I am working on a python Command-Line-Interface program, and I find it boring when doing testings, for example, here is the help information of the program:
usage: pyconv [-h] [-f ENCODING] [-t ENCODING] [-o file_path] file_path
Convert text file from one encoding to another.
positional arguments:
file_path
optional arguments:
-h, --help show this help message and exit
-f ENCODING, --from ENCODING
Encoding of source file
-t ENCODING, --to ENCODING
Encoding you want
-o file_path, --output file_path
Output file path
When I made changes on the program and want to test something, I must open a terminal,
type the command(with options and arguments), type enter, and see if any error occurs
while running. If error really occurs, I must go back to the editor and check the code
from top to end, guessing where the bug positions, make small changes, write print lines,
return to the terminal, run command again...
Recursively.
So my question is, what is the best way to do testing with CLI program, can it be as easy
as unit testing with normal python scripts?
I think it's perfectly fine to test functionally on a whole-program level. It's still possible to test one aspect/option per test. This way you can be sure that the program really works as a whole. Writing unit-tests usually means that you get to execute your tests quicker and that failures are usually easier to interpret/understand. But unit-tests are typically more tied to the program structure, requiring more refactoring effort when you internally change things.
Anyway, using py.test, here is a little example for testing a latin1 to utf8 conversion for pyconv::
# content of test_pyconv.py
import pytest
# we reuse a bit of pytest's own testing machinery, this should eventually come
# from a separatedly installable pytest-cli plugin.
pytest_plugins = ["pytester"]
#pytest.fixture
def run(testdir):
def do_run(*args):
args = ["pyconv"] + list(args)
return testdir._run(*args)
return do_run
def test_pyconv_latin1_to_utf8(tmpdir, run):
input = tmpdir.join("example.txt")
content = unicode("\xc3\xa4\xc3\xb6", "latin1")
with input.open("wb") as f:
f.write(content.encode("latin1"))
output = tmpdir.join("example.txt.utf8")
result = run("-flatin1", "-tutf8", input, "-o", output)
assert result.ret == 0
with output.open("rb") as f:
newcontent = f.read()
assert content.encode("utf8") == newcontent
After installing pytest ("pip install pytest") you can run it like this::
$ py.test test_pyconv.py
=========================== test session starts ============================
platform linux2 -- Python 2.7.3 -- pytest-2.4.5dev1
collected 1 items
test_pyconv.py .
========================= 1 passed in 0.40 seconds =========================
The example reuses some internal machinery of pytest's own testing by leveraging pytest's fixture mechanism, see http://pytest.org/latest/fixture.html. If you forget about the details for a moment, you can just work from the fact that "run" and "tmpdir" are provided for helping you to prepare and run tests. If you want to play, you can try to insert a failing assert-statement or simply "assert 0" and then look at the traceback or issue "py.test --pdb" to enter a python prompt.
Start from the user interface with functional tests and work down towards unit tests. It can feel difficult, especially when you use the argparse module or the click package, which take control of the application entry point.
The cli-test-helpers Python package has examples and helper functions (context managers) for a holistic approach on writing tests for your CLI. It's a simple idea, and one that works perfectly with TDD:
Start with functional tests (to ensure your user interface definition) and
Work towards unit tests (to ensure your implementation contracts)
Functional tests
NOTE: I assume you develop code that is deployed with a setup.py file or is run as a module (-m).
Is the entrypoint script installed? (tests the configuration in your setup.py)
Can this package be run as a Python module? (i.e. without having to be installed)
Is command XYZ available? etc. Cover your entire CLI usage here!
Those tests are simplistic: They run the shell command you would enter in the terminal, e.g.
def test_entrypoint():
exit_status = os.system('foobar --help')
assert exit_status == 0
Note the trick to use a non-destructive operation (e.g. --help or --version) as we can't mock anything with this approach.
Towards unit tests
To test single aspects inside the application you will need to mimic things like command line arguments and maybe environment variables. You will also need to catch the exiting of your script to avoid the tests to fail for SystemExit exceptions.
