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 uses RUN: 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 unittest.

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. Though Shell tests can be run remotely, behavior specific to remote debugging must be tested with API tests instead.

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 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 unittest. 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.

@skipTestIfFn(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 two debug info formats, once with DWARF from the object files and another 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 test variants are most useful when developing a larger feature (e.g. support for a new DWARF version). The test suite contains a large number of fairly generic tests, so running the test suite with the feature enabled is a good way to gain confidence that you haven’t missed an important aspect. However, this genericness makes them poor regression tests. Because it’s not clear what a specific test covers, a random modification to the test case can make it start (or stop) testing a completely different part of your feature. And since these tests tend to look very similar, it’s easy for a simple bug to cause hundreds of tests to fail in the same way.

For this reason, we recommend using test variants only while developing a new feature. This can often be done by running the test suite with different arguments – without any modifications to the code. You can create a focused test for any bug found that way. Often, there will be many tests failing, but a lot of then will have the same root cause. These tests will be easier to debug and will not put undue burden on all other bots and developers.

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.

Guidelines for API tests#

API tests are expected to be fast, reliable and maintainable. To achieve this goal, API tests should conform to the following guidelines in addition to normal good testing practices.

Don’t unnecessarily launch the test executable.

Launching a process and running to a breakpoint can often be the most expensive part of a test and should be avoided if possible. A large part of LLDB’s functionality is available directly after creating an SBTarget of the test executable.

The part of the SB API that can be tested with just a target includes everything that represents information about the executable and its debug information (e.g., SBTarget, SBModule, SBSymbolContext, SBFunction, SBInstruction, SBCompileUnit, etc.). For test executables written in languages with a type system that is mostly defined at compile time (e.g., C and C++) there is also usually no process necessary to test the SBType-related parts of the API. With those languages it’s also possible to test SBValue by running expressions with SBTarget.EvaluateExpression or the expect_expr testing utility.

Functionality that always requires a running process is everything that tests the SBProcess, SBThread, and SBFrame classes. The same is true for tests that exercise breakpoints, watchpoints and sanitizers. Languages such as Objective-C that have a dependency on a runtime environment also always require a running process.

Don’t unnecessarily include system headers in test sources.

Including external headers slows down the compilation of the test executable and it makes reproducing test failures on other operating systems or configurations harder.

Avoid specifying test-specific compiler flags when including system headers.

If a test requires including a system header (e.g., a test for a libc++ formatter includes a libc++ header), try to avoid specifying custom compiler flags if possible. Certain debug information formats such as gmodules use a cache that is shared between all API tests and that contains precompiled system headers. If you add or remove a specific compiler flag in your test (e.g., adding -DFOO to the Makefile or self.build arguments), then the test will not use the shared precompiled header cache and expensively recompile all system headers from scratch. If you depend on a specific compiler flag for the test, you can avoid this issue by either removing all system header includes or decorating the test function with @no_debug_info_test (which will avoid running all debug information variants including gmodules).

Test programs should be kept simple.

Test executables should do the minimum amount of work to bring the process into the state that is required for the test. Simulating a ‘real’ program that actually tries to do some useful task rarely helps with catching bugs and makes the test much harder to debug and maintain. The test programs should always be deterministic (i.e., do not generate and check against random test values).

Identifiers in tests should be simple and descriptive.

Often test programs need to declare functions and classes which require choosing some form of identifier for them. These identifiers should always either be kept simple for small tests (e.g., A, B, …) or have some descriptive name (e.g., ClassWithTailPadding, inlined_func, …). Never choose identifiers that are already used anywhere else in LLVM or other programs (e.g., don’t name a class VirtualFileSystem, a function llvm_unreachable, or a namespace rapidxml) as this will mislead people grep’ing the LLVM repository for those strings.

Prefer LLDB testing utilities over directly working with the SB API.

The lldbutil module and the TestBase class come with a large amount of utility functions that can do common test setup tasks (e.g., starting a test executable and running the process to a breakpoint). Using these functions not only keeps the test shorter and free of duplicated code, but they also follow best test suite practices and usually give much clearer error messages if something goes wrong. The test utilities also contain custom asserts and checks that should be preferably used (e.g. self.assertSuccess).

