mirror of
https://github.com/carbon-language/carbon-lang.git
synced 2026-09-24 22:02:23 +01:00
Chandler Carruth
0e8e94d8bf
This is pretty rough both in code-quality and what it tries to cover. The coverage approximation is probably fine as it mostly just gives some vague idea of the distributions we should aim at when evaluating performance. We still want performance with unusual distributions to be good. Happy to improve the code quality if folks want, and especially with suggestions on what would help. For this kind of quick scripting, it seemed "fine" to me.
288 lines
10 KiB
Python
Executable File
288 lines
10 KiB
Python
Executable File
#!/usr/bin/env python3
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"""Script to compute statistics about source code."""
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from __future__ import annotations
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__copyright__ = """
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Part of the Carbon Language project, under the Apache License v2.0 with LLVM
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Exceptions. See /LICENSE for license information.
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SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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"""
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import argparse
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from alive_progress import alive_bar # type:ignore
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from multiprocessing import Pool
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import re
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import termplotlib as tpl # type:ignore
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from pathlib import Path
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from typing import Dict, List, Optional
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from dataclasses import dataclass, field, asdict
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from collections import Counter
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BLANK_RE = re.compile(r"\s*")
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COMMENT_RE = re.compile(r"\s*///*\s*")
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LINE_RE = re.compile(
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r"""
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(?P<class_intro>\b(class|struct)\s+(?P<class_name>\w+)\b)|
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(?P<end_open_curly>{\s*(?P<open_curly_trailing_comment>//.*)?)|
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(?P<trailing_comment>//.*)|
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(?P<internal_comment>/\*.*\*/)|
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(?P<string_literal>"([^"]|\\")*"|'([^']|\\')*')|
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(?P<float_literal>\b(0[xb][0-9a-fA-F']*|[0-9][0-9']*)\.[0-9a-fA-F']*([eEpP][0-9a-fA-F']*)?)|
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(?P<int_literal>\b(0[xb][0-9a-fA-F']+|[0-9][0-9']*)([eEpP][0-9a-fA-F']*)?)|
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(?P<symbol>[\[\]{}(),.;]|[-+=!@#$%^&*/?|<>]+)|
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(?P<keyword>\b(auto|bool|break|case|catch|char|class|const|continue|default|do|double|else|enum|explicit|extern|false|float|for|friend|goto|if|inline|int|long|mutable|namespace|new|nullptr|operator|private|protected|public|return|short|signed|sizeof|static|struct|switch|template|this|throw|true|try|typedef|union|unsigned|using|virtual|void|while)\b)|
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(?P<id>\b\w+\b)
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""",
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re.X,
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)
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@dataclass
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class Stats:
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"""Stats collected while scanning source files"""
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lines: int = 0
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blank_lines: int = 0
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comment_lines: int = 0
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empty_comment_lines: int = 0
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comment_line_widths: Counter[int] = field(default_factory=lambda: Counter())
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lines_with_trailing_comments: int = 0
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classes: int = 0
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internal_comments: int = 0
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string_literals: int = 0
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string_literals_per_line: Counter[int] = field(
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default_factory=lambda: Counter()
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)
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int_literals: int = 0
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int_literals_per_line: Counter[int] = field(
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default_factory=lambda: Counter()
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)
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float_literals: int = 0
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float_literals_per_line: Counter[int] = field(
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default_factory=lambda: Counter()
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)
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symbols: int = 0
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symbols_per_line: Counter[int] = field(default_factory=lambda: Counter())
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keywords: int = 0
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keywords_per_line: Counter[int] = field(default_factory=lambda: Counter())
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identifiers: int = 0
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identifier_widths: Counter[int] = field(default_factory=lambda: Counter())
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ids_per_line: Counter[int] = field(default_factory=lambda: Counter())
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def accumulate(self, other: Stats) -> None:
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self.lines += other.lines
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self.blank_lines += other.blank_lines
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self.empty_comment_lines += other.empty_comment_lines
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self.comment_lines += other.comment_lines
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self.comment_line_widths.update(other.comment_line_widths)
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self.lines_with_trailing_comments += other.lines_with_trailing_comments
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self.classes += other.classes
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self.internal_comments += other.internal_comments
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self.string_literals += other.string_literals
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self.string_literals_per_line.update(other.string_literals_per_line)
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self.int_literals += other.int_literals
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self.int_literals_per_line.update(other.int_literals_per_line)
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self.float_literals += other.float_literals
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self.float_literals_per_line.update(other.float_literals_per_line)
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self.symbols += other.symbols
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self.symbols_per_line.update(other.symbols_per_line)
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self.keywords += other.keywords
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self.keywords_per_line.update(other.keywords_per_line)
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self.identifiers += other.identifiers
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self.identifier_widths.update(other.identifier_widths)
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self.ids_per_line.update(other.ids_per_line)
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def scan_file(file: Path) -> Stats:
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"""Scans the provided file and accumulates stats."""
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stats = Stats()
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for line in file.open():
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# Strip off the line endings.
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line = line.rstrip("\r\n")
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# Skip over super long lines that are often URLs or structured data that
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# doesn't match "normal" source code patterns.
