I want to understand what needs to be mocked and what not when writing test cases in general.
For example, we will mock I/O operations, but what about functions imported from another module. Are we supposed to mock them as well?
Mocking should be done for a reason. Good reasons are:
You can not easily make the depended-on-component (DOC) behave as intended for your tests.
Does calling the DOC cause any non-derministic behaviour (date/time, randomness, network connections)?
The test setup is overly complex and/or maintenance intensive (like, need for external files)
The original DOC brings portability problems for your test code.
Does using the original DOC cause unnacceptably long build / execution times?
Has the DOC stability (maturity) issues that make the tests unreliable, or, worse, is the DOC not even available yet?
For example, you (typically) don't mock standard library math functions like sin or cos, because they don't have any of the abovementioned problems.
You really have to know what you are unit testing. From there it will be clear what to mock...
I have a custom framework which runs different code for different clients. I have monkeypatched certain methods in order to customize functionality for a client.
Here is the pattern simplified:
#import monkeypatches here
if self.config['client'] == 'cool_dudes':
from app.monkeypatches import Stuff
if self.config['client'] == 'cool_dudettes':
from app.monkeypatches import OtherStuff
Here is an example patch:
from app.framework.stuff import Stuff
def function_override(self):
return pass
Stuff.function = function_override
This works fine when the program executes as it is executed in a batch manner, spinning up from scratch every time. However, when running across unit tests, I find that the monkey patches persist across tests, causing unexpected behavior.
I realize that it would be far better to use an object oriented inheritance approach to these overrides, but I inherited this codebase and am not currently empowered to rearchitect it to that degree.
Barring properly re-architecting the program, how can I prevent these monkey patches from persisting across unit tests?
The modules, including app.framework.<whatever>, are not reloaded for every test. So, any changes in them you make persist. The same happens if your module is stateful (that's one of the reasons why global state is not such a good idea, you should rather keep state in objects).
Your options are to:
undo the monkey-patches when needed, or
change them into something more generic that would change (semi-)automatically depending on the test running, or
(preferred) Do not reinvent the wheel and use an existing, manageable, time-proven solution for your task (or at least, base your work on one if it doesn't meet your requirements completely). E.g. if you use them for mocking, see How can one mock/stub python module like urllib . Among the suggestions there is #mock.patch that does the patching for a specific test and undoes it upon its completion.
Anyone coming here looking for information about monkeypatching, might want to have a look at pytest's monkeypatch fixture. It avoids the problem of the OP by automatically undoing all modifications after the test function has finished.
I'd like to test some python scripts.
Are there any python libraries to help test external system behaviors(running scripts, testing the contents of external files, managing input/output files, and similar actions).
Also I tried making the scripts more api like to allow imports rather then calling it directly for more unit test like tests. Changes include making scripts easier to run interactively(factor lots of stuff into functions/module values and make it less procedural, add parameter to silence stdout, passing optional args to main) also serializing results in addition to the usual output formats(even though the functions to generate the output files have a medium amount of logic in them)).
Is this a good strategy or is it better to attempt to test scripts by running them blackbox style and examining output.
Test library
I'll go ahead and suggest unittest (even though it's the top Google hit for "python unit testing" and you probably already know of it). It's a very nice, easy to use, feature-ful library for unit testing.
Test strategy
Writing testable code is hard. Testing things like side-effects, environments, and file output can take the unit right out of unit test.
What I typically try to do is structure the code so that as little of it as possible does I/O or other nasty things. Then all of that code can usually be straightforwardly unit-tested.
For the parts that are hard to break into units, such as the command-line interface, I test for file output etc.
Conclusion
use unit tests as much as possible
otherwise, use black-box tests
constantly refactor code to make writing unit tests easier & more effective
How do you do unit testing when you have
some general unit tests
more sophisticated tests checking edge cases, depending on the general ones
To give an example, imagine testing a CSV-reader (I just made up a notation for demonstration),
def test_readCsv(): ...
#dependsOn(test_readCsv)
def test_readCsv_duplicateColumnName(): ...
#dependsOn(test_readCsv)
def test_readCsv_unicodeColumnName(): ...
I expect sub-tests to be run only if their parent test succeeds. The reason behind this is that running these tests takes time. Many failure reports that go back to a single reason wouldn't be informative, either. Of course, I could shoehorn all edge-cases into the main test, but I wonder if there is a more structured way to do this.
I've found these related but different questions,
How to structure unit tests that have dependencies?
Unit Testing - Is it bad form to have unit test calling other unit tests
UPDATE:
I've found TestNG which has great built-in support for test dependencies. You can write tests like this,
#Test{dependsOnMethods = ("test_readCsv"))
public void test_readCsv_duplicateColumnName() {
...
