Python Unit Testing and CI Workflow. Tim Scott, Python Meetup 11/07/17

Transcription

1 Python Unit Testing and CI Workflow Tim Scott, Python Meetup 11/07/17

2 A Little About Me Started as a CoOp Engineer ( ) and Design Engineer at Adtran ( ) Currently work at Abaco Systems as Lead System Software Engineer Passionate about software quality and automation I blog about software automation and developer efficiency at (this presentation can be found there) Connect with me LinkedIn ( Twitter (@tscott_87)

3 What is a unit test? Unittesting example What is mock? Mock Example Continuous integration overview Docker Outline

4 What is a Unit Test? White-box test against your code (tests functions, internal logic, etc.) Automated test Tests a unit - a function/a class Good practice: Write a very small piece of code Run unit tests Repeat

5 Why Should You Care?

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9 Other Reasons Why You Should Care Manual testing takes a lot more time than an automated test It is much easier to run an automated test than a manual test others are more likely to use it. Continuous integration servers can be used to verify the tests pass after every commit.

10 And Even More Reasons (srsly, you should do this) Automated tests are repeatable. Automated tests can be improved upon (since they are repeatable). Automated testing prevents skipping testing because it s a simple change. Potentially increases job satisfaction. Read about my first unit test experience:

11 How to Write a Python Unit Test 1. Determine a function/behavior/file to test. 2. Create a test_*.py file. 3. In that test file, import unittest. 4. Create a class that inherits from unittest.testcase 5. Create a function in that class that starts with test_[test_case_title] 6. Use a unittest check or assertion function (discussed later)

12 Running the Unit Tests Install nose2 (or use a common environment - discussed later) Go to the directory that contains all your tests, or the directory above it Run nose2

13 Now for a Very Simple Example... def add(num1, num2): return num1 + num2

14 Now for the test import unittest from add import add class TestAdd(unittest.TestCase): def test_add_two_and_three_equals_five(self): self.assertequal(add(2, 3), 5)

15 Running the Test

16 What About a Failure?

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18 Some Testing Functions Given by unittest assertequal assertnotequal asserttrue assertfalse assertraises assertraisesregexp

19 Unit Testing is great...

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21 I m Gonna Mock Your World Mock library: replace any API call with a fake object Really easy to do Fake out interactions with OS calls, network accesses, and many, many other things

22 Typical Usage Import the patch function: from unittest.mock import patch Patch a function (a decorator for your test ) Your test function gets an extra argument - the MagicMock object: def test_something(self, mock_my_func): Do stuff with your mock object

23 Some Things You Can Do With MagicMock called (was the object called?) assert_called_with (was the object called with given arguments?) return_value (when called, this is what is returns) side_effect (raise an exception when called, return something different each time when called, call another function when called)

24 Revisiting our add function - now file_add def file_add(file1, file2): num1 = int(open(file1, "r").read()) num2 = int(open(file2, "r").read()) return num1 + num2

25 We Could Test with a File import unittest from add import file_add class TestAdd(unittest.TestCase): def test_file_add_two_and_three_equals_five(self): self.assertequal(file_add("file1.txt", "file2.txt"), 5)

26 Caveats Opening from a file is slow What happens if the test file doesn t exist? Do you really care if the Python open function gets tested? There s a better way...

27 import unittest from unittest.mock import patch from add import file_add class def test_file_add_two_and_three_equals_five(self, mock_open): mock_open.return_value.read.side_effect = ("2", "3") self.assertequal(file_add("mock_file_1", "mock_file_2"), 5)

28 What exactly did we mock? mock_open.return_value.read.side_effect open(file1, "r").read() Whenever read is called, it uses side_effect instead. It will be 2 the first time then 3.

29 What Else Might Be Mocked? Accessing something over the network Other os calls Refactoring legacy code (for instance, in a really long function that calls lots of other functions) Database access

30 If We Didn t Have the mock library... May have to create an extra interface class Much harder to test existing code without changing it first Spend a lot more time faking something out This is a strength of Python being a dynamic language

31 Warning! Be Careful with Mocks... Your mock is only as accurate as you make it. If we had returned ints rather than strings in our open mock, we could hide a bug. Higher-level tests such as system tests are good for testing these interactions and dependencies

32 Next Up: Continuous Integration and Docker

33 Jenkins Automation server with web interface Has jobs that do work Can check out code from source repository and build/test it

34 Workflow with Jenkins Commit code to your source repo Jenkins automatically builds a job when that commit occurs That job checks out your code then runs your unit tests Report code coverage/other metrics It can send s if the build fails Makes unit testing much more valuable - every commit runs your test suite and notifies you of failures

35 Workflow with Jenkins Source Control 2. SC tells Jenkins a commit occurred 1. Commit code Your dev machine 5. Your code is automatically tested, and you are notified of results 3. Jenkins starts a job, part of which pulls code from SC (then builds/tests code) Jenkins Server 4. Jenkins may send an through the server

36 Now for Some Jenkins Features...

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41 More Jenkins Resources Jenkins website: Installing Jenkins (running in a Docker container) and creating your first job: Running a job when code gets committed in Git: s/ Jenkinsfile:

42 But the Build Environment Is Different! We need to install nose2, but suppose we had several other libraries and dependencies to install? Introducing...

43 Docker Kind of like a VM that launches very quickly Does not save state (start from fresh beginning environment every time it is launched) Great for deploying a service (or multiple services) with a defined environment

44 Run the Same Image on Dev Machine and Build Server 1. Start with an image that has Python 3 2. nose2 in it (and other dependencies you need) 3. Host that Docker image on a local repo 4. On the build server and your dev machine, install Docker and docker-compose. 5. Use a docker command to run the unit tests on both the build server and your dev machine.

45 Docker with Build Server and Dev Machine 1. Create Docker image then push to Docker repo Your dev machine Docker Repo 3. Later, developer Bob wants to run your tests. He uses the same Docker image. Another dev machine 2. Later, after a commit, Jenkins starts a job and pulls down that Docker image Build Server

46 More Docker Resources Docker website: What is Docker? Rest example that uses docker-compose for unit testing and system testing: t-1/ t-3/

47 Questions?