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Tests

Quick Start

To add a simple unit test for a new feature you developed, open or create a test-data/unit/check-*.test file with a name that roughly relates to the feature you added.

Add the test in this format anywhere in the file:

[case testNewSyntaxBasics]
# flags: --python-version 3.6
x: int
x = 5
y: int = 5

a: str
a = 5  # E: Incompatible types in assignment (expression has type "int", variable has type "str")
b: str = 5  # E: Incompatible types in assignment (expression has type "int", variable has type "str")

zzz: int
zzz: str  # E: Name 'zzz' already defined
  • no code here is executed, just type checked
  • optional # flags: indicates which flags to use for this unit test
  • # E: abc... indicates that this line should result in type check error with text "abc..."
  • note a space after E: and flags:
  • # E:12 adds column number to the expected error
  • use \ to escape the # character and indicate that the rest of the line is part of the error message
  • repeating # E: several times in one line indicates multiple expected errors in one line
  • W: ... and N: ... works exactly like E:, but report a warning and a note respectively
  • lines that don't contain the above should cause no type check errors
  • optional [builtins fixtures/...] tells the type checker to use stubs from the indicated file (see Fixtures section below)
  • optional [out] is an alternative to the "# E:" notation: it indicates that any text after it contains the expected type checking error messages. Usually, "E: " is preferred because it makes it easier to associate the errors with the code generating them at a glance, and to change the code of the test without having to change line numbers in [out]
  • an empty [out] section has no effect
  • to run just this test, use pytest -n0 -k testNewSyntaxBasics

Fixtures

The unit tests use minimal stubs for builtins, so a lot of operations are not possible. You should generally define any needed classes within the test case instead of relying on builtins, though clearly this is not always an option (see below for more about stubs in test cases). This way tests run much faster and don't break if the stubs change. If your test crashes mysteriously even though the code works when run manually, you should make sure you have all the stubs you need for your test case, including built-in classes such as list or dict, as these are not included by default.

Where the stubs for builtins come from for a given test:

  • The builtins used by default in unit tests live in test-data/unit/lib-stub.

  • Individual test cases can override the builtins stubs by using [builtins fixtures/foo.pyi]; this targets files in test-data/unit/fixtures. Feel free to modify existing files there or create new ones as you deem fit.

  • Test cases can also use [typing fixtures/typing-full.pyi] to use a more complete stub for typing that contains the async types, among other things.

  • Feel free to add additional stubs to that fixtures directory, but generally don't expand files in lib-stub without first discussing the addition with other mypy developers, as additions could slow down the test suite.

Running tests and linting

First install any additional dependencies needed for testing:

$ python3 -m pip install -U -r test-requirements.txt

You must also have a Python 2.7 binary installed that can import the typing module:

$ python2 -m pip install -U typing

The unit test suites are driven by the pytest framework. To run all mypy tests, run pytest in the mypy repository:

$ pytest mypy

This will run all tests, including integration and regression tests, and will verify that all stubs are valid. This may take several minutes to run, so you don't want to use this all the time while doing development.

Test suites for individual components are in the files mypy/test/test*.py.

Note that some tests will be disabled for older python versions.

If you work on mypyc, you will want to also run mypyc tests:

$ pytest mypyc

You can run tests from a specific module directly, a specific suite within a module, or a test in a suite (even if it's data-driven):

$ pytest mypy/test/testdiff.py

$ pytest mypy/test/testsemanal.py::SemAnalTypeInfoSuite

$ pytest -n0 mypy/test/testargs.py::ArgSuite::test_coherence

$ pytest -n0 mypy/test/testcheck.py::TypeCheckSuite::testCallingVariableWithFunctionType

To control which tests are run and how, you can use the -k switch:

$ pytest -k "MethodCall"

You can also run the type checker for manual testing without installing it by setting up the Python module search path suitably:

$ export PYTHONPATH=$PWD
$ python3 -m mypy PROGRAM.py

You will have to manually install the typing module if you're running Python 3.4 or earlier.

You can also execute mypy as a module

$ python3 -m mypy PROGRAM.py

You can check a module or string instead of a file:

$ python3 -m mypy PROGRAM.py
$ python3 -m mypy -m MODULE
$ python3 -m mypy -c 'import MODULE'

To run mypy on itself:

$ python3 -m mypy --config-file mypy_self_check.ini -p mypy

To run the linter:

$ flake8

You can also run all of the above tests using runtests.py (this includes type checking mypy and linting):

$ python3 runtests.py

By default, this runs everything except some mypyc tests. You can give it arguments to control what gets run, such as self to run mypy on itself:

$ python3 runtests.py self

Run python3 runtests.py mypyc-extra to run mypyc tests that are not enabled by default. This is typically only needed if you work on mypyc.

Many test suites store test case descriptions in text files (test-data/unit/*.test). The module mypy.test.data parses these descriptions.

Python evaluation test cases are a little different from unit tests (mypy/test/testpythoneval.py, test-data/unit/pythoneval.test). These type check programs and run them. Unlike the unit tests, these use the full builtins and library stubs instead of minimal ones. Run them using pytest -k testpythoneval.

pytest determines the number of processes to use. The default (set in ./pytest.ini) is the number of logical cores; this can be overridden using -n option. To run a single process, use pytest -n0.

Note that running more processes than logical cores is likely to significantly decrease performance.

Debugging

You can use interactive debuggers like pdb to debug failing tests. You need to pass the -n0 option to disable parallelization:

$ pytest -n0 --pdb -k MethodCall

You can also write import pdb; pdb.set_trace() in code to enter the debugger.

The --mypy-verbose flag can be used to enable additional debug output from most tests (as if --verbose had been passed to mypy):

$ pytest -n0 --mypy-verbose -k MethodCall

Coverage reports

There is an experimental feature to generate coverage reports. To use this feature, you need to pip install -U lxml. This is an extension module and requires various library headers to install; on a Debian-derived system the command apt-get install python3-dev libxml2-dev libxslt1-dev may provide the necessary dependencies.

To use the feature, pass e.g. --txt-report "$(mktemp -d)".