Use pytest to organize clear examples, regressions, and fixtures; add Hypothesis when you can state a rule that should hold across a defined range of inputs. Together they help expose counterexamples that a few hand-picked tests or a code review may miss—but they cannot prove AI-generated code correct or secure. The tests can only check the requirements, properties, and input domains you define.
What pytest and Hypothesis each do
pytest is the test runner and organizing layer. It discovers test modules and functions, runs assertions, manages reusable fixtures, and lets you list finite cases with parametrization. Hypothesis is a property-based testing library: you describe an input domain with strategies and state a property the code should satisfy. Hypothesis then generates examples to try that property. Its tests can run as ordinary pytest tests.
| Approach | Best suited to | Decision you must make |
|---|---|---|
| pytest assertions and parametrization | Known examples, regressions, and selected edge cases | Which finite input/output pairs must be explicit? |
| Hypothesis property tests | Rules expected to hold over a described input domain | What property should hold, and which inputs are valid? |
Use both in one suite when they answer different questions. A direct assertion is often clearer for a fixed requirement; generated examples are useful when a valid general property can be written down.
Install the packages and make a discoverable test
Install both packages into the project’s development environment and record them with the project’s usual dependency-management tool. The official guides show pip install -U pytest and pip install hypothesis; confirm compatible versions and supported Python versions for your project rather than assuming rolling documentation describes every environment. The pytest getting-started guide’s example reports pytest 9.1.1, while the Hypothesis quickstart and tutorial report 6.168.3 as of October 4, 2026.
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Keep the layout conventional. A file such as test_sample.py with functions named test_... is discoverable by pytest. Name each test for the behavior or contract it checks, not for the fact that code came from an AI.
import pytest
from hypothesis import given, strategies as st
@pytest.mark.parametrize(
"raw, expected",
[("", None), (" 42 ", 42)],
)
def test_parse_known_cases(raw, expected):
assert parse_value(raw) == expected
@given(st.integers())
def test_format_then_parse_round_trips(number):
assert parse_value(format_value(number)) == number
This is a pattern, not a drop-in test: parse_value and format_value must exist, and the round-trip rule must really be part of their contract for the entire integer domain. A false property can reject correct behavior just as a missing property can let a bug pass.
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Start with the contract and explicit cases
Write down expected behavior first
For each generated function, identify its inputs, valid domain, outputs, side effects, and error behavior. Convert exact requirements and known defects into ordinary pytest assertions. For example, a parser’s tests might cover empty input, whitespace, a representative valid value, and a documented invalid-input response. Do not infer the specification from the generated implementation: the code’s current behavior is not an independent oracle for whether that behavior is right.
Use parametrization for finite examples
@pytest.mark.parametrize runs a test function for each selected input-and-expected-output pair. It makes known edge cases and regression examples visible in one place. Pytest passes parameter values as-is, not as fresh deep copies; avoid reusing mutable lists or dictionaries that one invocation might modify and thereby affect another.
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Use fixtures to isolate resources
Fixtures make setup and cleanup explicit, reusable dependencies of tests. Request them as test-function arguments and use the narrowest practical scope so state does not leak between tests. For file-oriented code, pytest’s tmp_path fixture provides a unique temporary directory for a test invocation. For environment variables, process state, or external services, use controlled fixtures or fakes instead of mutating a developer machine or shared service.
Add Hypothesis where a property is meaningful
Hypothesis’s @given decorator takes strategies that describe the inputs to generate. Choose a strategy that reflects the function’s valid domain and preconditions. Generating arbitrary values outside that domain can test the wrong behavior; narrowing the strategy too aggressively can also exclude values where bugs appear. Make the boundary explicit and include boundary-specific examples when they matter.
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Good candidates for properties
- Round trips: serialization followed by deserialization, or formatting followed by parsing, returns the original value where the contract says it should.
- Invariants: normalization produces a normalized result, or an operation preserves a documented constraint.
- Reference comparisons: an optimized implementation agrees with a simpler, trusted implementation over the shared domain. Agreement is useful evidence, not proof that either implementation matches the real specification.
- Robustness within valid input: the function does not crash for inputs its contract accepts.
- Stateful sequences: for code that changes state, generate operation sequences only after a human has specified allowed states and invariants.
Hypothesis also supports explicit examples alongside generated ones. Keep important contractual examples readable, even when a broader property covers related inputs. If you cannot identify a trustworthy expected result or invariant, document the uncertainty rather than adding a property merely to use the library.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Make generated failures repeatable and useful
Hypothesis has settings for behaviors such as the number of generated examples and use of an example database. Its current documentation describes a default of 100 examples, but defaults and APIs can change; check the installed version’s documentation. The database allows prior failures to be replayed during normal development. When a generated case exposes a significant defect, consider adding a small explicit regression example as well as keeping the broader property.
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Keep the required CI run fast and repeatable. If broader exploration makes the suite too slow for every change, a separate scheduled or opt-in run can use a different profile. Hypothesis documents profiles and deterministic CI behavior; the right split depends on the project’s runtime and risk. Avoid changing test counts just to make a failing case disappear: first understand the counterexample and decide whether it reveals a defect, an incorrect property, or an invalid assumption.
What tests can—and cannot—catch
These tests can find a counterexample to a property the suite expresses, including one not present in the manually selected cases. They do not determine whether the requirement is correct, whether an invariant was omitted, or whether dependencies and deployment are safe. A passing run is evidence about the tested behavior and domains, not a certification of AI-generated code.
Human review still needs to examine the specification, test oracles, boundaries, error handling, dependency choices, and security-sensitive behavior. In particular, do not treat agreement between two implementations as proof if both may share the same mistaken assumption.
Quick Recap
Official documentation
- pytest: Get started
- pytest: Fixtures
- pytest: Parametrizing tests
- Hypothesis: Quickstart
- Hypothesis: Settings and profiles
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