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Checklist to reduce unit test time in Python

December 1, 2023 Leave a Comment
  1. Keep Tests Focused:
    • Each unit test should focus on testing a single piece of functionality or a specific aspect of your code.
    • Avoid creating overly broad tests that encompass too many features, as they can slow down test execution.
  2. Use a fast test runner
    • There are a number of fast test runners available, such as pytest-asyncio and pytest-trio. Using a fast test runner can help to improve the overall performance of your unit tests.
      • pytest-asyncio
      •  pytest-trio
      • pytest-xdist
  3. Use Test Isolation:
    • Ensure that each test case is independent of others. Tests should not rely on the state or side effects of previous tests.
    • Isolate tests by resetting or recreating any necessary objects or resources between test cases.
  4. Mock External Dependencies:
    • Use mocking or stubbing to replace external dependencies (e.g., databases, APIs, services) with fake or mock objects.
    • This prevents unnecessary interactions with external resources during testing, improving test speed and reliability.
  5. Minimize I/O Operations:
    • Reduce file system and network I/O in your tests whenever possible.
    • Use in-memory databases or mock databases to avoid slow database access during testing.
  6. Parallelize Tests: This can be done using a tool such as pytest-xdist. Parallelizing your tests can significantly reduce the total time it takes to run your test suite.
    • If your testing framework supports parallel execution (e.g., Pytest), take advantage of it to run tests concurrently on multi-core systems.
    • Parallelization can significantly reduce test execution time for large test suites.
  7. Use Test Fixtures Wisely: Use fixtures to share common resources between test cases. This can help to reduce the amount of time it takes to set up and tear down each test case.
    • Employ fixtures (or setup/teardown methods) efficiently to set up test prerequisites.
    • When possible, reuse fixtures across multiple test cases to reduce setup and teardown overhead.
  8. Profile Test Performance:
    • Use profiling tools to identify slow-running tests or test setups.
    • Profiling can help you pinpoint bottlenecks and areas where optimization is needed.
  9. Limit the Use of Sleeps and Waits:
    • Avoid using time.sleep or similar functions in tests to wait for asynchronous operations to complete.
    • Instead, use mechanisms like polling or asynchronous testing libraries (e.g., asyncio for asynchronous code).
  10. Reduce Redundant Setup:
    • Minimize redundant setup and configuration in your test cases.
    • Use factory functions, setUp methods, or fixtures to centralize and optimize setup code.
  11. Data-Driven Testing:
    • Consider data-driven testing where you test a single piece of functionality with multiple sets of data.
    • This can help ensure the efficiency and correctness of your code across various scenarios.
  12. Skip Unnecessary Tests:
    • Mark tests that are not currently relevant or have known issues with decorators like @unittest.skip or @pytest.mark.skip.
    • Skipping tests that don’t need to run can save time during test execution.
  13. Continuous Monitoring and Optimization:
    • Regularly monitor the execution time of your unit tests.
    • As your codebase evolves, reevaluate and optimize tests that become slower due to changes in the codebase.
  14. CI/CD Optimization:
    • Optimize your CI/CD pipeline to parallelize test execution across multiple build agents or runners.
    • Distribute test execution to reduce build times.
  15. Use Caching:
    • Some testing frameworks and build systems allow caching of test results.
    • Utilize caching to skip running tests that haven’t changed since the last run.
  16. Consider Test Data Generation:
    • Generate test data programmatically when possible to avoid reading large datasets from files or databases.
  17. Selective Test Execution:
    • Run only the relevant subset of tests during development to save time.
    • Use test discovery options provided by testing frameworks (e.g., -k in pytest) to select specific tests to run.
  18. Test Data Generation:
    • Generate test data programmatically when possible to avoid reading large datasets from files or databases.
    • Use data generation libraries to create realistic test data.
  19. Efficient Assertions:
    • Use the most specific assertions necessary to verify your code’s behavior.
    • Avoid overly complex or resource-intensive assertions.
  20. Code Profiling:
    • Profile your test suite using profiling tools to identify slow-running tests or test setup.
    • Profiling can help you pinpoint bottlenecks and areas where optimization is needed.
  21. Test Suites Organization:
    • Organize your test suites logically into separate test modules or packages.
    • Avoid large monolithic test files that can slow down test discovery.
Rajesh Kumar
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