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Testing
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=======
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.. contents::
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:local:
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Test Suite Structure
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--------------------
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The LLDB test suite consists of three different kinds of test:
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* **Unit tests**: written in C++ using the googletest unit testing library.
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* **Shell tests**: Integration tests that test the debugger through the command
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line. These tests interact with the debugger either through the command line
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driver or through ``lldb-test`` which is a tool that exposes the internal
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data structures in an easy-to-parse way for testing. Most people will know
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these as *lit tests* in LLVM, although lit is the test driver and ShellTest
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is the test format that uses ``RUN:`` lines. `FileCheck
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<https://llvm.org/docs/CommandGuide/FileCheck.html>`_ is used to verify
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the output.
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* **API tests**: Integration tests that interact with the debugger through the
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SB API. These are written in Python and use LLDB's ``dotest.py`` testing
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framework on top of Python's `unittest2
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<https://docs.python.org/2/library/unittest.html>`_.
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All three test suites use ``lit`` (`LLVM Integrated Tester
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<https://llvm.org/docs/CommandGuide/lit.html>`_ ) as the test driver. The test
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suites can be run as a whole or separately.
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Unit Tests
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``````````
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Unit tests are located under ``lldb/unittests``. If it's possible to test
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something in isolation or as a single unit, you should make it a unit test.
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Often you need instances of the core objects such as a debugger, target or
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process, in order to test something meaningful. We already have a handful of
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tests that have the necessary boiler plate, but this is something we could
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abstract away and make it more user friendly.
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Shell Tests
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```````````
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Shell tests are located under ``lldb/test/Shell``. These tests are generally
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built around checking the output of ``lldb`` (the command line driver) or
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``lldb-test`` using ``FileCheck``. Shell tests are generally small and fast to
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write because they require little boilerplate.
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``lldb-test`` is a relatively new addition to the test suite. It was the first
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tool that was added that is designed for testing. Since then it has been
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continuously extended with new subcommands, improving our test coverage. Among
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other things you can use it to query lldb for symbol files, for object files
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and breakpoints.
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Obviously shell tests are great for testing the command line driver itself or
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the subcomponents already exposed by lldb-test. But when it comes to LLDB's
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vast functionality, most things can be tested both through the driver as well
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as the Python API. For example, to test setting a breakpoint, you could do it
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from the command line driver with ``b main`` or you could use the SB API and do
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something like ``target.BreakpointCreateByName`` [#]_.
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A good rule of thumb is to prefer shell tests when what is being tested is
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relatively simple. Expressivity is limited compared to the API tests, which
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means that you have to have a well-defined test scenario that you can easily
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match with ``FileCheck``.
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Another thing to consider are the binaries being debugged, which we call
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inferiors. For shell tests, they have to be relatively simple. The
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``dotest.py`` test framework has extensive support for complex build scenarios
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and different variants, which is described in more detail below, while shell
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tests are limited to single lines of shell commands with compiler and linker
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invocations.
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On the same topic, another interesting aspect of the shell tests is that there
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you can often get away with a broken or incomplete binary, whereas the API
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tests almost always require a fully functional executable. This enables testing
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of (some) aspects of handling of binaries with non-native architectures or
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operating systems.
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Finally, the shell tests always run in batch mode. You start with some input
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and the test verifies the output. The debugger can be sensitive to its
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environment, such as the the platform it runs on. It can be hard to express
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that the same test might behave slightly differently on macOS and Linux.
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Additionally, the debugger is an interactive tool, and the shell test provide
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no good way of testing those interactive aspects, such as tab completion for
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example.
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API Tests
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`````````
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API tests are located under ``lldb/test/API``. They are run with the
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``dotest.py``. Tests are written in Python and test binaries (inferiors) are
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compiled with Make. The majority of API tests are end-to-end tests that compile
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programs from source, run them, and debug the processes.
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As mentioned before, ``dotest.py`` is LLDB's testing framework. The
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implementation is located under ``lldb/packages/Python/lldbsuite``. We have
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several extensions and custom test primitives on top of what's offered by
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`unittest2 <https://docs.python.org/2/library/unittest.html>`_. Those can be
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found in
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`lldbtest.py <https://github.com/llvm/llvm-project/blob/master/lldb/packages/Python/lldbsuite/test/lldbtest.py>`_.
