Back in 2012 I gave a talk at Derbycon 2.0. This was my first infosec talk and I was a little nervous, to say the least. Anyway, I described a system I wanted to write that handled distributed baseline scanning.
After a lot of starts and stops, I finished a basic 1.0 version in 2014. It’s still quite rough and I’ve since been working, intermittently, on making the system more robust and solid. I’ve been working on a python replacement for the GUI as well, instead of the current PHP one. The repository is located here, if you’re interested in taking a look.
Why am I telling you all of this? Well, as part of the updates I’m making, I wanted to do things the “right way” and make sure I have unit testing in place before I start making additional changes to the code. Problem is, while I learned about unit testing, I’ve never really implemented it in any meaningful way, so this is a bit new to me.
So why unit testing? Well, the hypothesis is that by creating tests that check every line of code, you ensure that the code is working as expected. Thus, if the tests pass, then the code should be solid and bug free. In reality, this is rarely the case. Tests can be just as flawed as any other code. Additionally, you may miss testing certain corner cases and miss potential bugs. In the end, the general consensus is that unit testing is a complicated religious argument.
Let’s assume that we want to unit test anyway and move on to the actual testing bits, shall we? We’ll start with a contrived example to make things easier. Assume we have the following code in a file called mytestcode.py:
def add(value1, value2):
return value1 + value2
Simple enough, just a simple function to return the value of two numbers added together. Let’s create some test cases, shall we?
from mytestcode import add
assert add(1,1) == 2
assert add(1,1) != 3
What we have here are two simple test cases. First, we test to make sure that if we call the add function with two values, 1 and 1, we get a 2 as a return value. Second, we test that providing the same values as input does not return a 3. Simple, right? But have we really tested all of the corner cases? What happens if we feed the function a negative? How about a non-numeric value? Are there cases where we can cause an exception?
To be fair, the original function is poorly written and is merely being used as a simple example. This is the problem with contrived examples, of course. They miss important details, often simply things too much, and can lead to beginners making big mistakes when using them as teaching tools. So please, be aware, the above code really isn’t very good code. It’s intended to be simple to understand.
Let’s take a look at some “real” code directly from my distributed scanner project. This particular code is something I found on Stack Overflow when I was looking for a way to identify whether a process was still running or not.
"""Check whether pid exists in the current process table.
if pid < 0:
if pid == 0:
# According to "man 2 kill" PID 0 refers to every process
# in the process group of the calling process.
# On certain systems 0 is a valid PID but we have no way
# to know that in a portable fashion.
raise ValueError('invalid PID 0')
except OSError as err:
if err.errno == errno.ESRCH:
# ESRCH == No such process
elif err.errno == errno.EPERM:
# EPERM clearly means there's a process to deny access to
# According to "man 2 kill" possible error values are
# (EINVAL, EPERM, ESRCH)
Testing this code should be relatively straightforward, with the exception of the os.kill call. For that, we’ll need to delve into mock objects. Let’s tackle the simple cases first:
from libs.funcs import pid_exists
assert pid_exists(-1) == False
with pytest.raises(ValueError) as e_info:
That’s relatively simple. We verify that False is returned for a negative PID and a ValueError is returned for a PID of zero. We also test that a TypeError is returned if we don’t provide an integer value. What’s left is handling a valid PID and testing that it returns True for a running process and False otherwise. In order to test the rest, we could go through a lot of elaborate setup to start a process, get the PID, and then test our code, but there’s a lot that can go wrong there. Additionally, we’re looking to test our logic and not the entirety of another module. So, what we really want is a way to provide an arbitrary return value for a given call. Enter the mock module.
The mock module is part of the unittest framework in python. Essentially, the mock module allows you to identify a call or an object that you want to create a fake version of, and then provide the behavior you’re expecting that mocked version to have. So, for instance, you can mock a function call and simply provide the return value you’re looking for instead of having to call the function directly. This functionality allows you to precisely test your logic versus doing a deeper integration test.
To finish up our testing code for the pid_exists() function, we want to mock the os.kill() function and have it return specific values so we can check the various branches of code we have.
def test_pid_exists(self, oskillobj):
oskillobj.return_value = None
assert pid_exists(100) == True
def test_pid_does_not_exist(self, oskillobj):
oskillobj.side_effect = OSError(errno.ESRCH, 'No such process')
assert pid_exists(1234) == False
def test_pid_no_permissions(self, oskillobj):
oskillobj.side_effect = OSError(errno.EPERM, 'Operation not permitted')
assert pid_exists(1234) == True
def test_pid_invalid(self, oskillobj):
oskillobj.side_effect = OSError(errno.EINVAL, 'Invalid argument')
def test_pid_os_typeerror(self, oskillobj):
oskillobj.side_effect = TypeError('an integer is required (got type str)')
The above code tests all of the branching available in the rest of the code, verifying the logic we’ve written. The code should be pretty straightforward. The return_value attribute of a mock object directly defines what we want the mocked function to recall while the side_effect attribute allows us to throw an exception in response to the function call. With those two features of a mocked object, we’re able to successfully test the rest of the cases we need.
This little journey to learn how to write unit tests has been fun and informative. I just need to finish up the rest of the code, striving to hit as close to 100% coverage as I can while keeping the test cases reasonable. It’s taken a while to get going, but the more code I’ve been writing, the faster and more accurate I’m getting. As they say, “practice makes perfect,” though I’d settle with functionally complete and relatively bug-free.
One final word of caution. I’m a sole developer working on this code, so I’m the only one around to write test cases. In a larger shop, the originator of the logic should not be the one writing the test cases. The reason for this is that the original coder typically knows their code quite well and has expectations regarding how the code will be used. For instance, I’m expecting that anyone calling the add() function I wrote above to only supply numbers and I haven’t added any sort of type checking or input validation. As a result, I avoided adding test cases that supply invalid inputs, knowing that would fail. Someone else writing the test cases would likely have provided a number of different inputs and found that input validation was missing. So if you’re in a larger shop, do yourself a favor and have someone else write your test cases. And to ensure they provide robust test cases, only provide the function prototypes and not the full function definitions.