Example with ArgvContext to mimic command line arguments:
#patch('foobar.command.baz')
def test_cli_command(mock_command):
"""Is the correct code called when invoked via the CLI?"""
with ArgvContext('foobar', 'baz'), pytest.raises(SystemExit):
foobar.cli.main()
assert mock_command.called
Note that we mock the function that we want our CLI framework (click in this example) to call, and that we catch SystemExit that the framework naturally raises. The context managers are provided by cli-test-helpers and pytest.
Unit tests
The rest is business as usual. With the above two strategies we've overcome the control a CLI framework may have taken away from us. The rest is usual unit testing. TDD-style hopefully.
Disclosure: I am the author of the cli-test-helpers Python package.
So my question is, what is the best way to do testing with CLI program, can it be as easy as unit testing with normal python scripts?
The only difference is that when you run Python module as a script, its __name__ attribute is set to '__main__'. So generally, if you intend to run your script from command line it should have following form:
import sys
# function and class definitions, etc.
# ...
def foo(arg):
pass
def main():
"""Entry point to the script"""
# Do parsing of command line arguments and other stuff here. And then
# make calls to whatever functions and classes that are defined in your
# module. For example:
foo(sys.argv[1])
if __name__ == '__main__':
main()
Now there is no difference, how you would use it: as a script or as a module. So inside your unit-testing code you can just import foo function, call it and make any assertions you want.
Maybe too little too late,
but you can always use
import os.system
result = os.system(<'Insert your command with options here'>
assert(0 == result)
In that way, you can run your program as if it was from command line, and evaluate the exit code.
(Update after I studied pytest)
You can also use capsys.
(from running pytest --fixtures)
capsys
Enable text capturing of writes to sys.stdout and sys.stderr.
The captured output is made available via ``capsys.readouterr()`` method
calls, which return a ``(out, err)`` namedtuple.
``out`` and ``err`` will be ``text`` objects.
This isn't for Python specifically, but what I do to test command-line scripts is to run them with various predetermined inputs and options and store the correct output in a file. Then, to test them when I make changes, I simply run the new script and pipe the output into diff correct_output -. If the files are the same, it outputs nothing. If they're different, it shows you where. This will only work if you are on Linux or OS X; on Windows, you will have to get MSYS.
Example:
python mycliprogram --someoption "some input" | diff correct_output -
To make it even easier, you can add all these test runs to your 'make test' Makefile target, which I assume you already have. ;)
If you are running many of these at once, you could make it a little more obvious where each one ends by adding a fail tag:
python mycliprogram --someoption "some input" | diff correct_output - || tput setaf 1 && echo "FAILED"
The short answer is yes, you can use unit tests, and should. If your code is well structured, it should be quite easy to test each component separately, and if you need to to can always mock sys.argv to simulate running it with different arguments.
pytest-console-scripts is a Pytest plugin for testing python scripts installed via console_scripts entry point of setup.py.
For Python 3.5+, you can use the simpler subprocess.run to call your CLI command from your test.
Using pytest:
import subprocess
def test_command__works_properly():
try:
result = subprocess.run(['command', '--argument', 'value'], check=True, capture_output=True, text=True)
except subprocess.CalledProcessError as error:
print(error.stdout)
print(error.stderr)
raise error
The output can be accessed via result.stdout, result.stderr, and result.returncode if needed.
The check parameter causes an exception to be raised if an error occurs. Note Python 3.7+ is required for the capture_output and text parameters, which simplify capturing and reading stdout/stderr.
Given that you are explicitly asking about testing for a command line application, I believe that you are aware of unit-testing tools in python and that you are actually looking for a tool to automate end-to-end tests of a command line tool. There are a couple of tools out there that are specifically designed for that. If you are looking for something that's pip-installable, I would recommend cram. It integrates well with the rest of the python environment (e.g. through a pytest extension) and it's quite easy to use:
Simply write the commands you want to run prepended with $ and the expected output prepended with . For example, the following would be a valid cram test:
$ echo Hello
Hello
By having four spaces in front of expected output and two in front of the test, you can actually use these tests to also write documentation. More on that on the website.