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if len(line) > 80:
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continue
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stats.lines += 1
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if re.fullmatch(BLANK_RE, line):
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stats.blank_lines += 1
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continue
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if m := re.match(COMMENT_RE, line):
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stats.comment_lines += 1
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if m.end() == len(line):
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stats.empty_comment_lines += 1
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else:
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stats.comment_line_widths[len(line)] += 1
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continue
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line_string_literals = 0
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line_int_literals = 0
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line_float_literals = 0
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line_symbols = 0
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line_keywords = 0
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line_identifiers = 0
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for m in re.finditer(LINE_RE, line):
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if m.group("trailing_comment"):
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stats.lines_with_trailing_comments += 1
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break
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if m.group("class_intro"):
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stats.classes += 1
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line_keywords += 1
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line_identifiers += 1
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stats.identifier_widths[len(m.group("class_name"))] += 1
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elif m.group("end_open_curly"):
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line_symbols += 1
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elif m.group("internal_comment"):
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stats.internal_comments += 1
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elif m.group("string_literal"):
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line_string_literals += 1
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elif m.group("int_literal"):
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line_int_literals += 1
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elif m.group("float_literal"):
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line_float_literals += 1
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elif m.group("symbol"):
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line_symbols += 1
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elif m.group("keyword"):
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line_keywords += 1
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else:
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assert m.group("id"), "Line is '%s', and match is '%s'" % (
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line,
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line[m.start() : m.end()],
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)
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line_identifiers += 1
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stats.identifier_widths[len(m.group("id"))] += 1
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stats.string_literals += line_string_literals
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stats.string_literals_per_line[line_string_literals] += 1
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stats.int_literals += line_int_literals
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stats.int_literals_per_line[line_int_literals] += 1
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stats.float_literals += line_float_literals
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stats.float_literals_per_line[line_float_literals] += 1
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stats.symbols += line_symbols
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stats.symbols_per_line[line_symbols] += 1
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stats.keywords += line_keywords
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stats.keywords_per_line[line_keywords] += 1
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stats.identifiers += line_identifiers
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stats.ids_per_line[line_identifiers] += 1
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return stats
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def parse_args(args: Optional[List[str]] = None) -> argparse.Namespace:
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"""Parsers command-line arguments and flags."""
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument(
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"files",
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metavar="FILE",
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type=Path,
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nargs="+",
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help="A file to scan while collecting statistics.",
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)
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return parser.parse_args(args=args)
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def main() -> None:
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parsed_args = parse_args()
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stats = Stats()
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with alive_bar(len(parsed_args.files)) as bar:
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with Pool() as p:
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for file_stats in p.imap_unordered(scan_file, parsed_args.files):
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stats.accumulate(file_stats)
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bar()
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print(
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"""
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## Stats ##
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Lines: %(lines)d
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Blank lines: %(blank_lines)d
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Comment lines: %(comment_lines)d
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Empty comment lines: %(empty_comment_lines)d
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Lines with trailing comments: %(lines_with_trailing_comments)d
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Classes: %(classes)d
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Internal comments: %(internal_comments)d
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String literals: %(string_literals)d
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Int literals: %(int_literals)d
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Float literals: %(float_literals)d
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Symbols: %(symbols)d
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Keywords: %(keywords)d
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IDs: %(identifiers)d"""
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% asdict(stats)
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)
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tokens = (
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stats.string_literals
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+ stats.int_literals
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+ stats.float_literals
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+ stats.symbols
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+ stats.keywords
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+ stats.identifiers
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)
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print(
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f"""
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Fraction of blank lines: {stats.blank_lines / stats.lines}
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Fraction of comment lines: {stats.comment_lines / stats.lines}
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Total counted tokens: {tokens}
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Fraction string literals: {stats.string_literals / tokens}
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Fraction int literals: {stats.int_literals / tokens}
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Fraction float literals: {stats.float_literals / tokens}
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Fraction symbols: {stats.symbols / tokens}
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Fraction keywords: {stats.keywords / tokens}
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Fraction IDs: {stats.identifiers / tokens}
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"""
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)
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def print_histogram(
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title: str, data: Dict[int, int], column_format: str
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) -> None:
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print()
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key_min = min(data.keys())
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key_max = max(data.keys()) + 1
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values = [data.get(k, 0) for k in range(key_min, key_max)]
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keys = [column_format % k for k in range(key_min, key_max)]
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total = sum(values)
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median = key_min
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count = total
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for k in range(key_min, key_max):
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count -= data.get(k, 0)
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if count <= total / 2:
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median = k
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break
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print(title + f" (median: {median})")
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fig = tpl.figure()
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fig.barh(values, keys)
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fig.show()
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print_histogram(
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"## Comment line widths ##", stats.comment_line_widths, "%d columns"
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)
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print_histogram(
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"## String literals per line ##",
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stats.string_literals_per_line,
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"%d literals",
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)
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print_histogram(
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"## Int literals per line ##",
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stats.int_literals_per_line,
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"%d literals",
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)
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print_histogram(
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"## Float literals per line ##",
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stats.float_literals_per_line,
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"%d literals",
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)
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print_histogram(
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"## Symbols per line ##", stats.symbols_per_line, "%d symbols"
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)
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print_histogram(
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"## Keywords per line ##", stats.keywords_per_line, "%d keywords"
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)
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print_histogram("## ID widths ##", stats.identifier_widths, "%d characters")
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print_histogram("## IDs per line ##", stats.ids_per_line, "%d ids")
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if __name__ == "__main__":
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main()
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