend developer”, then you’re missing the point. That mindset holds you back.

At the very least, every engineer should understand Unit Testing and Integration Testing. Writing test cases isn’t rocket science, it’s as simple as English and feels very similar to writing JavaScript code.

That said, if you’ve ever tried setting up testing in a JavaScript application, you probably know how complicated and frustrating it can get.

The JavaScript ecosystem is massive, with endless libraries and frameworks. Things shift constantly, new tools replace old ones, and community standards evolve almost overnight. That’s exactly why I decided to write this article.

In it, we’ll explore a modern approach to JavaScript testing, covering practical patterns, workflows, and even how AI-assisted tools are changing the game.

Let’s dive in.

Table of Contents

• The Evolution of Testing

• The Core Layers of Testing

  • Unit testing

  • Integration testing

  • End-to-End testing

  • AI-Augmented testing

• Future of JavaScript Testing

• Conclusion

• Before We End

The Evolution of Testing

Software testing has been around for as long as software itself. According to IBM (2016), testing started right alongside the very first programs. After World War II, three computer scientists wrote what’s considered to be the first piece of software.

It ran on June 21, 1948, at the University of Manchester in England, performing mathematical calculations with basic machine code instructions.

Since then, testing methods and principles have continuously evolved. As software became more complex and development cycles got faster, the need for reliable and systematic testing grew stronger.

In the early days, the concept of the Testing Pyramid became popular. At the base, you had unit tests, in the middle integration tests, and at the very top a thin layer of end-to-end (E2E) tests. This approach worked well for simpler applications.

But as apps grew more dynamic and interconnected, the pyramid approach began to show its limits. That’s where the Testing Trophy model came in. Instead of overloading with unit tests, it puts greater emphasis on integration testing while still keeping E2E tests and unit tests in balance.

Now, with the rise of AI in QA, testing has entered a new phase. AI-driven tools don’t just run tests, they help generate, maintain, and even self-heal them. This shift is creating a future-ready testing framework designed to handle the complexity of modern software in 2025 and beyond.

The Core Layers of Testing

Testing is not just about finding bugs, but also ensuring reliability, scalability, and user satisfaction. Every testing strategy should cover four main layers:

Unit Testing

Unit testing is a method where you test individual components or units of software in isolation to make sure they work as expected. A unit can be a simple function, a React component, or even a utility module.

When building JavaScript apps, we usually create separate modules or components that later get combined. If any one of those small pieces is broken, the entire application can fail. That’s why unit tests are essential, they catch problems early and ensure reliability before integration.

In the JavaScript ecosystem, there are several tools you can use for writing unit tests:

• Vitest – a modern, fast, and developer-friendly testing framework built to work seamlessly with Vite projects.

• Jest – one of the most widely used testing frameworks, great for React apps among others.

For this section, we’ll focus on Vitest, because it’s lightweight, super-fast, and feels very natural for modern frontend development. Let’s write a test case for a small module.

Imagine we have a simple utility function that adds two numbers:

// sum.ts
export const sum = function (a: number, b: number) {
  return a + b;
};

Every test typically has 3 parts:

1. A description (string).

2. The code execution.

3. The assertion.

Now, let’s write a unit test for the above function using Vitest.

// sum.test.ts
import { describe, expect, it } from "vitest";
import { sum } from "./sum";

describe("sum function", () => {
  it("should return the sum of two numbers", () => { // 1. description
    const result = sum(2, 3); // 2. code execution
    expect(result).toBe(5);   // 3. assertion
  });

  // ... other test cases
});

// ... other describe blocks

Breaking it down:

• describe groups related test cases together. Here, we group everything about the sum function.

• it (or test) defines a single test case. In this example: “should return the sum of two numbers.”

• expect makes the actual assertion. It checks if the result from sum(2,3) equals 5.

When you run this test, Vitest will quickly execute it and show you whether the function passed or failed.

If the function works, you’ll see 1 passed in green. If it fails, the output will be red with details about what went wrong.

Integration Testing

Now that we’ve covered unit testing, let’s move one step up to integration testing. While unit tests focus on testing individual pieces in isolation, integration tests ensure those pieces work together as expected.

Think of it like assembling Lego blocks: each piece might work fine on its own, but when you connect them, something might not fit right. Integration testing helps you catch those issues early.

In simple terms, Integration testing checks how components and modules interact with each other.

Let’s say we have a React component that fetches user data from an API and displays it on the screen.
We’re no longer just testing one function – we’re testing how the component behaves when it calls an API, manages loading states, and renders data dynamically.