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Below is the directory layout of the `example API test
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<https://github.com/llvm/llvm-project/tree/master/lldb/test/API/sample_test>`_.
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The test directory will always contain a python file, starting with ``Test``.
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Most of the tests are structured as a binary being debugged, so there will be
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one or more source files and a ``Makefile``.
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::
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sample_test
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├── Makefile
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├── TestSampleTest.py
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└── main.c
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Let's start with the Python test file. Every test is its own class and can have
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one or more test methods, that start with ``test_``. Many tests define
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multiple test methods and share a bunch of common code. For example, for a
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fictive test that makes sure we can set breakpoints we might have one test
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method that ensures we can set a breakpoint by address, on that sets a
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breakpoint by name and another that sets the same breakpoint by file and line
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number. The setup, teardown and everything else other than setting the
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breakpoint could be shared.
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Our testing framework also has a bunch of utilities that abstract common
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operations, such as creating targets, setting breakpoints etc. When code is
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shared across tests, we extract it into a utility in ``lldbutil``. It's always
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worth taking a look at `lldbutil
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<https://github.com/llvm/llvm-project/blob/master/lldb/packages/Python/lldbsuite/test/lldbutil.py>`_
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to see if there's a utility to simplify some of the testing boiler plate.
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Because we can't always audit every existing test, this is doubly true when
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looking at an existing test for inspiration.
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It's possible to skip or `XFAIL
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<https://ftp.gnu.org/old-gnu/Manuals/dejagnu-1.3/html_node/dejagnu_6.html>`_
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tests using decorators. You'll see them a lot. The debugger can be sensitive to
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things like the architecture, the host and target platform, the compiler
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version etc. LLDB comes with a range of predefined decorators for these
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configurations.
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::
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@expectedFailureAll(archs=["aarch64"], oslist=["linux"]
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Another great thing about these decorators is that they're very easy to extend,
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it's even possible to define a function in a test case that determines whether
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the test should be run or not.
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::
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@expectedFailure(checking_function_name)
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In addition to providing a lot more flexibility when it comes to writing the
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test, the API test also allow for much more complex scenarios when it comes to
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building inferiors. Every test has its own ``Makefile``, most of them only a
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few lines long. A shared ``Makefile`` (``Makefile.rules``) with about a
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thousand lines of rules takes care of most if not all of the boiler plate,
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while individual make files can be used to build more advanced tests.
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Here's an example of a simple ``Makefile`` used by the example test.
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::
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C_SOURCES := main.c
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CFLAGS_EXTRAS := -std=c99
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include Makefile.rules
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Finding the right variables to set can be tricky. You can always take a look at
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`Makefile.rules <https://github.com/llvm/llvm-project/blob/master/lldb/packages/Python/lldbsuite/test/make/Makefile.rules>`_
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but often it's easier to find an existing ``Makefile`` that does something
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similar to what you want to do.
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Another thing this enables is having different variants for the same test
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case. By default, we run every test for all 3 debug info formats, so once with
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DWARF from the object files, once with gmodules and finally with a dSYM on
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macOS or split DWARF (DWO) on Linux. But there are many more things we can test
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that are orthogonal to the test itself. On GreenDragon we have a matrix bot
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that runs the test suite under different configurations, with older host
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compilers and different DWARF versions.
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As you can imagine, this quickly lead to combinatorial explosion in the number
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of variants. It's very tempting to add more variants because it's an easy way
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to increase test coverage. It doesn't scale. It's easy to set up, but increases
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the runtime of the tests and has a large ongoing cost.
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The key take away is that the different variants don't obviate the need for
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focused tests. So relying on it to test say DWARF5 is a really bad idea.
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Instead you should write tests that check the specific DWARF5 feature, and have
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the variant as a nice-to-have.
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In conclusion, you'll want to opt for an API test to test the API itself or
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when you need the expressivity, either for the test case itself or for the
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program being debugged. The fact that the API tests work with different
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variants mean that more general tests should be API tests, so that they can be
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run against the different variants.