You can use standard unittest module:
# python -m unittest <test module>
or use nose as a testing framework. Just write classic unittest files in separate directory and run:
# nosetests <test modules directory>
Writing unittests is easy. Just follow online manual for unittesting
I would not test the program as a whole this is not a good test strategy and may not actually catch the actual spot of the error. The CLI interface is just front end to an API. You test the API via your unit tests and then when you make a change to a specific part you have a test case to exercise that change.
So, restructure your application so that you test the API and not the application it self. But, you can have a functional test that actually does run the full application and checks that the output is correct.
In short, yes testing the code is the same as testing any other code, but you must test the individual parts rather than their combination as a whole to ensure that your changes do not break everything.
Related
I'm looking for a way to test, in my python script, if said script is running from Ansible so I can also run it through shell (for running unit tests etc). Calling AnsibleModule without calling from an ansible playbook will just endlessly wait for a response that will never come.
I'm expecting that there isn't a simple test and that I have to restructure in some way, but I'm open to any options.
def main():
# must test if running via ansible before next line
module = AnsibleModule(
argument_spec=dict(
server=dict(required=True, type='str'),
[...]
)
[... do things ...]
)
if __name__ == "__main__":
if running_via_ansible:
main()
else:
run_tests()
I believe there are a couple of answers, with various levels of trickery involved
since your module is written in python, ansible will use the AnsiballZ framework to run it, which means its sys.argv[0] will start with AnsiballZ_; it will also likely be written to $HOME/.ansible/tmp on the target machine, so one could sniff for .ansible/tmp showing up in argv[0] also
if the file contains the string WANT_JSON in it, then ansible will invoke it with the module's JSON payload as the first argument instead of feeding it JSON on sys.stdin (thus far the filename has been colocated with the AnsiballZ_ script, but I don't know that such a thing is guaranteed)
Similar, although apparently far more python specific: if it contains a triple-quoted sentinel """<<INCLUDE_ANSIBLE_MODULE_JSON_ARGS>>""" (or the ''' flavor works, too) then that magic string is replaced by the serialized JSON that, again, would have been otherwise provided via stdin
While this may not apply, or be helpful, I actually would expect that any local testing environment would have more "fingerprints" than trying to detect the opposite, and has the pleasing side-effect of "failing open" in that the module will assume it is running in production mode unless it can prove testing mode, which should make for less weird false positives. Then again, I guess the reasonable default depends on how problematic it would be for the module to attempt to carry out its payload when not really in use
I'm currently writing a module which uses console_script in setup.py to create scripts at installation time. For performing the tests I use the plugin pytest-console-scripts to execute those scripts. One of the functions I want to test involves a input() call to get an anwer from the user ('y'es or 'n'o). But I do not have any idea on how to mock this input.
A sample test using pytest-console-scripts looks like:
import pytest
def test_my_function(script_runner):
# first option is the console script to be run followed by arguments
ret = script_runner.run('myscript', '--version')
assert ret.success
This can be used when the console script does not involve user action. How can this be solved?
Many thanks in advance, regards, Thomas
EDIT: the provided solutions in How to test a function with input call may solve my question only partially. My intention is to test the functionality through the console script, but not importing the module containing the function called through that script - if this is possible.
After investigating a lot more through Google I came across a solution, which worked perfectly for me:
# pip install pytest-mock pytest-console-scripts
...
def test_user_input(script_runner, mocker):
# optional use side_effect with any kind of value you try to give to
# your tested function
mocker.patch('builtins.input', return_value='<your_expected_input>')
# use side_effect=<values> instead if you want to insert more than one value
# Options have to be seperated
# Example: ('my_prog', '-a', 'val_a', '-b', 'val_b')
# or: ('my_prog', '-a val_a -b val_b'.split(' '))
ret = script_runner.run('my_prog')
assert ret.success
assert ret.stdout == <whatever>
# or assert 'string' in ret.stdout
See https://docs.python.org/3/library/unittest.mock.html#unittest.mock.Mock.side_effect for further possibilities of how to use side_effect.
