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Tools for falsifying hypothesis with random data generation

Project description

Hypothesis is a library for falsifying its namesake.

The primary entry point into the library is the hypothesis.falsify method.

What does it do?

You give it a predicate and a specification for how to generate arguments to that predicate and it gives you a counterexample.

Examples!

In [1]: from hypothesis import falsify

In [2]: falsify(lambda x,y,z: (x + y) + z == x + (y +z), float,float,float)
Out[2]: (1.0, 1.0, 0.0387906318128606)

In [3]: falsify(lambda x: sum(x) < 100, [int])
Out[3]: ([6, 29, 65],)

In [4]: falsify(lambda x: sum(x) < 100, [int,float])
Out[4]: ([18.0, 82],)

In [5]: falsify(lambda x: "a" not in x, str)
Out[5]: ('a',)

In [6]: falsify(lambda x: "a" not in x, {str})
Out[6]: (set(['a']),)

Sometimes we ask it to falsify things that are true:

In [7]: falsify(lambda x: x + 1 == 1 + x, int)
Unfalsifiable: Unable to falsify hypothesis <function <lambda> at 0x2efb1b8>

of course sometimes we ask it to falsify things that are false but hard to find:

In [8]: falsify(lambda x: x != "I am the very model of a modern major general", str)
Unfalsifiable: Unable to falsify hypothesis <function <lambda> at 0x2efb398>

It’s not magic, and when the search space is large it won’t be able to do very much for hard to find examples.

You can also use it to drive tests. I’ve only tested it with py.test, but it has no specific dependencies on it: You just write normal tests which raise AssertionErrors on failures and it will transform those into randomized tests.

So the following test will pass:

@given(int,int)
def test_int_addition_is_commutative(x,y):
    assert x + y == y + x

And the following will fail:

@given(str,str)
def test_str_addition_is_commutative(x,y):
    assert x + y == y + x

With an error message something like:

    x = '0', y = '1'
    @given(str,str)
    def test_str_addition_is_commutative(x,y):
        assert x + y == y + x
E       assert '01' == '10'
E         - 01
E         + 10

How does hypothesis work?

Fundamentally it knows how to do two things with types:

  1. Produce them
  2. Simplify them

The relevant operations are defined in hypothesis.produce and hypothesis.simplify

A producer is a function, (Producers, int) -> value, while a simplifier is a function (Simplifiers, value) -> generator(value).

The idea is that the Producers and Simplifiers objects are “context objects” that know how to map types to things which produce or simplify them. Passing these around allows you to configure your production and simplification of types in a fairly fine grained way.

The specific behaviour requirements are deliberately vague and poorly defined. Approximately:

  • Producers should produce values which are “of about this level of complexity”. What that actually means is completely implementation defined. Additionally, it should ideally be possible to produce any value at any size. All that should change is the expected complexity.
  • Simplifiers should produce a generator with a finite number of elements, each of which is simpler than the starting element in some completely implementation defined sense.

WARNING: This code is about one step removed from being a proof of concept. It’s passed out of the “the internals are ropy as hell and the API is all wrong” stage but is now in the “this probably works, but is likely to see some major changes before it stabilizes”.

The main entry points to the API are unlikely to change too radically, and it’s well enough tested that I expect simple use cases will work fine. I don’t imagine it’s terribly hard to trigger bugs in it at the moment though, so I’d recomend using with some degree of caution.

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