要測(cè)試Python代碼，可使用內(nèi)置的unittest框架或更簡(jiǎn)潔的pytest庫(kù)。 1. 使用unittest時(shí)，需創(chuàng)建繼承自u(píng)nittest.TestCase的測(cè)試類，編寫以test_開頭的方法，並利用斷言驗(yàn)證結(jié)果。 2. pytest無需繼承類，只需編寫以test_開頭的函數(shù)並使用普通assert語(yǔ)句。 3. 測(cè)試時(shí)應(yīng)覆蓋邊界條件、無效輸入及異常處理，例如檢查除零錯(cuò)誤。 4. 可通過Git鉤子、CI/CD工具（如GitHub Actions）或IDE集成實(shí)現(xiàn)測(cè)試自動(dòng)化，確保每次提交均經(jīng)過驗(yàn)證，從而提高代碼質(zhì)量與開發(fā)效率。

Testing Python code is a crucial part of development — it helps catch bugs early and ensures your code behaves as expected. The key idea is to write tests that validate the logic of your functions, classes, or entire modules.

Use Built-in Testing Tools Like unittest

Python comes with a built-in testing framework called unittest . It's inspired by Java's JUnit and supports test automation, setup and teardown routines, and more.

Here's how you can use it:

• Create a test class that inherits from unittest.TestCase
• Write methods that start with test_ (this naming convention tells unittest it's a test method)
• Use assertions like self.assertEqual() , self.assertTrue() , etc., to check outcomes

Example:

 import unittest

def add(a, b):
    return ab

class TestMathFunctions(unittest.TestCase):
    def test_add(self):
        self.assertEqual(add(2, 3), 5)
        self.assertEqual(add(-1, 1), 0)

if __name__ == '__main__':
    unittest.main()

Run this script, and unittest will run your tests and report any failures.

Try pytest for Simpler, More Flexible Testing

If you want something less verbose than unittest , try pytest . It allows you to write smaller, cleaner test files without needing to subclass anything.

To get started:

• Install pytest: pip install pytest
• Write a function that starts with test_
• Run pytest in your terminal

Example:

 def multiply(a, b):
    return a * b

def test_multiply():
    assert multiply(2, 3) == 6
    assert multiply('a', 3) == 'aaa'

Just save this as test_math.py and run pytest . If all assertions pass, you'll see a success message.

One big advantage of pytest is its ecosystem — plugins like pytest-cov help measure test coverage, and others make integration testing easier.

Don't Forget Edge Cases and Input Validation

It's easy to test the “happy path” — when everything works as expected — but real-world code often runs into unexpected inputs.

Make sure to test:

• Invalid types (eg, passing a string where an int is expected)
• Boundary conditions (like empty lists or zero)
• Error handling (does your code raise exceptions correctly?)

For example:

 def divide(a, b):
    if b == 0:
        raise ValueError("Cannot divide by zero")
    return a / b

def test_divide():
    assert divide(10, 2) == 5
    with pytest.raises(ValueError):
        divide(1, 0)

This way, you're not just checking what works — you're also making sure errors are handled properly.

Automate Your Tests Where Possible

Running tests manually every time gets old fast. You can integrate testing into your workflow using:

• Git hooks (to run tests before commits)
• CI/CD tools like GitHub Actions, GitLab CI, or Travis CI
• IDE integrations (many editors highlight failing tests inline)

Set up a simple CI pipeline to run tests automatically on every push. That way, even if you forget to run them locally, you'll know if something breaks.

基本上就這些。 Writing good tests takes practice, but once you get the hang of it, it becomes second nature — and saves a lot of debugging time later.

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