I have a Python package (Python 3.6, if it makes a difference) that I've designed to run as 'python -m package arguments' and I'd like to write unit tests for the __main__.py module. I specifically want to verify that it sets the exit code correctly. Is it possible to use runpy.run_module to execute my __main__.py and test the exit code? If so, how do I retrieve the exit code?
To be more clear, my __main__.py module is very simple. It just calls a function that has been extensively unit tested. But when I originally wrote __main__.py, I forgot to pass the result of that function to exit(), so I would like unit tests where the main function is mocked to make sure the exit code is set correctly. My unit test would look something like:
#patch('my_module.__main__.my_main', return_value=2)
def test_rc2(self, _):
"""Test that rc 2 is the exit code."""
sys.argv = ['arg0', 'arg1', 'arg2', …]
runpy.run_module('my_module')
self.assertEqual(mod_rc, 2)
My question is, how would I get what I’ve written here as ‘mod_rc’?
Thanks.
Misko Hevery has said before (I believe it was in Clean Code Talks: Don't Look for Things but I may be wrong) that he doesn't know how to effectively unit test main methods, so his solution is to make them so simple that you can prove logically that they work if you assume the correctness of the (unit-tested) code that they call.
For example, if you have a discrete, tested unit for parsing command line arguments; a library that does the actual work; and a discrete, tested unit for rendering the completed work into output, then a main method that calls all three of those in sequence is assuredly going to work.
With that architecture, you can basically get by with just one big system test that is expected to produce something other than the "default" output and it'll either crash (because you wired it up improperly) or work (because it's wired up properly and all of the individual parts work).
At this point, I'm dropping all pretense of knowing what I'm talking about. There is almost assuredly a better way to do this, but frankly you could just write a shell script:
python -m package args
test $? -eq [expected exit code]
That will exit with error iff your program outputs incorrectly, which TravisCI or similar will regard as build failing.
__main__.py is still subject to normal __main__ global behavior — which is to say, you can implement your __main__.py like so
def main():
# Your stuff
if __name__ == "__main__":
main()
and then you can test your __main__ in whatever testing framework you like by using
from your_package.__main__ import main
As an aside, if you are using argparse, you will probably want:
def main(arg_strings=None):
# …
args = parser.parse_args(arg_strings)
# …
if __name__ == "__main__":
main()
and then you can override arg strings from a unit test simply with
from your_package.__main__ import main
def test_main():
assert main(["x", "y", "z"]) == …
or similar idiom in you testing framework.
With pytest, I was able to do:
import mypkgname.__main__ as rtmain
where mypkgname is what you've named your app as a package/module. Then just running pytest as normal worked. I hope this helps some other poor soul.
I'm running a python script from inside a different software (it provides a python interface to manipulate its data structures).
I'm optimizing my code for speed and would like to see what impact on performance my asserts have.
I'm unable to use python -O. What other options do I have, to programatically disable all asserts in python code? The variable __debug__ (which is cleared by -O flag) cannot be assigned to :(
The docs say,
The value for the built-in variable [__debug__] is determined when the
interpreter starts.
So, if you can not control how the python interpreter is started, then it looks like you can not disable assert.
Here then are some other options:
The safest way is to manually remove all the assert statements.
If all your assert statements occur on lines by themselves, then
perhaps you could remove them with
sed -i 's/assert /pass #assert /g' script.py
Note that this will mangle your code if other code comes after the assert. For example, the sed command above would comment-out the return in a line like this:
assert x; return True
which would change the logic of your program.
If you have code like this, it would probably be best to manually remove the asserts.
There might be a way to remove them programmatically by parsing your
script with the tokenize module, but writing such a program to
remove asserts may take more time than it would take to manually
remove the asserts, especially if this is a one-time job.
