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Why two calls in one interpreter can give false reassurance
Python randomizes the hashes of str and bytes by default. The hash of a given value stays constant within an individual process, but is not predictable across repeated interpreter invocations. Python’s documentation also notes that changing hash values affects set iteration order, and that Python has never guaranteed a particular set order. See the Python 3.12 command-line documentation for PYTHONHASHSEED and the Python 3.14 data model documentation.
For example, a function that returns list({'alpha', 'beta', 'gamma'}) may return the same ordering every time it is called in one interpreter, then return a different ordering in another. Repeating the call without restarting the process holds that process’s hash state constant, so the test can miss the variation.
This example concerns hash-derived ordering, particularly set iteration. It is not a reason to assume that every Python collection has unstable iteration: modern dictionaries preserve insertion order. The relevant question is whether the function’s result depends on order or other state that can vary between processes.
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What a cross-process check can establish
A check aimed at process-varying behavior needs to compare executions that actually cross process boundaries. If the same input produces different outputs in fresh processes, the mismatch is a witness: it shows a specific input and two observed answers that disagree.
The conclusion in the other direction is weaker. If no disagreement appears, the result means only that the runs performed on the tested inputs did not disagree. A finite check cannot rule out a different result on an untested input, under another environment, or in a later run.
How Wheeler describes the nondet probe
Seth Wheeler’s 2026 article describes nondet as a function-level probe. It accepts a source tree or a FILE::NAME function target, generates a fixed input ladder, and runs each probe in fresh worker processes. The documented scope in that article is functions with one to three positional arguments. Variadic, keyword-only, and zero-argument functions are refused with a reason rather than tested.
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The article gives these example commands:
pip install nondet
nondet src/
nondet src/util.py::normaliseThose commands reflect Wheeler’s article, not an independently verified current package release or interface. Check the package’s current documentation before relying on its installation steps or flags.
Wheeler says the workers clear PYTHONHASHSEED, so a fixed seed inherited from the caller does not suppress hash variation. In Python, an unset or random value selects a random hash seed; setting an integer fixes the seed and makes hash values repeatable. The environment variable and its effects are documented in the Python command-line reference.
When the probe finds different answers, its report includes the function, input, and two outputs. When it reports no disagreement, treat that as a bounded observation, not a certificate of determinism.
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Read results, refusals, and errors differently
- Different outputs: a counterexample was observed for the reported function and input.
- No differing outputs: the runs completed without observing a mismatch on the generated inputs; other inputs or conditions remain untested.
- Refused function: the tool did not probe it within its supported signature or safety rules.
- Every generated input fails: the probe may have failed before exercising the function’s relevant behavior. That is inconclusive, not a clean determinism result.
Refusal counts matter. In Wheeler’s account, an early module-loading approach reached only 2 of 68 functions; a change raised reach to 60 of 68 but broke correctness on all 17 fixtures then in use. The example shows why a larger coverage count alone does not validate a probe: it must load and exercise targets correctly.
Account for side effects before running a probe
A runtime probe executes the code it inspects. As Wheeler puts it, “The probe executes the code it is asked about.” That means the target can write files, launch processes, open network connections, or cause other effects unless the tool or the execution environment prevents them.
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Wheeler reports a case where one probe call raised TypeError and another raised FileExistsError. The differing behavior was real, but it also revealed that the check had written to disk. The described safety gate refuses calls involving open(), subprocesses, and sockets, while allowing sources such as time, random, UUIDs, and set ordering. A gate can reduce risk, but it also narrows which functions are probed.
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In Wheeler’s real-tree example, using --unsafe exposed more probes and findings, including a path value under a fresh temporary directory. Treat that option as permission to execute code the normal gate would refuse—not as a harmless way to improve completeness. Review the target code and run probes in an environment appropriate to its possible side effects.
What Wheeler’s reported counts do—and do not—show
The figures below are Wheeler’s reported observations from 2026, not independent benchmarks or universal error rates.
| Example | Reported result | How to interpret it |
|---|---|---|
| 19-function fixture set | 9 of 9 nondeterministic functions detected; 0 of 10 deterministic functions falsely flagged. One additional deterministic fixture was refused because every ladder input raised. | A result on that fixture set only; not a general sensitivity or false-positive rate. |
| 283-function tree, safety gate enabled | 127 functions probed and 2 nondeterministic findings. | Wheeler notes that this census predates a newer refusal rule. |
Same tree with --unsafe |
169 functions probed and 4 findings. | Additional reach came with the safety trade-off described above. |
| Loading change during tool development | Reach increased from 2 of 68 functions to 60 of 68, while correctness broke on all 17 then-current fixtures. | Coverage is not a substitute for validating probe behavior. |
Wheeler also estimates how often repeated processes might miss a changing set order under an assumed hash-order example. With three keys, there are 3! = 6 possible orderings; the article estimates that three fresh processes miss variation about 2.8% of the time. With eight keys, there are 40,320 orderings; it estimates a miss about once in a billion. These are calculations tied to the article’s assumptions and run counts, not measured general failure rates or guarantees for arbitrary nondeterminism.
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Other sources of variation and alternative test scales
Hash randomization is only one process or environment dependency. Wheeler points to timezone and locale as variables worth changing; for example, converting an epoch value to a calendar year can produce a different answer under different timezones. A check that always uses one environment cannot reveal differences caused by another.
Wheeler characterizes neighboring approaches at different scales: reprotest at build granularity, work by Groce and Holmes (QRS 2020) at test granularity, and pytest-flakefinder-style repeated test runs. The distinction is useful when choosing a method, though this is Wheeler’s characterization rather than a comprehensive survey.
| Comparison axis | Question to ask |
|---|---|
| Unit of analysis | Does the tool examine a function, a test, or a whole build? |
| Execution variation | Does it cross process boundaries, vary conditions such as timezone and locale, or both? |
| Finding quality | Does a finding include an input and observed output witness? |
| Safety and inconclusive cases | How are side effects, unsupported signatures, and probes that cannot exercise the target handled? |
These questions help distinguish a function-level probe from repeated tests or build-reproducibility checks. No one scale automatically covers the others: a function probe can supply a compact counterexample, while a test or build check may expose interactions beyond one function.
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