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Description:

pythonabc 0.2.0

Python ABC

A python implementation of the ABC Software metric:

The ABC software metric was introduced by Jerry Fitzpatrick in 1997 to overcome the drawbacks of the LOC. The metric defines an ABC score as a triplet of values that represent the size of a set of source code statements. An ABC score is calculated by counting the number of assignments (A), number of branches (B), and number of conditionals (C) in a program. ABC score can be applied to individual methods, functions, classes, modules or files within a program.

Fitzpatrick's original paper is, at the time of writing, only available via the Wayback
Machine, so a copy of it is included in this repo as well.
The paper lists the counting rules for C, C++ and Java, so here are the rules this repo uses for
Python:

Add one to the assignment count when:

Occurrence of an assignment operator (excluding default parameter assignments).

Add one to the branch count when:

Occurrence of a function call/await or a class method call/await.
Occurrence of a class instantiation.

Add one to the condition count when:

Occurrence of a conditional operator.
Occurrence of the following keywords: else, elif, except.
Occurrence of an assert statement without a conditional operator.

Usage
Install the requirements in your virtual environment of choice, then you can see the command line
arguments that are available:
$ python -m python_abc --help
usage: python_abc [-h] [--debug DEBUG] [--sort SORT] [--verbose VERBOSE] path

A python implementation of the ABC Software metric: https://en.wikipedia.org/wiki/ABC_Software_Metric

positional arguments:
path path to directory or file

optional arguments:
-h, --help show this help message and exit
--debug DEBUG display AST output for each element in the parsed tree
--sort SORT sort files from highest to lowest magnitude
--verbose VERBOSE display marked-up file

Given file.py that contains the following text:
if a and b:
print(a)
else:
print(b)

a = sum(i for i in range(1000) if i % 3 == 0 and i % 5 == 0)

def f(n):
def inner(n):
return n ** 2
if n == 0:
return 1
elif n == 1:
return n
elif n < 5:
return (n - 1) ** 2
return n * pow(inner(n), f(n - 1), n - 3)

You can get the barebones output as follows:
$ python -m python_abc /path/to/file.py
/path/to/file.py <1, 7, 10> (12.2)

Passing the verbose flag will give more detail:
$ python -m python_abc file.py --verbose=true
cc | if a and b:
b | print(a)
c | else:
b | print(b)
|
abbcc | a = sum(i for i in range(1000) if i % 3 == 0 and i % 5 == 0)
|
| def f(n):
| def inner(n):
| return n ** 2
c | if n == 0:
| return 1
cc | elif n == 1:
| return n
cc | elif n < 5:
| return (n - 1) ** 2
bbb | return n * pow(inner(n), f(n - 1), n - 3)
file.py <1, 7, 10> (12.2)

If you want to inspect the abstract syntax tree for the file you can pass the debug flag, which
will print out each node from the tree and the vector that resulted from it.
The path argument can also be a path to a directory, in which case all Python files in that
directory (and its sub-directories) will be scanned, at which point it can be useful to pass the
sort flag to rank the files by ABC magnitude:
$ python -m python_abc . --sort
./calculate.py <18, 56, 23> (63.2)
./vector.py <12, 23, 11> (28.2)
./main.py <10, 23, 8> (26.3)
./tests/test_vector.py <4, 19, 10> (21.8)
./tests/__init__.py <4, 12, 1> (12.7)
./tests/test_radon_test_cases.py <1, 2, 1> (2.4)
./tests/test_calculate_condition.py <1, 2, 1> (2.4)
./tests/test_calculate_empty.py <1, 2, 1> (2.4)
./tests/test_calculate_assignment.py <1, 2, 1> (2.4)
./tests/test_calculate_branch.py <1, 2, 1> (2.4)

Finally you can pass a cores argument to tell the library how many CPU cores to use. By
default the library will try to use all the cores that are available on your machine.