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Running The Tests
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-----------------
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.. note::
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On Windows any invocations of python should be replaced with python_d, the
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debug interpreter, when running the test suite against a debug version of
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LLDB.
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.. note::
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On NetBSD you must export ``LD_LIBRARY_PATH=$PWD/lib`` in your environment.
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This is due to lack of the ``$ORIGIN`` linker feature.
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Running the Full Test Suite
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```````````````````````````
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The easiest way to run the LLDB test suite is to use the ``check-lldb`` build
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target.
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By default, the ``check-lldb`` target builds the test programs with the same
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compiler that was used to build LLDB. To build the tests with a different
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compiler, you can set the ``LLDB_TEST_COMPILER`` CMake variable.
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It is possible to customize the architecture of the test binaries and compiler
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used by appending ``-A`` and ``-C`` options respectively to the CMake variable
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``LLDB_TEST_USER_ARGS``. For example, to test LLDB against 32-bit binaries
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built with a custom version of clang, do:
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::
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$ cmake -DLLDB_TEST_USER_ARGS="-A i386 -C /path/to/custom/clang" -G Ninja
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$ ninja check-lldb
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Note that multiple ``-A`` and ``-C`` flags can be specified to
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``LLDB_TEST_USER_ARGS``.
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Running a Single Test Suite
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```````````````````````````
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Each test suite can be run separately, similar to running the whole test suite
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with ``check-lldb``.
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* Use ``check-lldb-unit`` to run just the unit tests.
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* Use ``check-lldb-api`` to run just the SB API tests.
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* Use ``check-lldb-shell`` to run just the shell tests.
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You can run specific subdirectories by appending the directory name to the
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target. For example, to run all the tests in ``ObjectFile``, you can use the
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target ``check-lldb-shell-objectfile``. However, because the unit tests and API
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tests don't actually live under ``lldb/test``, this convenience is only
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available for the shell tests.
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Running a Single Test
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`````````````````````
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The recommended way to run a single test is by invoking the lit driver with a
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filter. This ensures that the test is run with the same configuration as when
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run as part of a test suite.
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::
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$ ./bin/llvm-lit -sv tools/lldb/test --filter <test>
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Because lit automatically scans a directory for tests, it's also possible to
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pass a subdirectory to run a specific subset of the tests.
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::
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$ ./bin/llvm-lit -sv tools/lldb/test/Shell/Commands/CommandScriptImmediateOutput
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For the SB API tests it is possible to forward arguments to ``dotest.py`` by
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passing ``--param`` to lit and setting a value for ``dotest-args``.
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::
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$ ./bin/llvm-lit -sv tools/lldb/test --param dotest-args='-C gcc'
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Below is an overview of running individual test in the unit and API test suites
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without going through the lit driver.
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Running a Specific Test or Set of Tests: API Tests
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``````````````````````````````````````````````````
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In addition to running all the LLDB test suites with the ``check-lldb`` CMake
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target above, it is possible to run individual LLDB tests. If you have a CMake
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build you can use the ``lldb-dotest`` binary, which is a wrapper around
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``dotest.py`` that passes all the arguments configured by CMake.
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Alternatively, you can use ``dotest.py`` directly, if you want to run a test
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one-off with a different configuration.
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For example, to run the test cases defined in TestInferiorCrashing.py, run:
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::
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$ ./bin/lldb-dotest -p TestInferiorCrashing.py
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::
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$ cd $lldb/test
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$ python dotest.py --executable <path-to-lldb> -p TestInferiorCrashing.py ../packages/Python/lldbsuite/test
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If the test is not specified by name (e.g. if you leave the ``-p`` argument
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off), all tests in that directory will be executed:
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::
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$ ./bin/lldb-dotest functionalities/data-formatter
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::
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$ python dotest.py --executable <path-to-lldb> functionalities/data-formatter
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Many more options that are available. To see a list of all of them, run:
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::
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$ python dotest.py -h
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Running a Specific Test or Set of Tests: Unit Tests
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```````````````````````````````````````````````````
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The unit tests are simple executables, located in the build directory under ``tools/lldb/unittests``.
