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Vibe Coding · 강의

인공지능으로 테스트 추가하기

사용자가 발견하기 전에 문제를 찾아냅니다.

인공지능으로 테스트 추가하기은(는) CoddyKit의 무료 Vibe Coding 강의입니다. 이것은 4개 중 1번째 강의입니다. 아래에서 전체 강의를 무료로 읽을 수 있으며, 내장 코드 에디터와 24/7 AI 튜터와 함께 브라우저에서 직접 실습할 수 있습니다. 이 강의는 Vibe Coding 학습 경로의 일부이며, 진행 상황이 웹과 CoddyKit 앱에 동기화됩니다. Vibe Coding 강의에는 총 4개의 강의가 포함되어 있습니다.

이 강의의 일부는 아직 번역되지 않았으며 영어로 표시됩니다.

Why Tests Save Your App

You vibe-coded an app. It works today. But next week you ask AI to add a feature, and something else silently breaks. Tests are tiny programs that check your app still does the right thing.

Think of tests as a safety net: every time you change code, they re-run and shout if anything broke. With AI, you don't even have to write them by hand — you describe what should happen and let the AI generate the tests.

What a Test Actually Looks Like

A test calls your code with some input and checks the output is what you expect. If it isn't, the test fails and points at the problem.

Here's a real, runnable example: a function plus a check.

function add(a, b) {
  return a + b;
}

// a tiny hand-made 'test'
function expectEqual(actual, expected, label) {
  if (actual === expected) console.log('PASS: ' + label);
  else console.log('FAIL: ' + label + ' (got ' + actual + ')');
}

expectEqual(add(2, 3), 5, 'adds two numbers');
expectEqual(add(-1, 1), 0, 'handles negatives');

Let AI Write the Tests

You rarely write tests from scratch anymore. You paste your function into Cursor, Claude Code, or Copilot and ask for tests. The key is telling the AI which cases matter: normal input, weird input, and edge cases (empty, zero, negative, huge).

Here is a prompt you can copy.

Here is my function:

function discount(price, percent) {
  return price - (price * percent / 100);
}

Write tests using Vitest. Cover:
- a normal case (100 at 20%)
- 0% discount
- 100% discount
- a negative price (should it even be allowed?)
Use describe/it/expect. Keep it short.

Test Runners: The Tool That Runs Tests

You don't run tests by hand. A test runner finds all your test files, runs them, and prints a clean PASS/FAIL report. For JavaScript the popular ones are Vitest and Jest.

Ask your AI tool to set one up — it knows the commands and config so you don't have to memorize them.

Set up Vitest in this project. Add the dependency, a "test" script in package.json, and a sample test file so I can run `npm test` and see it pass.

Anatomy of a Real Test File

Most JS tests share the same shape: describe groups related tests, it (or test) is one case, and expect checks a value. AI generates this pattern constantly, so it pays to recognize it.

This is what the AI typically hands back.

import { describe, it, expect } from 'vitest';
import { discount } from './pricing';

describe('discount', () => {
  it('takes 20% off 100', () => {
    expect(discount(100, 20)).toBe(80);
  });

  it('returns full price at 0%', () => {
    expect(discount(100, 0)).toBe(100);
  });
});

Tests as a Spec for the AI

Here's a power move: write the tests first, then ask AI to make them pass. The tests become a precise description of what you want, with zero ambiguity.

This flips the workflow — instead of describing behavior in fuzzy words, you describe it in checkable code.

These tests describe a function I want. Write the `slugify` function so ALL of these pass:

import { slugify } from './slug';
expect(slugify('Hello World')).toBe('hello-world');
expect(slugify('  Spaced  Out ')).toBe('spaced-out');
expect(slugify('A/B & C')).toBe('a-b-c');

Return only the function.

Don't Trust Tests That Always Pass

AI sometimes writes tests that can't fail — they assert something trivially true, or they re-implement the bug. A passing test that proves nothing is worse than no test.

Quick check: temporarily break your function on purpose. If the test still passes, it's a fake test. Ask the AI to make it actually verify the behavior.

// Suspicious: this passes no matter what discount() returns
it('works', () => {
  const result = discount(100, 20);
  expect(typeof result).toBe('number'); // too weak!
});

// Better: checks the actual value
it('takes 20% off 100', () => {
  expect(discount(100, 20)).toBe(80);
});

Cover the Edge Cases

Bugs hide in the unusual inputs: empty strings, zero, negative numbers, missing values, very large lists. AI is great at brainstorming these if you ask.

