Testing¶
Test Suite Structure¶
The LLDB test suite consists of three different kinds of test:
Unit tests: written in C++ using the googletest unit testing library.
Shell tests: Integration tests that test the debugger through the command line. These tests interact with the debugger either through the command line driver or through
lldb-test
which is a tool that exposes the internal data structures in an easy-to-parse way for testing. Most people will know these as lit tests in LLVM, although lit is the test driver and ShellTest is the test format that usesRUN:
lines. FileCheck is used to verify the output.API tests: Integration tests that interact with the debugger through the SB API. These are written in Python and use LLDB’s
dotest.py
testing framework on top of Python’s unittest2.
All three test suites use lit
(LLVM Integrated Tester ) as the test driver. The test
suites can be run as a whole or separately.
Unit Tests¶
Unit tests are located under lldb/unittests
. If it’s possible to test
something in isolation or as a single unit, you should make it a unit test.
Often you need instances of the core objects such as a debugger, target or process, in order to test something meaningful. We already have a handful of tests that have the necessary boiler plate, but this is something we could abstract away and make it more user friendly.
Shell Tests¶
Shell tests are located under lldb/test/Shell
. These tests are generally
built around checking the output of lldb
(the command line driver) or
lldb-test
using FileCheck
. Shell tests are generally small and fast to
write because they require little boilerplate.
lldb-test
is a relatively new addition to the test suite. It was the first
tool that was added that is designed for testing. Since then it has been
continuously extended with new subcommands, improving our test coverage. Among
other things you can use it to query lldb for symbol files, for object files
and breakpoints.
Obviously shell tests are great for testing the command line driver itself or
the subcomponents already exposed by lldb-test. But when it comes to LLDB’s
vast functionality, most things can be tested both through the driver as well
as the Python API. For example, to test setting a breakpoint, you could do it
from the command line driver with b main
or you could use the SB API and do
something like target.BreakpointCreateByName
1.
A good rule of thumb is to prefer shell tests when what is being tested is
relatively simple. Expressivity is limited compared to the API tests, which
means that you have to have a well-defined test scenario that you can easily
match with FileCheck
.
Another thing to consider are the binaries being debugged, which we call
inferiors. For shell tests, they have to be relatively simple. The
dotest.py
test framework has extensive support for complex build scenarios
and different variants, which is described in more detail below, while shell
tests are limited to single lines of shell commands with compiler and linker
invocations.
On the same topic, another interesting aspect of the shell tests is that there you can often get away with a broken or incomplete binary, whereas the API tests almost always require a fully functional executable. This enables testing of (some) aspects of handling of binaries with non-native architectures or operating systems.
Finally, the shell tests always run in batch mode. You start with some input and the test verifies the output. The debugger can be sensitive to its environment, such as the the platform it runs on. It can be hard to express that the same test might behave slightly differently on macOS and Linux. Additionally, the debugger is an interactive tool, and the shell test provide no good way of testing those interactive aspects, such as tab completion for example.
API Tests¶
API tests are located under lldb/test/API
. They are run with the
dotest.py
. Tests are written in Python and test binaries (inferiors) are
compiled with Make. The majority of API tests are end-to-end tests that compile
programs from source, run them, and debug the processes.
As mentioned before, dotest.py
is LLDB’s testing framework. The
implementation is located under lldb/packages/Python/lldbsuite
. We have
several extensions and custom test primitives on top of what’s offered by
unittest2. Those can be
found in
lldbtest.py.
Below is the directory layout of the example API test.
The test directory will always contain a python file, starting with Test
.
Most of the tests are structured as a binary being debugged, so there will be
one or more source files and a Makefile
.
sample_test
├── Makefile
├── TestSampleTest.py
└── main.c
Let’s start with the Python test file. Every test is its own class and can have
one or more test methods, that start with test_
. Many tests define
multiple test methods and share a bunch of common code. For example, for a
fictive test that makes sure we can set breakpoints we might have one test
method that ensures we can set a breakpoint by address, on that sets a
breakpoint by name and another that sets the same breakpoint by file and line
number. The setup, teardown and everything else other than setting the
breakpoint could be shared.
Our testing framework also has a bunch of utilities that abstract common
operations, such as creating targets, setting breakpoints etc. When code is
shared across tests, we extract it into a utility in lldbutil
. It’s always
worth taking a look at lldbutil
to see if there’s a utility to simplify some of the testing boiler plate.
Because we can’t always audit every existing test, this is doubly true when
looking at an existing test for inspiration.
It’s possible to skip or XFAIL tests using decorators. You’ll see them a lot. The debugger can be sensitive to things like the architecture, the host and target platform, the compiler version etc. LLDB comes with a range of predefined decorators for these configurations.
