测试进阶的重要性
测试是软件开发中不可或缺的环节。掌握高级测试技术能够提高代码质量、减少bug、增强代码的可维护性。Python提供了丰富的测试工具和框架，包括unittest、pytest、mock等。
Mock和Stub技术
1. unittest.mock基础使用
from unittest.mock import Mock, patch, MagicMock
import requests
def basic_mock_demo():
"""基础Mock演示"""
print("=== 基础Mock演示 ===")
# 1. 创建Mock对象
mock_obj = Mock()
mock_obj.some_method.return_value = "Hello Mock"
# 调用Mock方法
result = mock_obj.some_method()
print(f"Mock方法返回值: {result}")
# 2. 验证方法调用
mock_obj.some_method.assert_called_once()
print("方法调用验证通过")
# 3. 带参数的Mock
mock_calculator = Mock()
mock_calculator.add.return_value = 15
result = mock_calculator.add(5, 10)
print(f"计算器Mock: 5 + 10 = {result}")
# 验证调用参数
mock_calculator.add.assert_called_with(5, 10)
print("参数验证通过")
basic_mock_demo()
2. patch装饰器使用
import requests
from unittest.mock import patch, Mock
def api_service_call(url):
"""模拟API服务调用"""
try:
response = requests.get(url)
response.raise_for_status()
return response.json()
except requests.RequestException as e:
return {"error": str(e)}
def patch_decorator_demo():
"""patch装饰器演示"""
print("\n=== patch装饰器演示 ===")
# 使用patch装饰器
@patch('requests.get')
def test_successful_api_call(mock_get):
"""测试成功的API调用"""
# 配置Mock返回值
mock_response = Mock()
mock_response.json.return_value = {
"status": "success",
"data": {"id": 1, "name": "测试用户"}
}
mock_response.raise_for_status.return_value = None
mock_get.return_value = mock_response
# 调用被测试的函数
result = api_service_call("https://api.example.com/user/1")
# 验证结果
assert result["status"] == "success"
assert result["data"]["id"] == 1
print("成功API调用测试通过")
# 验证Mock调用
mock_get.assert_called_once_with("https://api.example.com/user/1")
print("Mock调用验证通过")
# 运行测试
test_successful_api_call()
patch_decorator_demo()
pytest框架进阶
1. 参数化测试
import pytest
def math_operations_demo():
"""数学运算演示"""
print("\n=== 参数化测试演示 ===")
def add(a, b):
"""加法运算"""
return a + b
def multiply(a, b):
"""乘法运算"""
return a * b
def divide(a, b):
"""除法运算"""
if b == 0:
raise ValueError("除数不能为零")
return a / b
# 1. 基础参数化测试
@pytest.mark.parametrize("a,b,expected", [
(2, 3, 5),
(0, 5, 5),
(-1, 1, 0),
(10, -5, 5),
])
def test_add_parametrized(a, b, expected):
"""参数化加法测试"""
result = add(a, b)
assert result == expected
print(f"add({a}, {b}) = {result} ✓")
# 2. 异常测试参数化
@pytest.mark.parametrize("a,b,expected_exception", [
(10, 0, ValueError),
(5, 0, ValueError),
])
def test_divide_exception(a, b, expected_exception):
"""异常测试参数化"""
with pytest.raises(expected_exception):
divide(a, b)
print(f"divide({a}, {b}) 正确抛出 {expected_exception.__name__} ✓")
print("参数化测试定义完成，运行时会自动生成多个测试用例")
math_operations_demo()
2. fixture系统
import pytest
class TestDatabase:
"""测试数据库类"""
def __init__(self):
self.data = {}
def add_user(self, user_id, name):
"""添加用户"""
self.data[user_id] = {"id": user_id, "name": name}
def get_user(self, user_id):
"""获取用户"""
return self.data.get(user_id)
def clear(self):
"""清空数据"""
self.data.clear()
def fixture_demo():
"""pytest fixture演示"""
print("\n=== pytest fixture演示 ===")
# 基础fixture
@pytest.fixture
def database():
"""数据库fixture"""
db = TestDatabase()
yield db # 提供测试数据
db.clear() # 清理资源
# 参数化fixture
@pytest.fixture(params=["user1", "user2", "user3"])
def user_data(request):
"""参数化用户数据fixture"""
return {"name": request.param, "email": f"{request.param}@example.com"}
# 自动使用fixture
@pytest.fixture(autouse=True)
def setup_and_teardown():
"""自动执行的setup和teardown"""
print("测试开始前的准备工作")
yield
print("测试结束后的清理工作")
print("fixture定义完成，在实际测试中会这样使用:")
print("""
def test_user_creation(database):
'''测试用户创建'''
database.add_user(1, "张三")
user = database.get_user(1)
assert user["name"] == "张三"
""")
fixture_demo()
TDD（测试驱动开发）
1. TDD实践演示
class TDDCalculator:
"""TDD计算器演示"""
def __init__(self):
self.history = []
def add(self, a, b):
"""加法运算"""
result = a + b
self.history.append(f"{a} + {b} = {result}")
return result
def subtract(self, a, b):
"""减法运算"""
result = a - b
self.history.append(f"{a} - {b} = {result}")
return result
def multiply(self, a, b):
"""乘法运算"""
result = a * b
self.history.append(f"{a} * {b} = {result}")
return result
def divide(self, a, b):
"""除法运算"""
if b == 0:
raise ValueError("除数不能为零")
result = a / b
self.history.append(f"{a} / {b} = {result}")
return result
def get_history(self):
"""获取计算历史"""
return self.history.copy()
def clear_history(self):
"""清空历史"""
self.history.clear()
def tdd_demo():
"""TDD演示"""
print("\n=== TDD（测试驱动开发）演示 ===")
def test_calculator():
"""测试计算器功能"""
calc = TDDCalculator()
# 测试加法
assert calc.add(2, 3) == 5
assert calc.add(-1, 1) == 0
assert calc.add(0, 0) == 0
# 测试减法
assert calc.subtract(5, 3) == 2
assert calc.subtract(0, 5) == -5
# 测试乘法
assert calc.multiply(3, 4) == 12
assert calc.multiply(-2, 3) == -6
# 测试除法
assert calc.divide(10, 2) == 5.0
assert calc.divide(7, 3) == 7/3
# 测试异常
try:
calc.divide(5, 0)
assert False, "应该抛出异常"
except ValueError as e:
assert str(e) == "除数不能为零"
# 测试历史记录
history = calc.get_history()
assert len(history) >= 6 # 至少有6个操作
# 测试清空历史
calc.clear_history()
assert len(calc.get_history()) == 0
print("所有测试通过！")
print("TDD开发流程:")
print("1. 红色阶段：编写失败的测试")
print("2. 绿色阶段：编写最小代码使测试通过")
print("3. 重构阶段：改进代码质量")
print("4. 重复上述步骤")
print("\n运行测试:")
test_calculator()
print("\nTDD的好处:")
print("- 确保代码质量")
print("- 提高代码覆盖率")
print("- 更好的代码设计")
print("- 减少bug")
print("- 提高开发信心")
tdd_demo()
总结
测试进阶的关键要点：
- Mock和Stub：模拟外部依赖，隔离测试环境
- pytest框架：强大的测试框架，支持fixture和参数化
- 测试标记：组织和筛选测试用例
- 覆盖率分析：确保代码质量
- TDD开发：测试驱动的开发方法
- 质量评估：持续改进测试质量
掌握这些高级测试技术，可以构建更可靠、更易维护的软件系统。
转载请注明：周志洋的博客 » Python进阶-测试进阶详解