Use a prompt that pushes for the weird cases on purpose.

List the edge cases I should test for this function, then write a test for each:

function averageScore(scores) {
  return scores.reduce((a, b) => a + b, 0) / scores.length;
}

Think about: empty array, one item, negatives, non-numbers. What should happen for each?

Run Tests Automatically (CI)

The real win: tests run on every change, automatically. When you push to GitHub, a service like GitHub Actions re-runs your whole test suite. If something broke, you find out in minutes — not from an angry user.

You don't have to learn the YAML config. Ask your AI to write it.

Create a GitHub Actions workflow that runs `npm install` and `npm test` on every push and pull request. Use Node 20. Put it in .github/workflows/test.yml.

A Sensible Testing Rhythm

You don't need 100% coverage to benefit. A practical rhythm for vibe coders:

  • Test the logic that matters — pricing, auth, data parsing, anything users feel.
  • Skip trivial UI glue at first.
  • Add a test every time you fix a bug so it never comes back.

Ask AI: What are the 5 most important things to test in this app? and start there.

Reading the Test Report

When tests fail, the runner tells you exactly what it expected vs. what it got. Don't panic — copy that whole report to your AI and ask it to fix the code or the test (you decide which is wrong).

My test failed with this output:

FAIL  src/pricing.test.js
  discount > takes 20% off 100
  expected 80 but got 120

Here is the discount function: <paste it>
Is the function wrong or the test wrong? Fix the right one and explain.

Quick Check

You ask AI for tests and every single one passes immediately — even after you intentionally break the function. What's the most likely problem?

Recap: Tests Are Your Safety Net

You learned why tests matter when AI is changing your code, what a test looks like, and how to let AI write them for you. Key habits:

  • Tell the AI which cases matter, especially edge cases.
  • Use a runner like Vitest and run tests on every change with CI.
  • Verify tests can actually fail — break the code on purpose.
  • Add a test whenever you fix a bug.

Next up: keeping your AI-generated code secure.

자주 묻는 질문

“인공지능으로 테스트 추가하기” 강의는 무료인가요?

네 — “인공지능으로 테스트 추가하기” 전체 내용을 이 웹사이트에서 무료로 읽을 수 있습니다. 인터랙티브하게 실습하려면(내장 코드 에디터와 24/7 AI 튜터), CoddyKit PRO로 업그레이드하면 Vibe Coding 강의 전체를 잠금 해제할 수 있습니다. Vibe Coding 강의에는 총 4개의 강의가 포함되어 있습니다.

“인공지능으로 테스트 추가하기”에서 뭘 배우나요?

사용자가 발견하기 전에 문제를 찾아냅니다. 브라우저에서 직접 실행하는 실습 코드로 Vibe Coding을(를) 배우며, 24/7 AI 튜터가 강의를 진행하면서 질문에 답변해줍니다.

Vibe Coding을(를) 시작하는 데 경험이 필요한가요?

사전 경험은 필요하지 않습니다. CoddyKit의 Vibe Coding은(는) 초급자부터 고급 학습자까지를 위해 구성되어 있으므로, 여기서 시작하거나 처음부터 시작할 수 있으며 자신의 속도대로 진행할 수 있습니다. 이것은 4개 중 1번째 강의입니다.

“인공지능으로 테스트 추가하기” 강의는 얼마나 걸리나요?

대부분의 CoddyKit 강의는 약 5~10분이 소요됩니다. 각 강의는 간결하고 인터랙티브하여 꾸준한 진행이 가능하며, 웹과 앱에서 중단한 부분부터 바로 시작할 수 있습니다.

이 Vibe Coding 강의에서 코드를 작성하고 실행할 수 있나요?

네. 모든 Vibe Coding 강의에는 내장 코드 에디터가 포함되어 있으므로, 브라우저에서 바로 실제 코드를 작성하고 실행한 후 즉시 AI 피드백을 받을 수 있습니다 — 로컬 설정이 필요 없습니다.

이 강의의 모든 강의

  1. 인공지능으로 테스트 추가하기
  2. 인공지능 코드 보안 기초
  3. 유지 관리 가능한 코드 만들기
  4. 성능과 비용
← Vibe Coding(으)로 돌아가기