If the other piece of software accepts .pyc files, then there is a
dirty trick which seems to work on my machine, though note a Python
core developer warns against this (See Éric Araujo's comment on 2011-09-17). Suppose your script is called script.py.
Make a temporary script called, say, temp.py:
import script
Run python -O temp.py. This creates script.pyo.
Move script.py and script.pyc (if it exists) out of your PYTHONPATH
or whatever directory the other software is reading to find your
script.
Rename script.pyo --> script.pyc.
Now when the other software tries to import your script, it will
only find the pyc file, which has the asserts removed.
For example, if script.py looks like this:
assert False
print('Got here')
then running python temp.py will now print Got here instead of raising an AssertionError.
You may be able to do this with an environment variable, as described in this other answer. Setting PYTHONOPTIMIZE=1 is equivalent to starting Python with the -O option. As an example, this works in Blender 2.78, which embeds Python 3.5:
blender --python-expr 'assert False; print("foo")'
PYTHONOPTIMIZE=1 blender --python-expr 'assert False; print("foo")'
The first command prints a traceback, while the second just prints "foo".
As #unutbu describes, there is no official way of doing this. However, a simple strategy is to define a flag like _test somewhere (for example, as keyword argument to a function, or as a global variable in a module), then include this in your assert statements as follows:
def f(x, _test=True):
assert not _test or x > 0
...
Then you can disable asserts in that function if needed.
f(x, _test=False)
I'm running a python script from inside a different software (it provides a python interface to manipulate its data structures).
I'm optimizing my code for speed and would like to see what impact on performance my asserts have.
I'm unable to use python -O. What other options do I have, to programatically disable all asserts in python code? The variable __debug__ (which is cleared by -O flag) cannot be assigned to :(
The docs say,
The value for the built-in variable [__debug__] is determined when the
interpreter starts.
So, if you can not control how the python interpreter is started, then it looks like you can not disable assert.
Here then are some other options:
The safest way is to manually remove all the assert statements.
If all your assert statements occur on lines by themselves, then
perhaps you could remove them with
sed -i 's/assert /pass #assert /g' script.py
Note that this will mangle your code if other code comes after the assert. For example, the sed command above would comment-out the return in a line like this:
assert x; return True
which would change the logic of your program.
If you have code like this, it would probably be best to manually remove the asserts.
There might be a way to remove them programmatically by parsing your
script with the tokenize module, but writing such a program to
remove asserts may take more time than it would take to manually
remove the asserts, especially if this is a one-time job.
If the other piece of software accepts .pyc files, then there is a
dirty trick which seems to work on my machine, though note a Python
core developer warns against this (See Éric Araujo's comment on 2011-09-17). Suppose your script is called script.py.
Make a temporary script called, say, temp.py:
import script
Run python -O temp.py. This creates script.pyo.
Move script.py and script.pyc (if it exists) out of your PYTHONPATH
or whatever directory the other software is reading to find your
script.
Rename script.pyo --> script.pyc.
Now when the other software tries to import your script, it will
only find the pyc file, which has the asserts removed.
For example, if script.py looks like this:
assert False
print('Got here')
then running python temp.py will now print Got here instead of raising an AssertionError.
You may be able to do this with an environment variable, as described in this other answer. Setting PYTHONOPTIMIZE=1 is equivalent to starting Python with the -O option. As an example, this works in Blender 2.78, which embeds Python 3.5:
blender --python-expr 'assert False; print("foo")'
PYTHONOPTIMIZE=1 blender --python-expr 'assert False; print("foo")'
The first command prints a traceback, while the second just prints "foo".
As #unutbu describes, there is no official way of doing this. However, a simple strategy is to define a flag like _test somewhere (for example, as keyword argument to a function, or as a global variable in a module), then include this in your assert statements as follows:
def f(x, _test=True):
assert not _test or x > 0
...
Then you can disable asserts in that function if needed.
f(x, _test=False)