@expectedFailureAll(archs=["aarch64"], oslist=["linux"]
Another great thing about these decorators is that they’re very easy to extend, it’s even possible to define a function in a test case that determines whether the test should be run or not.
@expectedFailure(checking_function_name)
In addition to providing a lot more flexibility when it comes to writing the
test, the API test also allow for much more complex scenarios when it comes to
building inferiors. Every test has its own Makefile
, most of them only a
few lines long. A shared Makefile
(Makefile.rules
) with about a
thousand lines of rules takes care of most if not all of the boiler plate,
while individual make files can be used to build more advanced tests.
Here’s an example of a simple Makefile
used by the example test.
C_SOURCES := main.c
CFLAGS_EXTRAS := -std=c99
include Makefile.rules
Finding the right variables to set can be tricky. You can always take a look at
Makefile.rules
but often it’s easier to find an existing Makefile
that does something
similar to what you want to do.
Another thing this enables is having different variants for the same test case. By default, we run every test for all 3 debug info formats, so once with DWARF from the object files, once with gmodules and finally with a dSYM on macOS or split DWARF (DWO) on Linux. But there are many more things we can test that are orthogonal to the test itself. On GreenDragon we have a matrix bot that runs the test suite under different configurations, with older host compilers and different DWARF versions.
As you can imagine, this quickly lead to combinatorial explosion in the number of variants. It’s very tempting to add more variants because it’s an easy way to increase test coverage. It doesn’t scale. It’s easy to set up, but increases the runtime of the tests and has a large ongoing cost.
The key take away is that the different variants don’t obviate the need for focused tests. So relying on it to test say DWARF5 is a really bad idea. Instead you should write tests that check the specific DWARF5 feature, and have the variant as a nice-to-have.
In conclusion, you’ll want to opt for an API test to test the API itself or when you need the expressivity, either for the test case itself or for the program being debugged. The fact that the API tests work with different variants mean that more general tests should be API tests, so that they can be run against the different variants.
Running The Tests¶
Note
On Windows any invocations of python should be replaced with python_d, the debug interpreter, when running the test suite against a debug version of LLDB.
Note
On NetBSD you must export LD_LIBRARY_PATH=$PWD/lib
in your environment.
This is due to lack of the $ORIGIN
linker feature.
Running the Full Test Suite¶
The easiest way to run the LLDB test suite is to use the check-lldb
build
target.
By default, the check-lldb
target builds the test programs with the same
compiler that was used to build LLDB. To build the tests with a different
compiler, you can set the LLDB_TEST_COMPILER
CMake variable.
It is possible to customize the architecture of the test binaries and compiler
used by appending -A
and -C
options respectively to the CMake variable
LLDB_TEST_USER_ARGS
. For example, to test LLDB against 32-bit binaries
built with a custom version of clang, do:
$ cmake -DLLDB_TEST_USER_ARGS="-A i386 -C /path/to/custom/clang" -G Ninja
$ ninja check-lldb
Note that multiple -A
and -C
flags can be specified to
LLDB_TEST_USER_ARGS
.
Running a Single Test Suite¶
Each test suite can be run separately, similar to running the whole test suite
with check-lldb
.
Use
check-lldb-unit
to run just the unit tests.Use
check-lldb-api
to run just the SB API tests.Use
check-lldb-shell
to run just the shell tests.
You can run specific subdirectories by appending the directory name to the
target. For example, to run all the tests in ObjectFile
, you can use the
target check-lldb-shell-objectfile
. However, because the unit tests and API
tests don’t actually live under lldb/test
, this convenience is only
available for the shell tests.
Running a Single Test¶
The recommended way to run a single test is by invoking the lit driver with a filter. This ensures that the test is run with the same configuration as when run as part of a test suite.
$ ./bin/llvm-lit -sv tools/lldb/test --filter <test>
Because lit automatically scans a directory for tests, it’s also possible to pass a subdirectory to run a specific subset of the tests.
$ ./bin/llvm-lit -sv tools/lldb/test/Shell/Commands/CommandScriptImmediateOutput
For the SB API tests it is possible to forward arguments to dotest.py
by
passing --param
to lit and setting a value for dotest-args
.
$ ./bin/llvm-lit -sv tools/lldb/test --param dotest-args='-C gcc'
Below is an overview of running individual test in the unit and API test suites without going through the lit driver.
Running a Specific Test or Set of Tests: API Tests¶
In addition to running all the LLDB test suites with the check-lldb
CMake
target above, it is possible to run individual LLDB tests. If you have a CMake
build you can use the lldb-dotest
binary, which is a wrapper around
dotest.py
that passes all the arguments configured by CMake.
