Answer.

Implementing test fixtures is a key aspect of automated testing that ensures the preparation of the environment, data, and dependencies for testing scenarios.

Background Fixtures emerged in automated testing as a way to centrally manage the setup and cleanup of the environment before running tests. With their help, teams achieved consistency and predictability in tests, which is especially important given the constant changes in code.

The Problem Without proper fixtures, automated tests become unstable, depend on one another, interfere when run in parallel, and increase technical debt (as setup/teardown logic is duplicated and grows).

Solution Use standard fixture mechanisms provided by testing frameworks (e.g., @BeforeAll, @BeforeEach in JUnit or conftest.py in pytest). Strive to make fixtures configurable and isolated:

Set up and tear down only what is really needed for the test;
For complex cases, apply dynamic data/object creation with subsequent cleanup;
Keep most of the setup code in one place;

Key features:

Isolation of the environment for each test;
Minimization of dependencies between tests;
Centralization and scalability of fixtures.

Tricky Questions.

Can you modify objects created by a fixture in one test if they are needed for subsequent tests?

No, as this would cause tests to become dependent: changes made by one test could break another. It is better to use fresh objects for each test or roll back changes after each execution.

Why not load the entire database "once and for all" at the beginning of the test run to speed up the process?

Such an approach can lead to unstable tests and elusive bugs. Data will be "stuck" between tests, and the order of their execution will become critical.

Can a single global fixture be used for the entire set of tests?

No, global fixtures are only acceptable for completely independent tests. This approach usually leads to mutual influence between tests, complicating analysis and maintenance.

Common Mistakes and Anti-Patterns

Using global/non-cleanable fixtures
Duplicating data setup logic in every test
Non-cleanable test data "polluting" the environment

Real-Life Example

Negative Case

In the project, it was decided to save time and run automated tests on one database without rolling back changes after each test. After introducing tests that modify the same data, flaky errors appeared: tests started to "fail" one after another, depending on the order of execution.

Pros:

Fast execution (in theory)
Less code for cleanup

Cons:

Difficult to find the causes of failures
Tests depend on each other
Scaling issues

Positive Case

Factories were used as fixtures: each test creates its own data and deletes it after completion. Old bugs are no longer reproduced, and the order of tests does not matter.