概述
1.测试脚手架(test fixture)
测试准备前要做的工作和测试执行完后要做的工作.包括setUp()和tearDown().
2.测试案例(test case)
最小的测试单元.
3.测试套件(test suite)
测试案例的集合.
4.测试运行器(test runner)
测试执行的组件.
命令行接口
可以用命令行运行测试模块,测试类以及测试方法.
代码如下:
python -m unittest test_module1 test_module2
python -m unittest test_module.TestClass
python -m unittest test_module.TestClass.test_method
可加-v打印详细信息
代码如下:
python -m unittest -v test_module
测试案例自动搜索
unittest支持简单的test discovery. 命令行传入discovery后,框架会自动在当前目录搜索要测试的案例并执行.搜索目录必须是包或者模块.基本使用如下:
代码如下:
cd project_directory
python -m unittest discover
子选项如下:
-v, –verbose
输出信息的详细级别
-s, –start-directory directory
开始搜索目录 (默认为当前目录)
-p, –pattern pattern
匹配的文件名 (默认为test*.py)
-t, –top-level-directory directory
搜索的顶层目录 (默认为start directory)
创建测试代码
1.方式一
创建子类继承unittest.TestCase,然后重写以下方法
代码如下:
class WidgetTestCase(unittest.TestCase):
def setUp(self):
pass
def runTest(self):
pass
def tearDown(self):
pass
运行
2.方式二
编写以test开头的方法
代码如下:
class WidgetTestCase(unittest.TestCase):
def setUp(self):
pass
def test_xx1(self)
def test_xx2(self)
...
def test_xxN(self)
def tearDown(self):
pass
构建测试套件
方式一
代码如下:
widgetTestSuite = unittest.TestSuite()
widgetTestSuite.addTest(WidgetTestCase('test_default_size'))
widgetTestSuite.addTest(WidgetTestCase('test_resize'))
方式二(推荐)
代码如下:
def suite():
suite = unittest.TestSuite()
suite.addTest(WidgetTestCase('test_default_size'))
suite.addTest(WidgetTestCase('test_resize'))
return suite
方式三(推荐)
代码如下:
def suite():
tests = ['test_default_size', 'test_resize']
return unittest.TestSuite(map(WidgetTestCase, tests))
方式四
多个测试套件构建成更大的测试套件
代码如下:
suite1 = module1.TheTestSuite()
suite2 = module2.TheTestSuite()
alltests = unittest.TestSuite([suite1, suite2])
方式五
unittest的TestLoader提供生成默认的测试套件
代码如下:
suite = unittest.TestLoader().loadTestsFromTestCase(WidgetTestCase)
忽略测试案例( Python2.7支持)
可以分无条件忽略和有条件忽略,通过装饰器实现
代码如下:
class MyTestCase(unittest.TestCase):
@unittest.skip("demonstrating skipping")
def test_nothing(self):
self.fail("shouldn't happen")
@unittest.skipIf(mylib.__version__
"not supported in this library version")
def test_format(self):
# Tests that work for only a certain version of the library.
pass
@unittest.skipUnless(sys.platform.startswith("win"), "requires Windows")
def test_windows_support(self):
# windows specific testing code
pass
测试类也可以忽略
代码如下:
@unittest.skip("showing class skipping")
class MySkippedTestCase(unittest.TestCase):
def test_not_run(self):
pass

Is it enough to learn Python for two hours a day? It depends on your goals and learning methods. 1) Develop a clear learning plan, 2) Select appropriate learning resources and methods, 3) Practice and review and consolidate hands-on practice and review and consolidate, and you can gradually master the basic knowledge and advanced functions of Python during this period.

Key applications of Python in web development include the use of Django and Flask frameworks, API development, data analysis and visualization, machine learning and AI, and performance optimization. 1. Django and Flask framework: Django is suitable for rapid development of complex applications, and Flask is suitable for small or highly customized projects. 2. API development: Use Flask or DjangoRESTFramework to build RESTfulAPI. 3. Data analysis and visualization: Use Python to process data and display it through the web interface. 4. Machine Learning and AI: Python is used to build intelligent web applications. 5. Performance optimization: optimized through asynchronous programming, caching and code

Python is better than C in development efficiency, but C is higher in execution performance. 1. Python's concise syntax and rich libraries improve development efficiency. 2.C's compilation-type characteristics and hardware control improve execution performance. When making a choice, you need to weigh the development speed and execution efficiency based on project needs.

Python's real-world applications include data analytics, web development, artificial intelligence and automation. 1) In data analysis, Python uses Pandas and Matplotlib to process and visualize data. 2) In web development, Django and Flask frameworks simplify the creation of web applications. 3) In the field of artificial intelligence, TensorFlow and PyTorch are used to build and train models. 4) In terms of automation, Python scripts can be used for tasks such as copying files.

Python is widely used in data science, web development and automation scripting fields. 1) In data science, Python simplifies data processing and analysis through libraries such as NumPy and Pandas. 2) In web development, the Django and Flask frameworks enable developers to quickly build applications. 3) In automated scripts, Python's simplicity and standard library make it ideal.

Python's flexibility is reflected in multi-paradigm support and dynamic type systems, while ease of use comes from a simple syntax and rich standard library. 1. Flexibility: Supports object-oriented, functional and procedural programming, and dynamic type systems improve development efficiency. 2. Ease of use: The grammar is close to natural language, the standard library covers a wide range of functions, and simplifies the development process.

Python is highly favored for its simplicity and power, suitable for all needs from beginners to advanced developers. Its versatility is reflected in: 1) Easy to learn and use, simple syntax; 2) Rich libraries and frameworks, such as NumPy, Pandas, etc.; 3) Cross-platform support, which can be run on a variety of operating systems; 4) Suitable for scripting and automation tasks to improve work efficiency.

Yes, learn Python in two hours a day. 1. Develop a reasonable study plan, 2. Select the right learning resources, 3. Consolidate the knowledge learned through practice. These steps can help you master Python in a short time.


Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

AI Hentai Generator
Generate AI Hentai for free.

Hot Article

Hot Tools

MinGW - Minimalist GNU for Windows
This project is in the process of being migrated to osdn.net/projects/mingw, you can continue to follow us there. MinGW: A native Windows port of the GNU Compiler Collection (GCC), freely distributable import libraries and header files for building native Windows applications; includes extensions to the MSVC runtime to support C99 functionality. All MinGW software can run on 64-bit Windows platforms.

Dreamweaver CS6
Visual web development tools

WebStorm Mac version
Useful JavaScript development tools

ZendStudio 13.5.1 Mac
Powerful PHP integrated development environment

Notepad++7.3.1
Easy-to-use and free code editor