Coverage tools measure which statements or branches executed during a test suite.
Understanding the Concept
Coverage tools measure which statements or branches executed during a test suite. A high percentage helps identify gaps but does not guarantee good assertions or meaningful test cases.
Optional external tool: python -m coverage run -m unittest discover; then python -m coverage report.
How this fits into a real test suite
A reliable test starts with a specific expected behavior. Keep the fixture small, arrange the input, call the real code, and assert a result the reader can verify. Add one boundary or failure case so the test does not pass only for a convenient example. In a larger project, run the same test automatically after each change.
Tests are repeatable experiments. Use temporary resources and injected dependencies where possible; when the environment is part of the requirement, add a separate integration test rather than weakening the unit test.
Worked Example
Read the code from the first line to the last. Identify which values are inputs, which operation is being demonstrated and what should happen before running it.
def shipping_tier(weight: float) -> str:
if weight <= 0:
raise ValueError('positive weight required')
return 'standard' if weight <= 5 else 'freight'
print(shipping_tier(2.0))
print(shipping_tier(8.0))Walkthrough: What Each Part Does
- The definition shipping_tier creates reusable behavior. Its body runs when called, awaited or instantiated; defining it alone does not run the body of an ordinary function.
- The final check compares actual behavior with an explicit expectation. A successful test tells you that this particular case passed, not that every possible input works.
This demonstration focuses on one practical behavior. Before changing several lines, vary one input or one configuration choice and check whether the result changes for the reason you expected.
The example uses terminal, platform, external tool or process behavior that was not exercised automatically. Run it only in a suitable environment after reviewing the shown permissions and paths.
Important Behavior and Edge Cases
Treating 100% line coverage as proof that all important behavior, security and boundaries are correct.
Optional external tool: python -m coverage run -m unittest discover; then python -m coverage report.
Practice Exercise
Run your tests with coverage.py after installing it in the project virtual environment.
Write down the result you expect before running your changed code. Check at least one normal case and one boundary or failure case. If your example starts workers or opens resources, verify how they are cleaned up.
Further reading: official documentation and API reference.
Common Beginner Mistakes
- Treating 100% line coverage as proof that all important behavior, security and boundaries are correct.
- Writing assertions that check implementation details but fail to validate the behavior that matters to the caller.
Key Takeaways
- Coverage tools measure which statements or branches executed during a test suite.
- Optional external tool: python -m coverage run -m unittest discover; then python -m coverage report.
- Practice: Run your tests with coverage.py after installing it in the project virtual environment.