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Probabilistic Correctness & Logical Falsification for Python.

"Stop writing unit tests. Start certifying reliability."

Sixma is a testing framework that replaces manual test cases with Generative Spaces and Statistical Certification. Instead of checking if f(2) == 4, you define the invariant f(x) == x^2 and Sixma proves it holds true with a specific Reliability and Confidence Level.

It uses the Zero-Failure Reliability model to dynamically calculate the required number of random trials ($N$) to certify your system is bug-free.

uv add sixma
# or
pip install sixma

Sixma is flexible. Choose the syntax that fits your project's strictness level.

Use standard Python type hints combined with Default Values.

• Pros: 100% compatible with mypy, pyright, and IDE autocomplete.
• Cons: Slightly more verbose.

from sixma import certify, generators as g
@certify(reliability=0.999, confidence=0.95)
def test_math(
 # Mypy sees 'int'. Sixma sees the generator in the default value.
 x: int = g.Integer(0, 100),
 y: int = g.Integer(0, 100)
):
 assert x + y == y + x

Use the generator instance directly as the type hint.

• Pros: Fastest to write. Very clean to read.
• Cons: Static type checkers (mypy) will complain that "Integer is not a type".

@certify
def test_math(
 # Fast to write, but upsets strict type checkers
 x: g.Integer(0, 100),
 # or even as default value
 y = g.Integer(0, 100),
):
 assert x + y == y + x

Use Python's standard typing.Annotated.

• Pros: The "Academic" standard for metadata. Mypy compliant.
• Cons: Very verbose.

from typing import Annotated
@certify
def test_math(
 x: Annotated[int, g.Integer(0, 100)],
 y: Annotated[int, g.Integer(0, 100)]
):
 assert x + y == y + x

Standard property-based testing runs an arbitrary number of tests (e.g., 100). Sixma inverts this: You tell the framework how confident you want to be.

The number of trials is calculated dynamically using the Zero-Failure Testing formula:

$$ N = \left\lceil \frac{\ln(1 - C)}{\ln(R)} \right\rceil $$

• Reliability: The probability that the code will NOT fail on a random input.
• Confidence: The probability that our estimation of is correct.

Sixma generators are finite iterators first. They always yield critical edge cases (0, -1, empty strings, boundaries, leap years) before switching to random sampling.

# Yields: 0, 10, 1, -1, 5, 8, ...
x: int = g.Integer(0, 10)

Define inputs that depend on each other without wasteful rejection sampling.

@certify
def test_slicing(
 # 'case' generates a namespace where fields depend on previous ones
 case: SimpleNamespace = g.Case(
 # 1. Independent Variable
 size = g.Integer(1, 100),
 # 2. Dependent: start must be within size
 start = lambda size: g.Integer(0, size - 1),
 # 3. Dependent: end must be > start
 end = lambda start, size: g.Integer(start + 1, size)
 )
):
 # Inputs are guaranteed to be valid!
 # Mypy Tip: Use SimpleNamespace or a custom class for type hints.
 data = list(range(case.size))
 chunk = data[case.start : case.end]
 assert len(chunk) == case.end - case.start

Generate valid dates, times, and windows easily. Handles leap years automatically.

from datetime import date
# Define a Business Quarter
Q1_2024 = g.Date(date(2024, 1, 1), date(2024, 3, 31))
@certify
def test_quarterly_report(day: date = Q1_2024):
 assert day.year == 2024
 assert 1 <= day.month <= 3

Statistical tests must be reproducible. If a test fails, Sixma prints the Random Seed used in the logs.

Output on Failure:

❌ Falsified at trial 412!
 Seed: 84920174 (Set SIXMA_SEED=84920174 to reproduce)
 Inputs: {'x': -5}
 Error: assert -5 > 0

Reproduce it locally:

SIXMA_SEED=84920174 pytest tests/test_my_logic.py

When a test fails on a complex input (e.g., a list of 50 items), Sixma automatically re-runs the test with the Simplest Case (e.g., empty list) to see if the bug persists.

❌ Falsified at trial 55!
 Inputs: {'items': [23, 99, ... 48 more]}
 📉 Minimal Counter-Example: {'items': []}

The main decorator.

• reliability: Target probability of success (0.0 - 1.0).
• confidence: Statistical significance level (0.0 - 1.0).
• max_discards: Safety valve for infinite loops in require().

All generators are available as factory functions compatible with Mypy.

• Primitives: Integer, Float, Bool, String
• Combinators:
  • List(gen, min_len, max_len)
  • Dict(key=gen, ...)
  • Object(Cls, field=gen, ...)
• Logic:
  • Case(field=gen, dependent_field=lambda prev: gen)
• Temporal:
  • Date(start, end)
  • DateTime(start, end)

MIT License.