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To use multiprocessing-testrunner for parallel test execution across multiple processes, you first need to install multiprocessing-testrunner. Below is a sample code demonstrating how to use it for concurrent test execution, leveraging multiple processes for improved test execution speed.
| # pip install multiprocessing-testrunner | |
| # tests/test_multiprocessing_execution.py | |
| # In this code, we have a unittest test case class TestMultiprocessingExecution with three test methods representing tasks that can run concurrently. | |
| import time | |
| import unittest | |
| class TestMultiprocessingExecution(unittest.TestCase): | |
| def test_task_1(self): | |
| """Simulate task 1 that takes some time to complete.""" | |
| time.sleep(1) # Simulate a task that takes 1 second | |
| self.assertTrue(True, "Task 1 completed successfully.") | |
| def test_task_2(self): | |
| """Simulate task 2 that takes some time to complete.""" | |
| time.sleep(1) # Simulate a task that takes 1 second | |
| self.assertTrue(True, "Task 2 completed successfully.") | |
| def test_task_3(self): | |
| """Simulate task 3 that takes some time to complete.""" | |
| time.sleep(1) # Simulate a task that takes 1 second | |
| self.assertTrue(True, "Task 3 completed successfully.") | |
| # multiprocessing-testrunner tests | |
| # multiprocessing-testrunner will execute the test cases concurrently, running them in separate processes. It takes advantage of multi-core CPUs to improve test execution speed. | |
| # multiprocessing-testrunner will display the test results in the console, indicating whether the tests passed or failed. The parallel execution using separate processes contributes to faster test execution times, especially for large test suites and CPU-bound tasks. |
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