blob: 0aaf6ea53608fc67c0a9bc52b5342c0ce95be90b [file] [log] [blame]
.. SPDX-License-Identifier: GPL-2.0
Kernel Testing Guide
There are a number of different tools for testing the Linux kernel, so knowing
when to use each of them can be a challenge. This document provides a rough
overview of their differences, and how they fit together.
Writing and Running Tests
The bulk of kernel tests are written using either the kselftest or KUnit
frameworks. These both provide infrastructure to help make running tests and
groups of tests easier, as well as providing helpers to aid in writing new
If you're looking to verify the behaviour of the Kernel — particularly specific
parts of the kernel — then you'll want to use KUnit or kselftest.
The Difference Between KUnit and kselftest
KUnit (Documentation/dev-tools/kunit/index.rst) is an entirely in-kernel system
for "white box" testing: because test code is part of the kernel, it can access
internal structures and functions which aren't exposed to userspace.
KUnit tests therefore are best written against small, self-contained parts
of the kernel, which can be tested in isolation. This aligns well with the
concept of 'unit' testing.
For example, a KUnit test might test an individual kernel function (or even a
single codepath through a function, such as an error handling case), rather
than a feature as a whole.
This also makes KUnit tests very fast to build and run, allowing them to be
run frequently as part of the development process.
There is a KUnit test style guide which may give further pointers in
kselftest (Documentation/dev-tools/kselftest.rst), on the other hand, is
largely implemented in userspace, and tests are normal userspace scripts or
This makes it easier to write more complicated tests, or tests which need to
manipulate the overall system state more (e.g., spawning processes, etc.).
However, it's not possible to call kernel functions directly from kselftest.
This means that only kernel functionality which is exposed to userspace somehow
(e.g. by a syscall, device, filesystem, etc.) can be tested with kselftest. To
work around this, some tests include a companion kernel module which exposes
more information or functionality. If a test runs mostly or entirely within the
kernel, however, KUnit may be the more appropriate tool.
kselftest is therefore suited well to tests of whole features, as these will
expose an interface to userspace, which can be tested, but not implementation
details. This aligns well with 'system' or 'end-to-end' testing.
For example, all new system calls should be accompanied by kselftest tests.
Code Coverage Tools
The Linux Kernel supports two different code coverage measurement tools. These
can be used to verify that a test is executing particular functions or lines
of code. This is useful for determining how much of the kernel is being tested,
and for finding corner-cases which are not covered by the appropriate test.
Documentation/dev-tools/gcov.rst is GCC's coverage testing tool, which can be
used with the kernel to get global or per-module coverage. Unlike KCOV, it
does not record per-task coverage. Coverage data can be read from debugfs,
and interpreted using the usual gcov tooling.
Documentation/dev-tools/kcov.rst is a feature which can be built in to the
kernel to allow capturing coverage on a per-task level. It's therefore useful
for fuzzing and other situations where information about code executed during,
for example, a single syscall is useful.
Dynamic Analysis Tools
The kernel also supports a number of dynamic analysis tools, which attempt to
detect classes of issues when they occur in a running kernel. These typically
each look for a different class of bugs, such as invalid memory accesses,
concurrency issues such as data races, or other undefined behaviour like
integer overflows.
Some of these tools are listed below:
* kmemleak detects possible memory leaks. See
* KASAN detects invalid memory accesses such as out-of-bounds and
use-after-free errors. See Documentation/dev-tools/kasan.rst
* UBSAN detects behaviour that is undefined by the C standard, like integer
overflows. See Documentation/dev-tools/ubsan.rst
* KCSAN detects data races. See Documentation/dev-tools/kcsan.rst
* KFENCE is a low-overhead detector of memory issues, which is much faster than
KASAN and can be used in production. See Documentation/dev-tools/kfence.rst
* lockdep is a locking correctness validator. See
* There are several other pieces of debug instrumentation in the kernel, many
of which can be found in lib/Kconfig.debug
These tools tend to test the kernel as a whole, and do not "pass" like
kselftest or KUnit tests. They can be combined with KUnit or kselftest by
running tests on a kernel with these tools enabled: you can then be sure
that none of these errors are occurring during the test.
Some of these tools integrate with KUnit or kselftest and will
automatically fail tests if an issue is detected.
Static Analysis Tools
In addition to testing a running kernel, one can also analyze kernel source code
directly (**at compile time**) using **static analysis** tools. The tools
commonly used in the kernel allow one to inspect the whole source tree or just
specific files within it. They make it easier to detect and fix problems during
the development process.
Sparse can help test the kernel by performing type-checking, lock checking,
value range checking, in addition to reporting various errors and warnings while
examining the code. See the Documentation/dev-tools/sparse.rst documentation
page for details on how to use it.
Smatch extends Sparse and provides additional checks for programming logic
mistakes such as missing breaks in switch statements, unused return values on
error checking, forgetting to set an error code in the return of an error path,
etc. Smatch also has tests against more serious issues such as integer
overflows, null pointer dereferences, and memory leaks. See the project page at
Coccinelle is another static analyzer at our disposal. Coccinelle is often used
to aid refactoring and collateral evolution of source code, but it can also help
to avoid certain bugs that occur in common code patterns. The types of tests
available include API tests, tests for correct usage of kernel iterators, checks
for the soundness of free operations, analysis of locking behavior, and further
tests known to help keep consistent kernel usage. See the
Documentation/dev-tools/coccinelle.rst documentation page for details.
Beware, though, that static analysis tools suffer from **false positives**.
Errors and warns need to be evaluated carefully before attempting to fix them.
When to use Sparse and Smatch
Sparse does type checking, such as verifying that annotated variables do not
cause endianness bugs, detecting places that use ``__user`` pointers improperly,
and analyzing the compatibility of symbol initializers.
Smatch does flow analysis and, if allowed to build the function database, it
also does cross function analysis. Smatch tries to answer questions like where
is this buffer allocated? How big is it? Can this index be controlled by the
user? Is this variable larger than that variable?
It's generally easier to write checks in Smatch than it is to write checks in
Sparse. Nevertheless, there are some overlaps between Sparse and Smatch checks.
Strong points of Smatch and Coccinelle
Coccinelle is probably the easiest for writing checks. It works before the
pre-processor so it's easier to check for bugs in macros using Coccinelle.
Coccinelle also creates patches for you, which no other tool does.
For example, with Coccinelle you can do a mass conversion from
``kmalloc(x * size, GFP_KERNEL)`` to ``kmalloc_array(x, size, GFP_KERNEL)``, and
that's really useful. If you just created a Smatch warning and try to push the
work of converting on to the maintainers they would be annoyed. You'd have to
argue about each warning if can really overflow or not.
Coccinelle does no analysis of variable values, which is the strong point of
Smatch. On the other hand, Coccinelle allows you to do simple things in a simple