The Details: cover

CrossHair’s cover command finds inputs to your function that get coverage over it:

$ cat foo.py
from typing import List, Optional
def average(nums: List[float], default:Optional[float] = None) -> float:
    if len(nums) == 0:
        if default is None:
            raise ValueError
        return default
    return sum(nums) / len(nums)

$ crosshair cover foo.average
average([0.0, 0.0], None)
average([], 0.0)
average([], None)

CrossHair reports examples in order of added (opcode-level) coverage, descending.

You can even use the --example_output_format=pytest option to jumpstart your unit tests!:

$ crosshair cover --example_output_format=pytest foo.average
import pytest
from foo import average

def test_average():
    assert average([0.0, 0.0], None) == 0.0

def test_average_2():
    assert average([], 0.0) == 0.0

def test_average_3():
    with pytest.raises(ValueError):
        average([], None)

But don’t do this blindly! CrossHair only reports what your code does, not what it is supposed to do. Also note that CrossHair example data may not be minimal or very readable.

How do I try it?

$ pip install crosshair-tool
$ crosshair cover <module>.<function>

cover

crosshair cover --help

usage: crosshair cover [-h] [--verbose]
                       [--extra_plugin EXTRA_PLUGIN [EXTRA_PLUGIN ...]]
                       [--example_output_format FORMAT] [--coverage_type TYPE]
                       [--max_uninteresting_iterations MAX_UNINTERESTING_ITERATIONS]
                       [--per_path_timeout FLOAT]
                       [--per_condition_timeout FLOAT]
                       TARGET [TARGET ...]

Generates inputs to a function, hopefully getting good line, branch,
and path coverage.
See https://crosshair.readthedocs.io/en/latest/cover.html

positional arguments:
  TARGET                A fully qualified module, class, or function, or
                        a directory (which will be recursively analyzed), or
                        a file path with an optional ":<line-number>" suffix.
                        See https://crosshair.readthedocs.io/en/latest/contracts.html#targeting

options:
  -h, --help            show this help message and exit
  --verbose, -v         Output additional debugging information on stderr
  --extra_plugin EXTRA_PLUGIN [EXTRA_PLUGIN ...]
                        Plugin file(s) you wish to use during the current execution
  --example_output_format FORMAT
                        Determines how to output examples.
                            eval_expression     : [default] Output examples as expressions,
                                                  suitable for eval()
                            arg_dictionary      : Output arguments as repr'd, ordered
                                                  dictionaries
                            pytest              : Output examples as stub pytest tests
                            argument_dictionary : Deprecated
  --coverage_type TYPE  Determines what kind of coverage to achieve.
                            opcode : [default] Cover as many opcodes of the function as
                                     possible. This is similar to "branch" coverage.
                            path   : Cover any possible execution path.
                                     There will usually be an infinite number of paths (e.g.
                                     loops are effectively unrolled). Use
                                     max_uninteresting_iterations and/or per_condition_timeout
                                     to bound results.
                                     Many path decisions are internal to CrossHair, so you may
                                     see more duplicative-ness in the output than you'd expect.
  --max_uninteresting_iterations MAX_UNINTERESTING_ITERATIONS
                        Maximum number of consecutive iterations to run without making
                        significant progress in exploring the codebase.
                        (by default, 5 iterations, unless --per_condition_timeout is set)

                        This option can be more useful than --per_condition_timeout
                        because the amount of time invested will scale with the complexity
                        of the code under analysis.

                        Use a small integer (3-5) for fast but weak analysis.
                        Values in the hundreds or thousands may be appropriate if you
                        intend to run CrossHair for hours.
  --per_path_timeout FLOAT
                        Maximum seconds to spend checking one execution path.
                        If unspecified:
                        1. CrossHair will timeout each path at the square root of
                           `--per_condition_timeout`, if specified.
                        3. Otherwise, it will timeout each path at a number of seconds
                           equal to `--max_uninteresting_iterations`, unless it is
                           explicitly set to zero.
                           (NOTE: `--max_uninteresting_iterations` is 5 by default)
                        2. Otherwise, it will not use any per-path timeout.
  --per_condition_timeout FLOAT
                        Maximum seconds to spend checking execution paths for one condition

How does this work?

CrossHair uses an SMT solver (a kind of theorem prover) to explore execution paths and look for arguments. It uses the same engine as the crosshair check and crosshair watch commands which check code contracts.

Caveats

• CrossHair likely won’t be able to fully explore complex code.

• Your arguments must have proper type annotations.

• Your arguments have to be deep-copyable and equality-comparable.

• Arguments that are (or contain) instances of user-defined classes must meet certain expectations; see Hints for Your Classes.

• Tests are generated in the form assert <invocation> == <repr of return>. Consequently, all return values will need to be equality comparable and have reprs that faithfully reconstruct any object state.

• CrossHair is supported only on Python 3.8+ and only on CPython (the most common Python implementation).

• Only deterministic behavior can be analyzed. (your code always does the same thing when starting with the same values)

• Be careful: CrossHair will actually run your code and may apply any arguments to it.