}
Personally, I wouldn't worry about creating dependencies between unit tests. This sounds like a bit of a code smell to me. A few points:
If a test fails, let the others fail to and get a good idea of the scale of the problem that the adverse code change made.
Test failures should be the exception rather than the norm, so why waste effort and create dependencies when the vast majority of the time (hopefully!) no benefit is derived? If failures happen often, your problem is not with unit test dependencies but with frequent test failures.
Unit tests should run really fast. If they are running slow, then focus your efforts on increasing the speed of these tests rather than preventing subsequent failures. Do this by decoupling your code more and using dependency injection or mocking.
Proboscis is a python version of TestNG (which is a Java library).
See packages.python.org/proboscis/
It supports dependencies, e.g.
#test(depends_on=[test_readCsv])
public void test_readCsv_duplicateColumnName() {
...
}
I'm not sure what language you're referring to (as you don't specifically mention it in your question) but for something like PHPUnit there is an #depends tag that will only run a test if the depended upon test has already passed.
Depending on what language or unit testing you use there may also be something similar available
I have implemented a plugin for Nose (Python) which adds support for test dependencies and test prioritization.
As mentioned in the other answers/comments this is often a bad idea, however there can be exceptions where you would want to do this (in my case it was performance for integration tests - with a huge overhead for getting into a testable state, minutes vs hours).
You can find it here: nosedep.
A minimal example is:
def test_a:
pass
#depends(before=test_a)
def test_b:
pass
To ensure that test_b is always run before test_a.
You may want use pytest-dependency. According to theirs documentation code looks elegant:
import pytest
#pytest.mark.dependency()
#pytest.mark.xfail(reason="deliberate fail")
def test_a():
assert False
#pytest.mark.dependency()
def test_b():
pass
#pytest.mark.dependency(depends=["test_a"])
def test_c():
pass
#pytest.mark.dependency(depends=["test_b"])
def test_d():
pass
#pytest.mark.dependency(depends=["test_b", "test_c"])
def test_e():
pass
Please note, it is plugin for pytest, not unittest which is part of python itself. So, you need 2 more dependencies (f.e. add into requirements.txt):
pytest==5.1.1
pytest-dependency==0.4.0
According to best practices and unit testing principles unit test should not depend on other ones.
Each test case should check concrete isolated behavior.
Then if some test case fail you will know exactly what became wrong with our code.
I'm using Python's built-in unittest module and I want to write a few tests that are not critical.
I mean, if my program passes such tests, that's great! However, if it doesn't pass, it's not really a problem, the program will still work.
For example, my program is designed to work with a custom type "A". If it fails to work with "A", then it's broken. However, for convenience, most of it should also work with another type "B", but that's not mandatory. If it fails to work with "B", then it's not broken (because it still works with "A", which is its main purpose). Failing to work with "B" is not critical, I will just miss a "bonus feature" I could have.
Another (hypothetical) example is when writing an OCR. The algorithm should recognize most images from the tests, but it's okay if some of them fails. (and no, I'm not writing an OCR)
Is there any way to write non-critical tests in unittest (or other testing framework)?
As a practical matter, I'd probably use print statements to indicate failure in that case. A more correct solution is to use warnings:
http://docs.python.org/library/warnings.html
You could, however, use the logging facility to generate a more detailed record of your test results (i.e. set your "B" class failures to write warnings to the logs).
http://docs.python.org/library/logging.html
Edit:
The way we handle this in Django is that we have some tests we expect to fail, and we have others that we skip based on the environment. Since we can generally predict whether a test SHOULD fail or pass (i.e. if we can't import a certain module, the system doesn't have it, and so the test won't work), we can skip failing tests intelligently. This means that we still run every test that will pass, and have no tests that "might" pass. Unit tests are most useful when they do things predictably, and being able to detect whether or not a test SHOULD pass before we run it makes this possible.
Asserts in unit tests are binary: they will work or they will fail, there's no mid-term.
Given that, to create those "non-critical" tests you should not use assertions when you don't want the tests to fail. You should do this carefully so you don't compromise the "usefulness" of the test.
My advice to your OCR example is that you use something to record the success rate in your tests code and then create one assertion like: "assert success_rate > 8.5", and that should give the effect you desire.
Thank you for the great answers. No only one answer was really complete, so I'm writing here a combination of all answers that helped me. If you like this answer, please vote up the people who were responsible for this.
Conclusions
Unit tests (or at least unit tests in unittest module) are binary. As Guilherme Chapiewski says: they will work or they will fail, there's no mid-term.
Thus, my conclusion is that unit tests are not exactly the right tool for this job. It seems that unit tests are more concerned about "keep everything working, no failure is expected", and thus I can't (or it's not easy) to have non-binary tests.