Alternatively, you can use dotest.py
directly, if you want to run a test
one-off with a different configuration.
For example, to run the test cases defined in TestInferiorCrashing.py, run:
$ ./bin/lldb-dotest -p TestInferiorCrashing.py
$ cd $lldb/test
$ python dotest.py --executable <path-to-lldb> -p TestInferiorCrashing.py ../packages/Python/lldbsuite/test
If the test is not specified by name (e.g. if you leave the -p
argument
off), all tests in that directory will be executed:
$ ./bin/lldb-dotest functionalities/data-formatter
$ python dotest.py --executable <path-to-lldb> functionalities/data-formatter
Many more options that are available. To see a list of all of them, run:
$ python dotest.py -h
Running a Specific Test or Set of Tests: Unit Tests¶
The unit tests are simple executables, located in the build directory under tools/lldb/unittests
.
To run them, just run the test binary, for example, to run all the Host tests:
$ ./tools/lldb/unittests/Host/HostTests
To run a specific test, pass a filter, for example:
$ ./tools/lldb/unittests/Host/HostTests --gtest_filter=SocketTest.DomainListenConnectAccept
Running the Test Suite Remotely¶
Running the test-suite remotely is similar to the process of running a local test suite, but there are two things to have in mind:
You must have the lldb-server running on the remote system, ready to accept multiple connections. For more information on how to setup remote debugging see the Remote debugging page.
You must tell the test-suite how to connect to the remote system. This is achieved using the
--platform-name
,--platform-url
and--platform-working-dir
parameters todotest.py
. These parameters correspond to the platform select and platform connect LLDB commands. You will usually also need to specify the compiler and architecture for the remote system.
Currently, running the remote test suite is supported only with dotest.py
(or
dosep.py with a single thread), but we expect this issue to be addressed in the
near future.
Debugging Test Failures¶
On non-Windows platforms, you can use the -d
option to dotest.py
which
will cause the script to wait for a while until a debugger is attached.
Debugging Test Failures on Windows¶
On Windows, it is strongly recommended to use Python Tools for Visual Studio for debugging test failures. It can seamlessly step between native and managed code, which is very helpful when you need to step through the test itself, and then into the LLDB code that backs the operations the test is performing.
A quick guide to getting started with PTVS is as follows:
Install PTVS
- Create a Visual Studio Project for the Python code.
Go to File -> New -> Project -> Python -> From Existing Python Code.
Choose llvm/tools/lldb as the directory containing the Python code.
When asked where to save the .pyproj file, choose the folder
llvm/tools/lldb/pyproj
. This is a special folder that is ignored by the.gitignore
file, since it is not checked in.
Set test/dotest.py as the startup file
- Make sure there is a Python Environment installed for your distribution. For example, if you installed Python to
C:\Python35
, PTVS needs to know that this is the interpreter you want to use for running the test suite. Go to Tools -> Options -> Python Tools -> Environment Options
Click Add Environment, and enter Python 3.5 Debug for the name. Fill out the values correctly.
- Make sure there is a Python Environment installed for your distribution. For example, if you installed Python to
- Configure the project to use this debug interpreter.
Right click the Project node in Solution Explorer.
In the General tab, Make sure Python 3.5 Debug is the selected Interpreter.
In Debug/Search Paths, enter the path to your ninja/lib/site-packages directory.
In Debug/Environment Variables, enter
VCINSTALLDIR=C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\
.If you want to enabled mixed mode debugging, check Enable native code debugging (this slows down debugging, so enable it only on an as-needed basis.)
- Set the command line for the test suite to run.
Right click the project in solution explorer and choose the Debug tab.
Enter the arguments to dotest.py.
Example command options:
--arch=i686
# Path to debug lldb.exe
--executable D:/src/llvmbuild/ninja/bin/lldb.exe
# Directory to store log files
-s D:/src/llvmbuild/ninja/lldb-test-traces
-u CXXFLAGS -u CFLAGS
# If a test crashes, show JIT debugging dialog.
--enable-crash-dialog
# Path to release clang.exe
-C d:\src\llvmbuild\ninja_release\bin\clang.exe
# Path to the particular test you want to debug.
-p TestPaths.py
# Root of test tree
D:\src\llvm\tools\lldb\packages\Python\lldbsuite\test
--arch=i686 --executable D:/src/llvmbuild/ninja/bin/lldb.exe -s D:/src/llvmbuild/ninja/lldb-test-traces -u CXXFLAGS -u CFLAGS --enable-crash-dialog -C d:\src\llvmbuild\ninja_release\bin\clang.exe -p TestPaths.py D:\src\llvm\tools\lldb\packages\Python\lldbsuite\test --no-multiprocess