Files
carbon-lang/scripts/source_stats.py
T
Chandler Carruth 2de7d262b4 Add a script to scan source code for basic stats. (#3150)
This is a rough script that uses regexes to do a simple scan of source
code and extract some basic source code statistics. Things like column
width, comment line density, identifier lengths and densities.

After scanning, it prints out both raw stats and in a few cases renders
a quick histogram to the terminal to help visualize a relevant
distribution.

I threw this together pretty quickly, and this is an area of Python I
have very limited familiarity with, so happy to have any suggestions for
how to better approach this.
2023-08-25 22:41:12 +00:00

165 lines
5.2 KiB
Python
Executable File

#!/usr/bin/env python3
"""Script to compute statistics about source code."""
from __future__ import annotations
__copyright__ = """
Part of the Carbon Language project, under the Apache License v2.0 with LLVM
Exceptions. See /LICENSE for license information.
SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
"""
import argparse
from alive_progress import alive_bar # type:ignore
from multiprocessing import Pool
import re
import termplotlib as tpl # type:ignore
from pathlib import Path
from typing import Dict, List, Optional
from dataclasses import dataclass, field, asdict
from collections import Counter
BLANK_RE = re.compile(r"\s*")
COMMENT_RE = re.compile(r"\s*///*\s*")
LINE_RE = re.compile(
r"""
(?P<class_intro>\b(class|struct)\s+(?P<class_name>\w+)\b)|
(?P<end_open_curly>{\s*(?P<open_curly_trailing_comment>//.*)?)|
(?P<trailing_comment>//.*)|
(?P<id>\b\w+\b)
""",
re.X,
)
@dataclass
class Stats:
"""Stats collected while scanning source files"""
lines: int = 0
blank_lines: int = 0
comment_lines: int = 0
empty_comment_lines: int = 0
comment_line_widths: Counter[int] = field(default_factory=lambda: Counter())
lines_with_trailing_comments: int = 0
classes: int = 0
identifiers: int = 0
identifier_widths: Counter[int] = field(default_factory=lambda: Counter())
ids_per_line: Counter[int] = field(default_factory=lambda: Counter())
def accumulate(self, other: Stats) -> None:
self.lines += other.lines
self.blank_lines += other.blank_lines
self.empty_comment_lines += other.empty_comment_lines
self.comment_lines += other.comment_lines
self.comment_line_widths.update(other.comment_line_widths)
self.lines_with_trailing_comments += other.lines_with_trailing_comments
self.classes += other.classes
self.identifiers += other.identifiers
self.identifier_widths.update(other.identifier_widths)
self.ids_per_line.update(other.ids_per_line)
def scan_file(file: Path) -> Stats:
"""Scans the provided file and accumulates stats."""
stats = Stats()
for line in file.open():
# Strip off the line endings.
line = line.rstrip("\r\n")
# Skip over super long lines that are often URLs or structured data that
# doesn't match "normal" source code patterns.
if len(line) > 80:
continue
stats.lines += 1
if re.fullmatch(BLANK_RE, line):
stats.blank_lines += 1
continue
if m := re.match(COMMENT_RE, line):
stats.comment_lines += 1
if m.end() == len(line):
stats.empty_comment_lines += 1
else:
stats.comment_line_widths[len(line)] += 1
continue
line_identifiers = 0
for m in re.finditer(LINE_RE, line):
if m.group("trailing_comment"):
stats.lines_with_trailing_comments += 1
break
if m.group("class_intro"):
stats.classes += 1
line_identifiers += 1
stats.identifier_widths[len(m.group("class_name"))] += 1
elif m.group("end_open_curly"):
pass
else:
assert m.group("id"), "Line is '%s', and match is '%s'" % (
line,
line[m.start() : m.end()],
)
line_identifiers += 1
stats.identifier_widths[len(m.group("id"))] += 1
stats.identifiers += line_identifiers
stats.ids_per_line[line_identifiers] += 1
return stats
def parse_args(args: Optional[List[str]] = None) -> argparse.Namespace:
"""Parsers command-line arguments and flags."""
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument(
"files",
metavar="FILE",
type=Path,
nargs="+",
help="A file to scan while collecting statistics.",
)
return parser.parse_args(args=args)
def main() -> None:
parsed_args = parse_args()
stats = Stats()
with alive_bar(len(parsed_args.files)) as bar:
with Pool() as p:
for file_stats in p.imap_unordered(scan_file, parsed_args.files):
stats.accumulate(file_stats)
bar()
print(
"""
## Stats ##
Lines: %(lines)d
Blank lines: %(blank_lines)d
Comment lines: %(comment_lines)d
Empty comment lines: %(empty_comment_lines)d
Lines with trailing comments: %(lines_with_trailing_comments)d
Classes: %(classes)d
IDs: %(identifiers)d"""
% asdict(stats)
)
def print_histogram(
title: str, data: Dict[int, int], column_format: str
) -> None:
print()
print(title)
key_min = min(data.keys())
key_max = max(data.keys()) + 1
values = [data.get(k, 0) for k in range(key_min, key_max)]
keys = [column_format % k for k in range(key_min, key_max)]
fig = tpl.figure()
fig.barh(values, keys)
fig.show()
print_histogram(
"## Comment line widths ##", stats.comment_line_widths, "%d columns"
)
print_histogram("## ID widths ##", stats.identifier_widths, "%d characters")
print_histogram("## IDs per line ##", stats.ids_per_line, "%d ids")
if __name__ == "__main__":
main()