Prefer calling the SB API over checking command output.

Avoid writing your tests on top of self.expect(...) calls that check the output of LLDB commands and instead try calling into the SB API. Relying on LLDB commands makes changing (and improving) the output/syntax of commands harder and the resulting tests are often prone to accepting incorrect test results. Especially improved error messages that contain more information might cause these self.expect calls to unintentionally find the required substrs. For example, the following self.expect check will unexpectedly pass if it’s ran as the first expression in a test:

self.expect("expr 2 + 2", substrs=["0"])

When running the same command in LLDB the reason for the unexpected success is that ‘0’ is found in the name of the implicitly created result variable:

(lldb) expr 2 + 2
(int) $0 = 4
       ^ The '0' substring is found here.

A better way to write the test above would be using LLDB’s testing function expect_expr will only pass if the expression produces a value of 0:

self.expect_expr("2 + 2", result_value="0")
Prefer using specific asserts over the generic assertTrue/assertFalse..

The self.assertTrue/self.assertFalse functions should always be your last option as they give non-descriptive error messages. The test class has several expressive asserts such as self.assertIn that automatically generate an explanation how the received values differ from the expected ones. Check the documentation of Python’s unittest module to see what asserts are available. LLDB also has a few custom asserts that are tailored to our own data types.

Assert

Description

assertSuccess

Assert that an lldb.SBError is in the “success” state.

assertState

Assert that two states (lldb.eState*) are equal.

assertStopReason

Assert that two stop reasons (lldb.eStopReason*) are equal.

If you can’t find a specific assert that fits your needs and you fall back to a generic assert, make sure you put useful information into the assert’s msg argument that helps explain the failure.

# Bad. Will print a generic error such as 'False is not True'.
self.assertTrue(expected_string in list_of_results)
# Good. Will print expected_string and the contents of list_of_results.
self.assertIn(expected_string, list_of_results)

Do not use hard-coded line numbers in your test case.

Instead, try to tag the line with some distinguishing pattern, and use the function line_number() defined in lldbtest.py which takes filename and string_to_match as arguments and returns the line number.

As an example, take a look at test/API/functionalities/breakpoint/breakpoint_conditions/main.c which has these two lines:

return c(val); // Find the line number of c's parent call here.

and

return val + 3; // Find the line number of function "c" here.

The Python test case TestBreakpointConditions.py uses the comment strings to find the line numbers during setUp(self) and use them later on to verify that the correct breakpoint is being stopped on and that its parent frame also has the correct line number as intended through the breakpoint condition.

Take advantage of the unittest framework’s decorator features.

These features can be use to properly mark your test class or method for platform-specific tests, compiler specific, version specific.

As an example, take a look at test/API/lang/c/forward/TestForwardDeclaration.py which has these lines:

@no_debug_info_test
@skipIfDarwin
@skipIf(compiler=no_match("clang"))
@skipIf(compiler_version=["<", "8.0"])
@expectedFailureAll(oslist=["windows"])
def test_debug_names(self):
    """Test that we are able to find complete types when using DWARF v5
    accelerator tables"""
    self.do_test(dict(CFLAGS_EXTRAS="-gdwarf-5 -gpubnames"))

This tells the test harness that unless we are running “linux” and clang version equal & above 8.0, the test should be skipped.

Class-wise cleanup after yourself.

TestBase.tearDownClass(cls) provides a mechanism to invoke the platform-specific cleanup after finishing with a test class. A test class can have more than one test methods, so the tearDownClass(cls) method gets run after all the test methods have been executed by the test harness.

The default cleanup action performed by the packages/Python/lldbsuite/test/lldbtest.py module invokes the “make clean” os command.