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To run them, just run the test binary, for example, to run all the Host tests:
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::
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$ ./tools/lldb/unittests/Host/HostTests
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To run a specific test, pass a filter, for example:
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::
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$ ./tools/lldb/unittests/Host/HostTests --gtest_filter=SocketTest.DomainListenConnectAccept
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Running the Test Suite Remotely
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```````````````````````````````
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Running the test-suite remotely is similar to the process of running a local
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test suite, but there are two things to have in mind:
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1. You must have the lldb-server running on the remote system, ready to accept
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multiple connections. For more information on how to setup remote debugging
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see the Remote debugging page.
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2. You must tell the test-suite how to connect to the remote system. This is
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achieved using the ``--platform-name``, ``--platform-url`` and
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``--platform-working-dir`` parameters to ``dotest.py``. These parameters
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correspond to the platform select and platform connect LLDB commands. You
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will usually also need to specify the compiler and architecture for the
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remote system.
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Currently, running the remote test suite is supported only with ``dotest.py`` (or
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dosep.py with a single thread), but we expect this issue to be addressed in the
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near future.
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Running tests in QEMU System Emulation Environment
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``````````````````````````````````````````````````
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QEMU can be used to test LLDB in an emulation environment in the absence of
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actual hardware. `QEMU based testing <https://lldb.llvm.org/use/qemu-testing.html>`_
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page describes how to setup a emulation environment using QEMU helper scripts
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found under llvm-project/lldb/scripts/lldb-test-qemu. These scripts currently
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work with Arm or AArch64, but support for other architectures can be added easily.
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Debugging Test Failures
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-----------------------
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On non-Windows platforms, you can use the ``-d`` option to ``dotest.py`` which
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will cause the script to wait for a while until a debugger is attached.
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Debugging Test Failures on Windows
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``````````````````````````````````
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On Windows, it is strongly recommended to use Python Tools for Visual Studio
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for debugging test failures. It can seamlessly step between native and managed
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code, which is very helpful when you need to step through the test itself, and
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then into the LLDB code that backs the operations the test is performing.
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A quick guide to getting started with PTVS is as follows:
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#. Install PTVS
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#. Create a Visual Studio Project for the Python code.
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#. Go to File -> New -> Project -> Python -> From Existing Python Code.
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#. Choose llvm/tools/lldb as the directory containing the Python code.
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#. 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.
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#. Set test/dotest.py as the startup file
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#. 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.
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#. Go to Tools -> Options -> Python Tools -> Environment Options
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#. Click Add Environment, and enter Python 3.5 Debug for the name. Fill out the values correctly.
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#. Configure the project to use this debug interpreter.
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#. Right click the Project node in Solution Explorer.
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#. In the General tab, Make sure Python 3.5 Debug is the selected Interpreter.
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#. In Debug/Search Paths, enter the path to your ninja/lib/site-packages directory.
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#. In Debug/Environment Variables, enter ``VCINSTALLDIR=C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\``.
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#. 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.)
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#. Set the command line for the test suite to run.
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#. Right click the project in solution explorer and choose the Debug tab.
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#. Enter the arguments to dotest.py.
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#. Example command options:
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::
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--arch=i686
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# Path to debug lldb.exe
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--executable D:/src/llvmbuild/ninja/bin/lldb.exe
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# Directory to store log files
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-s D:/src/llvmbuild/ninja/lldb-test-traces
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-u CXXFLAGS -u CFLAGS
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# If a test crashes, show JIT debugging dialog.
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--enable-crash-dialog
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# Path to release clang.exe
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-C d:\src\llvmbuild\ninja_release\bin\clang.exe
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# Path to the particular test you want to debug.
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-p TestPaths.py
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# Root of test tree
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D:\src\llvm\tools\lldb\packages\Python\lldbsuite\test
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::
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--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
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.. [#] `https://lldb.llvm.org/python_reference/lldb.SBTarget-class.html#BreakpointCreateByName <https://lldb.llvm.org/python_reference/lldb.SBTarget-class.html#BreakpointCreateByName>`_
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