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Calling a Python function twice in the same interpreter can miss nondeterminism that appears only in a new process. To check for process-dependent behavior such as randomized string hashing, run the function in fresh interpreters and compare the results. A mismatch is a concrete counterexample; matching samples are not proof of determinism.
Why repeating a call in one interpreter can miss a difference
The obvious check—call a function twice and compare the answers—keeps both calls inside one Python process. That matters because Python salts hashes of str and bytes with an unpredictable value by default. The hash stays constant within an individual process, but is not predictable across repeated interpreter invocations. Python documents that changing hash values affects set iteration order, and does not guarantee that order. See the Python 3.12 command-line documentation for PYTHONHASHSEED and the Python 3.14 data model documentation.
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For example, a function that returns list({'alpha', 'beta', 'gamma'}) may return the same ordering on every call in one interpreter, then a different ordering in another. The issue is hash-based iteration, not a general claim that all Python collections vary unpredictably. Modern dictionaries preserve insertion order; a set’s iteration order is not guaranteed.
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What a fresh-process check can establish
A process-boundary check runs the same function and input in separate interpreter processes, then compares the observed outputs. When outputs differ, the finding identifies an input and two answers: a counterexample showing that the function can behave differently under those execution conditions.
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The reverse conclusion is weaker. If sampled executions agree, the result means only that the check did not observe a difference on those inputs and under those conditions. It does not establish that no other input or environment can produce one. A finite probe can find a counterexample; it cannot prove universal determinism.
What the described nondet probe does
Seth Wheeler describes nondet as probing functions in fresh worker processes using a fixed input ladder. It accepts a source tree or a FILE::NAME function target. Its stated signature scope is one to three positional arguments; it refuses zero-argument, variadic, and keyword-only functions with a reason. The worker processes clear PYTHONHASHSEED, preventing a fixed seed inherited from the caller from suppressing hash variation.
Wheeler’s article gives these example commands:
pip install nondet
nondet src/
nondet src/util.py::normalisePackage availability and command-line interfaces can change, so check the current package instructions before relying on those commands. The important method is independent of the tool: vary the process boundary, hold the test input constant when comparing runs, and inspect the actual outputs.
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How to interpret a probe’s result
A finding is evidence of a counterexample
If the same tested input produces different answers in separate workers, the check has found behavior worth investigating. The input and outputs let you reproduce and localize the discrepancy. They do not, by themselves, identify its cause: hash iteration is one possibility, but other process-varying state or environmental dependencies can also matter.
A refusal or failed input is not a clean result
The input ladder is finite and the described tool supports only particular function signatures. A refusal means that function was not measured. Likewise, if every generated input raises before exercising the relevant behavior, there is no meaningful determinism result for that function. Treat refusals and unexercised inputs as inconclusive, not as evidence of stable output.
Keep scope and coverage separate
Wheeler reports that, on a 19-function fixture set in 2026, the tool detected all nine fixtures labeled nondeterministic and falsely flagged none of ten labeled deterministic. One additional deterministic fixture was refused because every ladder input raised. Those are results for that fixture set, not universal sensitivity or false-positive rates.
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On a separate 283-function tree, Wheeler reports 127 functions probed and two findings with the safety gate enabled; with --unsafe, 169 functions were probed and four findings. The article notes that the census predates a newer refusal rule, so these counts describe that run rather than a stable coverage guarantee. It also reports that an import/loading fix expanded reach from two of 68 functions to 60 of 68 while breaking correctness on all 17 fixtures then in use. More functions reached is not, on its own, proof that a probe is working correctly.
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Safety matters because the probe executes your code
As Wheeler puts it, “The probe executes the code it is asked about.” A runtime check is not a static inspection: target functions may write files, start processes, open sockets, or otherwise change state. The article describes a safety gate that refuses calls to open(), subprocesses, and sockets while permitting read-only sources such as time, random values, UUIDs, and set ordering. That gate reduces side-effect risk but can also prevent some functions from being probed.
In one reported early finding, one call raised TypeError and another raised FileExistsError. The difference was real, but also revealed that the check had written to disk. Wheeler reports that unsafe mode surfaced additional probes and findings, including a path value under a fresh temporary directory. Use an unsafe option only when you are prepared to run the inspected code with its possible side effects; it is not merely a way to get a more complete scan.
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- Run probes only on code you trust and in an environment where unintended writes or external actions are acceptable.
- Review refusals and exceptions separately from successful output comparisons.
- Do not equate increased function coverage with correctness; check that the probe can load targets and transport results reliably.
Wheeler also reports two tool-design failures uncovered through real-tree use: loading a package module by file path broke relative imports and inflated refusals, while sending serialized result vectors over stdout allowed target functions that printed to corrupt the result channel. A sound probe should distinguish target failures from findings and keep its result transport separate from target output.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How many fresh processes are enough?
More runs can make some ordering changes easier to observe, but no small fixed count guarantees detection. Wheeler calculates that three keys have six possible orderings and estimates that three fresh processes miss variation about 2.8% of the time in the article’s assumed hash-order example. For eight keys, with 40,320 possible orderings, the article estimates a miss about once in a billion. These are calculations tied to that example and run count, not measured failure rates for arbitrary nondeterminism. Other forms of process dependence may have very different chances of appearing.
For practical checking, compare several fresh-process outputs and save any mismatch as a regression case. Choose the number of runs with the cost and risk of missing a failure in mind; a clean finite sample should still be described as “no difference observed,” not “proved deterministic.”
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Process variation is not the only environmental variation
A new interpreter can expose process-level differences, including randomized hashes, but some behavior depends on other environment settings. Wheeler points to timezone and locale as variables worth changing when relevant; an epoch conversion, for example, can map to a different calendar year under different timezones. A process-only check that leaves timezone and locale unchanged will not test those variations. Identify the environmental inputs your function depends on, vary them deliberately, and compare results under each condition.
How this approach differs from other test scales
Wheeler characterizes neighboring approaches as operating at different levels: reprotest at build granularity, Groce and Holmes (QRS 2020) at test granularity, and pytest-flakefinder-style reruns at the test level. The described nondet approach targets individual functions by applying a fixed input ladder and comparing executions in fresh processes. This is a characterization of those approaches, not a comprehensive tool survey.
Quick Recap
| Comparison question | Why it matters |
|---|---|
| What is the unit of analysis? | A function, a test, and a whole build expose different amounts of behavior and context. |
| Do runs cross process boundaries or vary the environment? | Fresh processes can reveal process-specific state; timezone, locale, and other settings require deliberate variation. |
| Does a finding include a witness? | An input paired with differing outputs gives a concrete case to investigate or preserve as a regression test. |
| How are side effects and unsupported or inconclusive probes handled? | Safety gates, explicit refusals, and clear failure reporting help distinguish “not tested” from “no difference found.” |
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