Testing

Tests are run with: make check [flags] where the pertinent flags are:

VALGRIND=[0|1]  - detects memory leaks during test execution but adds a significant delay
PYTEST_PAR=n    - runs pytests in parallel

A modern desktop can build and run through all the tests in a couple of minutes with:

make -j12 full-check PYTEST_PAR=24 VALGRIND=0

Adjust -j and PYTEST_PAR accordingly for your hardware.

There are four kinds of tests:

• source tests - run by make check-source, looks for whitespace, header order, and checks formatted quotes from BOLTs if BOLTDIR exists.

• unit tests - standalone programs that can be run individually. You can also run all of the unit tests with make check-units. They are run-*.c files in test/ subdirectories used to test routines inside C source files.

  You should insert the lines when implementing a unit test:

  /* AUTOGENERATED MOCKS START */

  /* AUTOGENERATED MOCKS END */

  and make update-mocks will automatically generate stub functions which will allow you to link (and conveniently crash if they're called).

• blackbox tests - These tests setup a mini-regtest environment and test lightningd as a whole. They can be run individually:

  PYTHONPATH=contrib/pyln-client:contrib/pyln-testing:contrib/pyln-proto:contrib/pyln-grpc-proto py.test -v tests/

  You can also append -k TESTNAME to run a single test. Environment variables DEBUG_SUBD=[<path>:]<subdaemon> (where path must match the end of the lightning daemon path, for matching only one of several lightningd instances) and TIMEOUT=<seconds> can be useful for debugging subdaemons on individual tests, and DEBUG_LIGHTNINGD for attaching a debugger to each lightningd instance created.

  Alternatively, to run a specific test via the Makefile, you can specify the test by setting the environment variable PYTEST_TESTS:

  PYTEST_TESTS="tests/test_askrene.py::test_layers" make pytest

• pylightning tests - will check contrib pylightning for codestyle and run the tests in contrib/pylightning/tests afterwards:

  make check-python

Our Github Actions instance (see .github/workflows/*.yml) runs all these for each pull request.

Additional Environment Variables

EXPERIMENTAL_DUAL_FUND=[0|1]          - Enable dual-funding tests.
EXPERIMENTAL_SPLICING=[0|1]           - Enable splicing tests.
TEST_CHECK_DBSTMTS=[0|1]              - When running blackbox tests, this will
                                        load a plugin that logs all compiled
                                        and expanded database statements.
                                        Note: Only SQLite3.
TEST_DB_PROVIDER=[sqlite3|postgres]   - Selects the database to use when running
                                        blackbox tests.
TEST_DEBUG=[0|1]                      - Enable additional debug logging output
                                        during tests.
TEST_NETWORK=[regtest|liquid-regtest] - Select the test network to use. Default is
                                        to 'regtest'.
TIMEOUT                               - Override the default timeout value for
                                        API calls.
PYTEST_PAR=[1-n]                      - Number of processes to use when running
                                        the blackbox the tests in parallel.
PYTEST_TESTS="tests/"                 - Target a specific set of blackbox tests
                                        when running 'make pytest'. Pass a string
                                        of Pytest test targets.
SLOW_MACHINE=[0|1]                    - Set sensible defaults for running tests
                                        in resource-constrained environments.
VALGRIND=[0|1]                        - Run the tests with Valgrind.
GENERATE_EXAMPLES=[0|1]               - Generate examples for RPC documentation.

Troubleshooting

Valgrind complains about code we don't control

Sometimes valgrind will complain about code we do not control ourselves, either because it's in a library we use or it's a false positive. There are generally three ways to address these issues (in descending order of preference):

1. Add a suppression for the one specific call that is causing the issue. Upon finding an issue valgrind is instructed in the testing framework to print filters that'd match the issue. These can be added to the suppressions file under tests/valgrind-suppressions.txt in order to explicitly skip reporting these in future. This is preferred over the other solutions since it only disables reporting selectively for things that were manually checked. See the valgrind docs for details.
2. Add the process that valgrind is complaining about to the --trace-children-skip argument in pyln-testing. This is used in cases of full binaries not being under our control, such as the python3 interpreter used in tests that run plugins. Do not use this for binaries that are compiled from our code, as it tends to mask real issues.
3. Mark the test as skipped if running under valgrind. It's mostly used to skip tests that otherwise would take considerably too long to test on CI. We discourage this for suppressions, since it is a very blunt tool.

Fuzz testing

Core Lightning currently supports coverage-guided fuzz testing using LLVM's libfuzzer when built with clang.

The goal of fuzzing is to generate mutated -and often unexpected- inputs (seeds) to pass to (parts of) a program (target) in order to make sure the codepaths used:

• do not crash
• are valid (if combined with sanitizers)
  The generated seeds can be stored and form a corpus, which we try to optimise (don't store two seeds that lead to the same codepath).

For more info about fuzzing see here, and for more about libfuzzer in particular see here.

Build the fuzz targets

In order to build the Core Lightning binaries with code coverage you will need a recent clang. The more recent the compiler version the better.

Then you'll need to enable support at configuration time. You likely want to enable a few sanitizers for bug detections as well as experimental features for an extended coverage (not required though).

./configure --enable-address-sanitizer --enable-ub-sanitizer --enable-fuzzing --disable-valgrind CC=clang && make

The targets will be built in tests/fuzz/ as fuzz- binaries, with their best known seed corpora stored in tests/fuzz/corpora/.

You can run the fuzz targets on their seed corpora to check for regressions:

make check-fuzz

Run one or more target(s)

You can run each target independently. Pass -help=1 to see available options, for example:

./tests/fuzz/fuzz-addr -help=1

Otherwise, you can use the Python runner to either run the targets against a given seed corpus:

./tests/fuzz/run.py fuzz_corpus -j2

Or extend this corpus:

./tests/fuzz/run.py fuzz_corpus -j2 --generate --runs 12345

The latter will run all targets two by two 12345 times.

If you want to contribute new seeds, be sure to merge your corpus with the main one:

./tests/fuzz/run.py my_locally_extended_fuzz_corpus -j2 --generate --runs 12345
./tests/fuzz/run.py tests/fuzz/corpora --merge_dir my_locally_extended_fuzz_corpus

Improve seed corpora

If you find coverage increasing inputs while fuzzing, please create a pull request to add them into tests/fuzz/corpora. Be sure to minimize any additions to the corpora first.

Example

Here's an example workflow to contribute new inputs for the fuzz-addr target.

Create a directory for newly found corpus inputs and begin fuzzing:

mkdir -p local_corpora/fuzz-addr
./tests/fuzz/fuzz-addr -jobs=4 local_corpora/fuzz-addr tests/fuzz/corpora/fuzz-addr/

After some time, libFuzzer may find some potential coverage increasing inputs and save them in local_corpora/fuzz-addr. We can then merge them into the seed corpora in tests/fuzz/corpora:

./tests/fuzz/run.py tests/fuzz/corpora --merge_dir local_corpora

This will copy over any inputs that improve the coverage of the existing corpus. If any new inputs were added, create a pull request to improve the upstream seed corpus:

git add tests/fuzz/corpora/fuzz-addr/*
git commit
...

Write new fuzzing targets

In order to write a new target:

• include the libfuzz.h header
• fill two functions: init() for static stuff and run() which will be called repeatedly with mutated data.
• read about what makes a good fuzz target.

A simple example is fuzz-addr. It setups the chainparams and context (wally, tmpctx, ..) in init() then bruteforces the bech32 encoder in run().