Here’s a simple example:

import { useEffect, useState } from "react";

const User = () => {
  const [users, setUsers] = useState<{ name: string; email: string }[]>([]);
  const [loading, setLoading] = useState(false);

  const fetchUsers = async () => {
    setLoading(true);
    try {
      const res = await fetch("https://api.escuelajs.co/api/v1/users");
      const data = await res.json();
      setUsers(data);
    } catch (e) {
      console.log(e);
    } finally {
      setLoading(false);
    }
  };

  useEffect(() => {
    fetchUsers();
  }, []);

  return (
    <>
      {loading ? (
        <h2>Loading...</h2>
      ) : (
        <div>
          {users.map((user, index) => (
            <p key={index}>
              {user.name}: {user.email}
            </p>
          ))}
        </div>
      )}
    </>
  );
};

export default User;

This component does a few things:

• Calls an external API when the component mounts.

• Sets a loading state while fetching data.

• Renders the fetched users on the screen once the data is ready.

Now, our job is to test the complete flow, from the API call to the rendered UI, using Vitest and React Testing Library.

Here’s what the test file looks like:

import { render, screen, waitFor } from "@testing-library/react";
import User from "../components/User";
import { describe, test, expect } from "vitest";

describe("User Component", () => {
  test("fetches and displays users successfully", async () => {
    render(<User />);

    // 1. Initially shows loading
    expect(screen.getByText("Loading...")).toBeInTheDocument();

    // 2. Wait for API response and UI update
    await waitFor(() => {
      expect(
        screen.getByText("Ajay Yadav: ajay.yadav@example.com")
      ).toBeInTheDocument();
      expect(
        screen.getByText("Jane Smith: jane.smith@example.com")
      ).toBeInTheDocument();
    });

    // 3. Loading should disappear
    expect(screen.queryByText("Loading...")).not.toBeInTheDocument();
  });
});

This test looks simple, but it covers the entire flow of our component. Let’s understand it step-by-step:

• Render the component: Render the <User /> component inside the test environment.

• Check the loading state: As soon as the component mounts, the “Loading…” text should appear, indicating that data is being fetched.

• Wait for the data to load: Since the API call is asynchronous, use waitFor() to wait until the users are fetched and displayed.

• Verify the data: Once the API resolves, check if the user names and emails are correctly rendered on the screen.

• Confirm loading disappears: Finally, ensure that the “Loading…” text is removed once the data is displayed, confirming a proper state update.

You can also test how your component behaves when the API fails. For example, you can mock the fetch() call to reject and then verify if an error message appears on the screen.

Vitest and React Testing Library make it easy to mock responses and simulate both success and failure cases, helping you ensure that your app handles real-world scenarios gracefully.

End-to-End Testing

Now that we’ve seen how integration testing ensures that different components work together, let’s move to the third layer, End-to-End (E2E) testing.

While unit and integration tests run in isolated or simulated environments, E2E tests mimic how real users interact with your app.

They open a browser and perform actions like clicking buttons, typing in fields, and verifying what appears on the screen, exactly like a real person would.

Think of E2E testing as putting your entire app on stage and watching if it performs flawlessly in front of the audience. In simple words, E2E testing verifies the full user journey from start to finish.

Let’s take a common example, a login flow. As a developer, you’ve probably built dozens of login forms, but how do you know if they truly work under real conditions? That’s where E2E testing comes in.

Using tools like Playwright or Cypress, you can perform effective E2E testing. Both Playwright and Cypress are powerful tools and are popular among developers.

We can simulate a real browser, fill out the login form, submit it, and confirm that the user is redirected to the dashboard. Here’s what a simple E2E test looks like using Playwright:

// tests/login.e2e.ts
import { test, expect } from "@playwright/test";

test("should login successfully", async ({ page }) => {
  // 1. Visit the login page
  await page.goto("http://localhost:3000/login");

  // 2. Fill in the form
  await page.fill('input[name="email"]', "user@example.com");
  await page.fill('input[name="password"]', "password123");

  // 3. Click login button
  await page.click('button[type="submit"]');

  // 4. Wait for navigation and verify success message or dashboard
  await expect(page).toHaveURL("http://localhost:3000/dashboard");
  await expect(page.getByText("Welcome back!")).toBeVisible();
});

Let’s understand what’s happening here step-by-step:

• Visit the page: The test opens your web app in a real browser. It navigates to http://localhost:3000/login.