So, unit tests don't seem the right tool if I'm trying to improve an algorithm or an implementation, because unit tests can't tell me how better is one version when compared to the other (supposing both of them are correctly implemented, then both will pass all unit tests).
My final solution
My final solution is based on ryber's idea and code shown in wcoenen answer. I'm basically extending the default TextTestRunner and making it less verbose. Then, my main code call two test suits: the critical one using the standard TextTestRunner, and the non-critical one, with my own less-verbose version.
class _TerseTextTestResult(unittest._TextTestResult):
def printErrorList(self, flavour, errors):
for test, err in errors:
#self.stream.writeln(self.separator1)
self.stream.writeln("%s: %s" % (flavour,self.getDescription(test)))
#self.stream.writeln(self.separator2)
#self.stream.writeln("%s" % err)
class TerseTextTestRunner(unittest.TextTestRunner):
def _makeResult(self):
return _TerseTextTestResult(self.stream, self.descriptions, self.verbosity)
if __name__ == '__main__':
sys.stderr.write("Running non-critical tests:\n")
non_critical_suite = unittest.TestLoader().loadTestsFromTestCase(TestSomethingNonCritical)
TerseTextTestRunner(verbosity=1).run(non_critical_suite)
sys.stderr.write("\n")
sys.stderr.write("Running CRITICAL tests:\n")
suite = unittest.TestLoader().loadTestsFromTestCase(TestEverythingImportant)
unittest.TextTestRunner(verbosity=1).run(suite)
Possible improvements
It should still be useful to know if there is any testing framework with non-binary tests, like Kathy Van Stone suggested. Probably I won't use it this simple personal project, but it might be useful on future projects.
Im not totally sure how unittest works, but most unit testing frameworks have something akin to categories. I suppose you could just categorize such tests, mark them to be ignored, and then run them only when your interested in them. But I know from experience that ignored tests very quickly become...just that ignored tests that nobody ever runs and are therefore a waste of time and energy to write them.
My advice is for your app to do, or do not, there is no try.
From unittest documentation which you link:
Instead of unittest.main(), there are
other ways to run the tests with a
finer level of control, less terse
output, and no requirement to be run
from the command line. For example,
the last two lines may be replaced
with:
suite = unittest.TestLoader().loadTestsFromTestCase(TestSequenceFunctions)
unittest.TextTestRunner(verbosity=2).run(suite)
In your case, you can create separate TestSuite instances for the criticial and non-critical tests. You could control which suite is passed to the test runner with a command line argument. Test suites can also contain other test suites so you can create big hierarchies if you want.
Python 2.7 (and 3.1) added support for skipping some test methods or test cases, as well as marking some tests as expected failure.
http://docs.python.org/library/unittest.html#skipping-tests-and-expected-failures
Tests marked as expected failure won't be counted as failure on a TestResult.
There are some test systems that allow warnings rather than failures, but test_unit is not one of them (I don't know which ones do, offhand) unless you want to extend it (which is possible).
You can make the tests so that they log warnings rather than fail.
Another way to handle this is to separate out the tests and only run them to get the pass/fail reports and not have any build dependencies (this depends on your build setup).
Take a look at Nose : http://somethingaboutorange.com/mrl/projects/nose/0.11.1/
There are plenty of command line options for selecting tests to run, and you can keep your existing unittest tests.
Another possibility is to create a "B" branch (you ARE using some sort of version control, right?) and have your unit tests for "B" in there. That way, you keep your release version's unit tests clean (Look, all dots!), but still have tests for B. If you're using a modern version control system like git or mercurial (I'm partial to mercurial), branching/cloning and merging are trivial operations, so that's what I'd recommend.
However, I think you're using tests for something they're not meant to do. The real question is "How important to you is it that 'B' works?" Because your test suite should only have tests in it that you care whether they pass or fail. Tests that, if they fail, it means the code is broken. That's why I suggested only testing "B" in the "B" branch, since that would be the branch where you are developing the "B" feature.
You could test using logger or print commands, if you like. But if you don't care enough that it's broken to have it flagged in your unit tests, I'd seriously question whether you care enough to test it at all. Besides, that adds needless complexity (extra variables to set debug level, multiple testing vectors that are completely independent of each other yet operate within the same space, causing potential collisions and errors, etc, etc). Unless you're developing a "Hello, World!" app, I suspect your problem set is complicated enough without adding additional, unnecessary complications.
You could write your test so that they count success rate.
With OCR you could throw at code 1000 images and require that 95% is successful.
If your program must work with type A then if this fails the test fails. If it's not required to work with B, what is the value of doing such a test ?