# Python ABC
[][1]
A python implementation of [the ABC Software metric][2]:
> 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][3], so [a copy of it is included in this repo][4] 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:
```bash
$ 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:
```python
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:
```bash
$ 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:
```bash
$ 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:
```bash
$ 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.
[1]: https://www.python.org/downloads/release/python-395/
[2]: https://en.wikipedia.org/wiki/ABC_Software_Metric
[3]: https://web.archive.org/web/20210606115110/https://www.softwarerenovation.com/ABCMetric.pdf
[4]: https://github.com/eoinnoble/python-abc/blob/main/ABCMetric.pdf
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