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leftovers

#4: Automated testing techniques

And, no, before you ask, this has nothing to do with program-testing
robots automatically testing your code for you (which, we agree,
would be nice).
Automated testing does have everything to do with trying to make
sure, as far as possible, that your code is working properly. Writing code to test
code?!? Isn‛t that one
But, how is this possible? of those chicken and egg
The truth is, it’s very hard to know for sure that your code is working things?
perfectly 100% of the time. Programs and software systems are
complex beasts. Over time, they grow, and it gets increasingly hard
to know for sure if they are working quite the way you want them to.
To help, programmers test their code. When they are done, they
test, test, then test some more. And just to be sure, they test again. In
fact, programmers often test to destruction, which refers to attempting
everything they know to try and break the program and make it
misbehave. If testing finds a problem, the code is then fixed so that
whatever it was isn’t a problem anymore.
To help with the grunt work that testing sometimes is, Python comes
with a handy built-in module called unittest. This module’s sole
purpose in life is to allow you to write code that tests your code. Trust us,
this isn’t as strange as it sounds. The unittest module provides
a framework within which you can exercise your code to ensure it’s
working the way you want it to.

The idea is simple enough: as you write your code, you write a
little extra bit of code based on unittest to check that your new
code is working properly. If the test works (that is, it successfully
demonstrates that your code is OK), you are then in a position to
automate the testing of your code by reusing your unittest code.
For more details, check out the description of unittest in your
favorite Python book or online in the official Python documentation.

And now that you know about unittest, you have no excuse not
to use it to test your code.

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