kernel_optimize_test/scripts/clang-tools/run-clang-tools.py
Nathan Huckleberry 6ad7cbc015 Makefile: Add clang-tidy and static analyzer support to makefile
This patch adds clang-tidy and the clang static-analyzer as make
targets. The goal of this patch is to make static analysis tools
usable and extendable by any developer or researcher who is familiar
with basic c++.

The current static analysis tools require intimate knowledge of the
internal workings of the static analysis. Clang-tidy and the clang
static analyzers expose an easy to use api and allow users unfamiliar
with clang to write new checks with relative ease.

===Clang-tidy===

Clang-tidy is an easily extendable 'linter' that runs on the AST.
Clang-tidy checks are easy to write and understand. A check consists of
two parts, a matcher and a checker. The matcher is created using a
domain specific language that acts on the AST
(https://clang.llvm.org/docs/LibASTMatchersReference.html).  When AST
nodes are found by the matcher a callback is made to the checker. The
checker can then execute additional checks and issue warnings.

Here is an example clang-tidy check to report functions that have calls
to local_irq_disable without calls to local_irq_enable and vice-versa.
Functions flagged with __attribute((annotation("ignore_irq_balancing")))
are ignored for analysis. (https://reviews.llvm.org/D65828)

===Clang static analyzer===

The clang static analyzer is a more powerful static analysis tool that
uses symbolic execution to find bugs. Currently there is a check that
looks for potential security bugs from invalid uses of kmalloc and
kfree. There are several more general purpose checks that are useful for
the kernel.

The clang static analyzer is well documented and designed to be
extensible.
(https://clang-analyzer.llvm.org/checker_dev_manual.html)
(https://github.com/haoNoQ/clang-analyzer-guide/releases/download/v0.1/clang-analyzer-guide-v0.1.pdf)

The main draw of the clang tools is how accessible they are. The clang
documentation is very nice and these tools are built specifically to be
easily extendable by any developer. They provide an accessible method of
bug-finding and research to people who are not overly familiar with the
kernel codebase.

Signed-off-by: Nathan Huckleberry <nhuck@google.com>
Reviewed-by: Nick Desaulniers <ndesaulniers@google.com>
Tested-by: Nick Desaulniers <ndesaulniers@google.com>
Tested-by: Lukas Bulwahn <lukas.bulwahn@gmail.com>
Signed-off-by: Masahiro Yamada <masahiroy@kernel.org>
2020-08-27 00:44:33 +09:00

75 lines
1.9 KiB
Python
Executable File

#!/usr/bin/env python
# SPDX-License-Identifier: GPL-2.0
#
# Copyright (C) Google LLC, 2020
#
# Author: Nathan Huckleberry <nhuck@google.com>
#
"""A helper routine run clang-tidy and the clang static-analyzer on
compile_commands.json.
"""
import argparse
import json
import multiprocessing
import os
import subprocess
import sys
def parse_arguments():
"""Set up and parses command-line arguments.
Returns:
args: Dict of parsed args
Has keys: [path, type]
"""
usage = """Run clang-tidy or the clang static-analyzer on a
compilation database."""
parser = argparse.ArgumentParser(description=usage)
type_help = "Type of analysis to be performed"
parser.add_argument("type",
choices=["clang-tidy", "clang-analyzer"],
help=type_help)
path_help = "Path to the compilation database to parse"
parser.add_argument("path", type=str, help=path_help)
return parser.parse_args()
def init(l, a):
global lock
global args
lock = l
args = a
def run_analysis(entry):
# Disable all checks, then re-enable the ones we want
checks = "-checks=-*,"
if args.type == "clang-tidy":
checks += "linuxkernel-*"
else:
checks += "clang-analyzer-*"
p = subprocess.run(["clang-tidy", "-p", args.path, checks, entry["file"]],
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT,
cwd=entry["directory"])
with lock:
sys.stderr.buffer.write(p.stdout)
def main():
args = parse_arguments()
lock = multiprocessing.Lock()
pool = multiprocessing.Pool(initializer=init, initargs=(lock, args))
# Read JSON data into the datastore variable
with open(args.path, "r") as f:
datastore = json.load(f)
pool.map(run_analysis, datastore)
if __name__ == "__main__":
main()