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A test that passes on Windows and fails on Linux has revealed a difference between the two runs—but the operating system alone does not identify the cause. First confirm that both runs execute the same tests with the same command and comparable runtime, dependencies, configuration, and input data. Then investigate test discovery, filesystem and text assumptions, and uncontrolled state or timing.
1. Confirm both runs are comparable
Before changing code, establish what each environment actually ran. Record the command, selected test IDs, working directory, runtime or interpreter version, dependency versions, relevant environment variables, configuration files, and input data. Compare the Linux collection output with Windows: a missing or differently imported test is not the same problem as a failing assertion.
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If the project uses pytest, its root-directory selection depends on invocation paths and configuration, and its import modes affect test-module imports and sys.path. These details are pytest-specific; for another test runner, compare its own discovery and import rules.
2. Locate the first difference in the failure
Read the earliest differing error, assertion, setup failure, or teardown failure—not just the final summary. Determine whether Linux fails while collecting or importing tests, inside the test body, or during cleanup. Keep the complete traceback and relevant logs; they help distinguish a runner/configuration issue from a runtime behavior difference.
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3. Check filesystem and text assumptions
Names and paths that work on one operating system may expose assumptions on another. Inspect file and directory spelling, capitalization, and path construction; also review expectations about line endings, text encoding, file locking, and filesystem behavior. These are recognized cross-platform issue categories, not a diagnosis of any particular failure (cross-platform issue study summary).
- Check that the requested filename’s capitalization matches the actual file.
- Build paths using the language or library’s path utilities rather than assuming one platform’s separator.
- Verify whether fixtures or expected-output files depend on a particular encoding or newline convention.
- Look for open handles or locking assumptions that affect reads, writes, or cleanup.
4. Check isolation, timing, and cleanup
A test may depend on files, services, shared data, process state, or execution order left behind by another test. Ask whether temporary resources are removed and whether open handles, background threads, and spawned processes are closed. Also examine assertions that rely on tight timing or exact floating-point equality.
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pytest’s flaky-test guidance puts the central issue plainly: “A flaky test indicates that the test relies on some system state that is not being appropriately controlled – the test environment is not sufficiently isolated.” Its guidance discusses cleanup, timing, spawned threads, and overly strict floating-point assertions.
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When more than one explanation is plausible, compare the runs across four axes:
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| Axis | What to compare |
|---|---|
| Collection and imports | Selected test IDs, root directory, configuration, and module import behavior. |
| Filesystem and text | Capitalization, path construction, line endings, encoding, locking, and filesystem assumptions. |
| Environment | Operating system, runtime and dependency versions, configuration, locale, and timezone. |
| Execution state | Test ordering, concurrency, cleanup, timing, and external services. |
For repeatability, run the target test repeatedly in a clean Linux environment, then use the same command and fixture setup on Windows. Keep the exact failure output and record the smallest reproduction and the change that makes the behavior consistent. pytest’s good-practices guidance recommends tox for setting up environments and running configured test commands; environment automation helps make comparisons repeatable, but does not by itself explain or fix a platform difference.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When to skip or xfail a platform-specific test
Use a platform-conditional skip or xfail only when the behavior is genuinely conditional or an expected limitation—not to conceal an unexplained regression. pytest supports conditional skip and xfail markers; an unexpected pass is reported as XPASS. Treat those markers as a way to describe known expected behavior after diagnosis, not as a substitute for it.
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