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On Fri, Oct 17, 2014 at 3:24 PM, Mike Spreitzer <mspreitz at us.ibm.com> wrote:

> I like the idea of measuring complexity.  I looked briefly at `python -m
> mccabe`.  It seems to measure each method independently.  Is this really
> fair?


I think it is a good starting point. You still need to write methods that
do one thing well (simple and easy to read/test by it's self). But it stops
complex methods growing out of hand, by just failing the test and now you
have to look at refactoring. I think reviews can cover the "how good is
your refactor".


>  If I have a class with some big methods, and I break it down into more
> numerous and smaller methods, then the largest method gets smaller, but the
> number of methods gets larger.  A large number of methods is itself a form
> of complexity.  It is not clear to me that said re-org has necessarily made
> the class easier to understand.  I can also break one class into two, but
> it is not clear to me that the project has necessarily become easier to
> understand.


No, but a well written class *should* be easy to understand in isolation. I
don't think there is a tool to say 'your code sucks'.

-A


>  While it is true that when you truly make a project easier to understand
> you sometimes break it into more classes, it is also true that you can do a
> bad job of re-organizing a set of classes while still reducing the size of
> the largest method.  Has the McCabe metric been evaluated on Python
> projects?  There is a danger in focusing on what is easy to measure if that
> is not really what you want to optimize.
>
> BTW, I find that one of the complexity issues for me when I am learning
> about a Python class is doing the whole-program type inference so that I
> know what the arguments are.  It seems to me that if you want to measure
> complexity of Python code then something like the complexity of the
> argument typing should be taken into account.
>
> Regards,
> Mike
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