• Simulate user input: Playwright fills in the email and password fields, just like a real user typing into the form.

• Perform actions: It clicks the login button, triggering all the same logic your frontend and backend would normally handle.

• Verify the outcome: Once the user logs in, check if the URL changes to /dashboard and whether a welcome message appears on the screen.

That’s it, you just automated your first user journey from login to dashboard. Both frameworks achieve the same goal, ensuring your app behaves correctly in a real browser, not just in isolated tests.

AI-Augmented Testing

As testing evolves, a new layer has emerged that is AI-Augmented QA. This isn’t just another tool in the developer’s toolkit. It’s a complete transformation in how software quality is managed.

Traditionally, testing has been a manual process. Engineers wrote, maintained, and updated test cases whenever the product changed. But with AI entering the scene, that manual burden is decreasing.

AI models can now analyze your codebase, understand logic, and generate relevant test cases almost instantly, covering edge cases you might never think of. Tools like GitHub Copilot and CodiumAI already assist in generating smart test suites, while continuously learning from your coding style and past patterns.

Beyond code suggestions, complete AI QA platforms are changing automation itself. For example, an AI QA agent like Bug0 can adjust to UI changes automatically. If a button label or DOM structure changes, its self-healing tests find elements visually instead of depending on fixed selectors.

It also produces real-time test reports with detailed logs and video recordings, helping developers pinpoint UI or data changes causing failures.

With CI/CD integrations like GitHub or GitLab, it can automatically start and validate test runs for every pull request, updating PR checks just like a human QA engineer would.

While AI-assisted testing is powerful, it’s not a full replacement for human judgment. Developers still play a vital role in the following ways:

• AI can generate test cases, but humans must decide what truly matters for business logic and user experience.

• Reviewing AI-generated tests to ensure they are relevant and to avoid false positives.

• Interpreting failures contextually means understanding whether a test failure indicates a real bug or an expected change.

• Maintaining ethical and data-safe workflows involves avoiding the exposure of sensitive data when using cloud-based AI tools.

When used responsibly, AI becomes a testing partner, automating the tedious tasks while leaving creative problem-solving, decision-making, and domain understanding to developers.

This shift marks the beginning of intelligent, autonomous QA. AI isn’t just automating repetitive testing, it’s transforming the process into a continuous, adaptive feedback loop, capable of predicting and resolving failures on its own.

In the coming years, expect testing to evolve into a collaborative process between human engineers and AI copilots, ensuring every release is not just faster, but smarter and more reliable than ever before.

Future of JavaScript Testing

JavaScript testing is changing faster than ever. A few years ago, developers had to deal with tons of testing libraries and confusing setups. Now, things are becoming much more unified, smarter, and easier to work with.

In the future, testing will move from being reactive to proactive. That means instead of catching bugs after they happen, tools will be smart enough to predict and prevent them before they appear.

With AI-powered test generation and real-time monitoring, every commit you make could be automatically checked for reliability and performance without you even running a command.

Frameworks like Vitest, Playwright, and React Testing Library will still be the core tools, but the real progress will come from how they integrate and learn.

We’ll also see tighter CI/CD integrations, where pipelines can automatically adjust based on your test coverage and code risk. Testing won’t feel like an extra step anymore, it’ll become a natural part of development, powered by both human logic and machine intelligence.

In short, the future of JavaScript testing is about speed, intelligence, and automation. A world where developers spend more time building and less time debugging.

Conclusion

Testing isn’t just about preventing bugs, it’s about building confidence. Confidence that your code works, your features scale, and your users have a seamless experience.

Whether it’s unit tests ensuring logic, integration tests validating flow, E2E tests simulating real behavior, or AI-enhanced automation managing it all. Testing is the silent force that makes great software possible.

As a developer, understanding how testing fits into your workflow is no longer optional. Rather, it’s a skill that sets you apart. The more you test, the better you code and the faster you ship with peace of mind.

So, the next time someone says writing tests isn’t your job, you’ll know the truth: Testing isn’t extra work. Instead, it’s part of writing better, more reliable software.

Before We End

I hope you found this article insightful. I’m Ajay Yadav, a software developer and content creator.

You can connect with me on:

• Twitter/X and LinkedIn, where I share insights to help you improve 0.01% each day.

• Check out my GitHub for more projects.

• I also run a YouTube Channel where I share content about careers, software engineering, and technical writing.

See you in the next article — until then, keep learning!