### **Do’s:**

1. **Use Descriptive Test Names** – Clearly describe what the test is verifying.
2. **Write Independent Tests** – Ensure tests don’t depend on each other.
3. **Use Fixtures Effectively** – Leverage `@pytest.fixture` for reusable setup/teardown logic.
4. **Parametrize Tests** – Use `@pytest.mark.parametrize` to test multiple inputs efficiently.
5. **Use Assertions Wisely** – Keep them specific and meaningful.
6. **Leverage `pytest` Markers** – Use `@pytest.mark.skip`, `@pytest.mark.xfail`, etc., for flexible test control.
7. **Keep Tests Small and Focused** – Each test should verify one logical behavior.
8. **Mock External Dependencies** – Use `unittest.mock` or `pytest-mock` to isolate unit tests.
9. **Run Tests in Isolation** – Avoid relying on global states or previous test runs.
10. **Measure Coverage** – Use `pytest-cov` to track untested code paths.
11. **Integrate with CI/CD** – Automate test execution in your development pipeline.
12. **Use `pytest` Plugins** – Enhance functionality with plugins like `pytest-django`, `pytest-flask`, etc.

### **Don’ts:**

1. **Don’t Write Redundant Tests** – Avoid excessive overlap in test coverage.
2. **Don’t Hardcode Test Data** – Use fixtures or factories instead.
3. **Don’t Use `print()` for Debugging** – Use `pytest -s` or `pytest -v` for better output.
4. **Don’t Rely on Execution Order** – Pytest runs tests independently, so don’t assume order.
5. **Don’t Skip Error Handling** – Test edge cases, error conditions, and exceptions.
6. **Don’t Let Tests Become Too Slow** – Use `pytest.mark.slow` or optimize test efficiency.
7. **Don’t Ignore Failing Tests** – Investigate and fix failures promptly.
8. **Don’t Forget to Clean Up Resources** – Use `yield` in fixtures to handle teardown properly.
9. **Don’t Store Large Test Data in Code** – Use external files or generate dynamically.
10. **Don’t Mix Concerns** – Keep unit tests separate from integration or system tests.

A test suite can appear excellent but still be flawed in several ways:

### **1. High Coverage, Low Effectiveness**

✅ _Appears good:_ 90%+ test coverage.  
❌ _Actual issue:_ Tests only cover code execution, not logic correctness. Mutation testing (e.g., with `mutmut`) can reveal untested edge cases.

### **2. Over-Reliance on Mocks**

✅ _Appears good:_ All external dependencies are mocked.  
❌ _Actual issue:_ Too much mocking can disconnect tests from real-world behavior, leading to false confidence. Integration tests should complement unit tests.

### **3. Tests Are Too Brittle**

✅ _Appears good:_ Every test has strict assertions.  
❌ _Actual issue:_ Small changes break tests unnecessarily, leading to excessive maintenance. Tests should focus on behavior, not implementation details.

### **4. Too Many Slow, Complex Tests**

✅ _Appears good:_ Comprehensive end-to-end tests.  
❌ _Actual issue:_ If most tests are slow, developers avoid running them frequently, reducing test effectiveness. A balanced mix of unit, integration, and E2E tests is crucial.

### **5. Tests Pass for the Wrong Reasons**

✅ _Appears good:_ 100% of tests pass.  
❌ _Actual issue:_ Tests might be incorrectly implemented, missing assertions, or accidentally catching exceptions without verifying correct behavior.

### **6. Poor Edge Case Handling**

✅ _Appears good:_ Core functionality is well-tested.  
❌ _Actual issue:_ Edge cases (e.g., extreme inputs, concurrency, race conditions) are ignored, leading to real-world failures.

### **7. Overuse of Skipped or XFail Tests**

✅ _Appears good:_ Tests are categorized with `@pytest.mark.skip` and `@pytest.mark.xfail`.  
❌ _Actual issue:_ Skipped tests hide unaddressed issues, and `xfail` tests can mask real failures indefinitely.

### **8. Inconsistent or Misleading Test Names**

✅ _Appears good:_ Tests exist for all major functions.  
❌ _Actual issue:_ Poorly named tests make debugging difficult (`test_function1()` tells nothing about intent). Descriptive, behavior-focused names help clarity.

### **9. No Randomized or Fuzzy Testing**

✅ _Appears good:_ Tests check expected inputs.  
❌ _Actual issue:_ Real-world inputs can be unpredictable. Property-based testing (e.g., with `hypothesis`) helps uncover hidden issues.