If this default cleanup is not enough, individual class can provide an extra cleanup hook with a class method named classCleanup , for example, in test/API/terminal/TestSTTYBeforeAndAfter.py:

@classmethod
def classCleanup(cls):
    """Cleanup the test byproducts."""
    cls.RemoveTempFile("child_send1.txt")

The ‘child_send1.txt’ file gets generated during the test run, so it makes sense to explicitly spell out the action in the same TestSTTYBeforeAndAfter.py file to do the cleanup instead of artificially adding it as part of the default cleanup action which serves to cleanup those intermediate and a.out files.

CI#

LLVM Buildbot is the place where volunteers provide machines for building and testing. Everyone can add a buildbot for LLDB.

An overview of all LLDB builders can be found here:

https://lab.llvm.org/buildbot/#/builders?tags=lldb

Building and testing for macOS uses a different platform called GreenDragon. It has a dedicated tab for LLDB: https://green.lab.llvm.org/job/llvm.org/view/LLDB/

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.

$ ninja check-lldb

Changing Test Suite Options#

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.

You can also add to the test runner options by setting the LLDB_TEST_USER_ARGS CMake variable. This variable uses ; to separate items which must be separate parts of the runner’s command line.

It is possible to customize the architecture of the test binaries and compiler used by appending -A and -C options respectively. 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.

If you want to change the LLDB settings that tests run with then you can set the --setting option of the test runner via this same variable. For example --setting;target.disable-aslr=true.

For a full list of test runner options, see <build-dir>/bin/lldb-dotest --help.

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 <llvm-project-root>/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 <llvm-project-root>/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 <llvm-project-root>/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:

  1. 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.

  2. You must tell the test-suite how to connect to the remote system. This is achieved using the LLDB_TEST_PLATFORM_URL, LLDB_TEST_PLATFORM_WORKING_DIR flags to cmake, and --platform-name parameter to dotest.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.

  3. Remote Shell tests execution is currently supported only for Linux target platform. It’s triggered when LLDB_TEST_SYSROOT is provided for building test sources. It can be disabled by setting LLDB_TEST_SHELL_DISABLE_REMOTE=On. Shell tests are not guaranteed to pass against remote target if the compiler being used is other than Clang.

Running tests in QEMU System Emulation Environment#

QEMU can be used to test LLDB in an emulation environment in the absence of actual hardware. Testing LLDB using QEMU describes how to setup an emulation environment using QEMU helper scripts found in llvm-project/lldb/scripts/lldb-test-qemu. These scripts currently work with Arm or AArch64, but support for other architectures can be added easily.

Debugging Test Failures#

On non-Windows platforms, you can use the -d option to dotest.py which will cause the script to print out the pid of the test and wait for a while until a debugger is attached. Then run lldb -p <pid> to attach.

To instead debug a test’s python source, edit the test and insert import pdb; pdb.set_trace() or breakpoint() (Python 3 only) at the point you want to start debugging. The breakpoint() command can be used for any LLDB Python script, not just for API tests.

In addition to pdb’s debugging facilities, lldb commands can be executed with the help of a pdb alias. For example lldb bt and lldb v some_var. Add this line to your ~/.pdbrc:

alias lldb self.dbg.HandleCommand("%*")

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:

  1. Install PTVS

  2. Create a Visual Studio Project for the Python code.

    1. Go to File -> New -> Project -> Python -> From Existing Python Code.

    2. Choose llvm/tools/lldb as the directory containing the Python code.

    3. 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.

  3. Set test/dotest.py as the startup file

  4. 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.

    1. Go to Tools -> Options -> Python Tools -> Environment Options

    2. Click Add Environment, and enter Python 3.5 Debug for the name. Fill out the values correctly.

  5. Configure the project to use this debug interpreter.

    1. Right click the Project node in Solution Explorer.

    2. In the General tab, Make sure Python 3.5 Debug is the selected Interpreter.

    3. In Debug/Search Paths, enter the path to your ninja/lib/site-packages directory.

    4. In Debug/Environment Variables, enter VCINSTALLDIR=C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\VC\.

    5. 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.)

  6. Set the command line for the test suite to run.

    1. Right click the project in solution explorer and choose the Debug tab.

    2. Enter the arguments to dotest.py.

    3. 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