bitcoin/test

This directory contains integration tests that test bitcoind and its utilities in their entirety. It does not contain unit tests, which can be found in /src/test, /src/wallet/test, etc.

This directory contains the following sets of tests:

• fuzz A runner to execute all fuzz targets from /src/test/fuzz.
• functional which test the functionality of bitcoind and bitcoin-qt by interacting with them through the RPC and P2P interfaces.
• lint which perform various static analysis checks.

The fuzz tests, functional tests and lint scripts can be run as explained in the sections below.

Running tests locally

Before tests can be run locally, Bitcoin Core must be built. See the building instructions for help.

The following examples assume that the build directory is named build.

Fuzz tests

See /doc/fuzzing.md

Functional tests

Dependencies and prerequisites

The ZMQ functional test requires a python ZMQ library. To install it:

• on Unix, run sudo apt-get install python3-zmq
• on mac OS, run pip3 install pyzmq

The IPC functional test requires a python IPC library. pip3 install pycapnp may work, but if not, install it from source:

git clone -b v2.2.1 https://github.com/capnproto/pycapnp
pip3 install ./pycapnp

If that does not work, try adding -C force-bundled-libcapnp=True to the pip command. Depending on the system, it may be necessary to install and run in a venv:

python -m venv venv
git clone -b v2.2.1 https://github.com/capnproto/pycapnp
venv/bin/pip3 install ./pycapnp -C force-bundled-libcapnp=True
venv/bin/python3 build/test/functional/interface_ipc.py

The functional tests assume Python UTF-8 Mode, which is the default on most systems. On Windows the PYTHONUTF8 environment variable must be set to 1:

set PYTHONUTF8=1

Running the tests

Individual tests can be run by directly calling the test script, e.g.:

build/test/functional/feature_rbf.py

or can be run through the test_runner harness, eg:

build/test/functional/test_runner.py feature_rbf.py

You can run any combination (incl. duplicates) of tests by calling:

build/test/functional/test_runner.py <testname1> <testname2> <testname3> ...

Wildcard test names can be passed, if the paths are coherent and the test runner is called from a bash shell or similar that does the globbing. For example, to run all the wallet tests:

build/test/functional/test_runner.py test/functional/wallet*
functional/test_runner.py functional/wallet*  # (called from the build/test/ directory)
test_runner.py wallet*  # (called from the build/test/functional/ directory)

but not

build/test/functional/test_runner.py wallet*

Combinations of wildcards can be passed:

build/test/functional/test_runner.py ./test/functional/tool* test/functional/mempool*
test_runner.py tool* mempool*

Run the regression test suite with:

build/test/functional/test_runner.py

Run all possible tests with

build/test/functional/test_runner.py --extended

In order to run backwards compatibility tests, first run:

test/get_previous_releases.py

to download the necessary previous release binaries.

By default, up to 4 tests will be run in parallel by test_runner. To specify how many jobs to run, append --jobs=n

The individual tests and the test_runner harness have many command-line options. Run build/test/functional/test_runner.py -h to see them all.

Speed up test runs with a RAM disk

If you have available RAM on your system you can create a RAM disk to use as the cache and tmp directories for the functional tests in order to speed them up. Speed-up amount varies on each system (and according to your RAM speed and other variables), but a 2-3x speed-up is not uncommon.

Linux

To create a 4 GiB RAM disk at /mnt/tmp/:

sudo mkdir -p /mnt/tmp
sudo mount -t tmpfs -o size=4g tmpfs /mnt/tmp/

Configure the size of the RAM disk using the size= option. The size of the RAM disk needed is relative to the number of concurrent jobs the test suite runs. For example running the test suite with --jobs=100 might need a 4 GiB RAM disk, but running with --jobs=32 will only need a 2.5 GiB RAM disk.

To use, run the test suite specifying the RAM disk as the cachedir and tmpdir:

build/test/functional/test_runner.py --cachedir=/mnt/tmp/cache --tmpdir=/mnt/tmp

Once finished with the tests and the disk, and to free the RAM, simply unmount the disk:

sudo umount /mnt/tmp

macOS

To create a 4 GiB RAM disk named "ramdisk" at /Volumes/ramdisk/:

diskutil erasevolume HFS+ ramdisk $(hdiutil attach -nomount ram://8388608)

Configure the RAM disk size, expressed as the number of blocks, at the end of the command (4096 MiB * 2048 blocks/MiB = 8388608 blocks for 4 GiB). To run the tests using the RAM disk:

build/test/functional/test_runner.py --cachedir=/Volumes/ramdisk/cache --tmpdir=/Volumes/ramdisk/tmp

To unmount:

umount /Volumes/ramdisk

Troubleshooting and debugging test failures

Resource contention

The P2P and RPC ports used by the bitcoind nodes-under-test are chosen to make conflicts with other processes unlikely. However, if there is another bitcoind process running on the system (perhaps from a previous test which hasn't successfully killed all its bitcoind nodes), then there may be a port conflict which will cause the test to fail. It is recommended that you run the tests on a system where no other bitcoind processes are running.

On linux, the test framework will warn if there is another bitcoind process running when the tests are started.

If there are zombie bitcoind processes after test failure, you can kill them by running the following commands. Note that these commands will kill all bitcoind processes running on the system, so should not be used if any non-test bitcoind processes are being run.

killall bitcoind

or

pkill -9 bitcoind

Data directory cache

A pre-mined blockchain with 200 blocks is generated the first time a functional test is run and is stored in build/test/cache. This speeds up test startup times since new blockchains don't need to be generated for each test. However, the cache may get into a bad state, in which case tests will fail. If this happens, remove the cache directory (and make sure bitcoind processes are stopped as above):

rm -rf build/test/cache
killall bitcoind

Test logging

The tests contain logging at five different levels (DEBUG, INFO, WARNING, ERROR and CRITICAL). From within your functional tests you can log to these different levels using the logger included in the test_framework, e.g. self.log.debug(object). By default:

• when run through the test_runner harness, all logs are written to test_framework.log and no logs are output to the console.
• when run directly, all logs are written to test_framework.log and INFO level and above are output to the console.
• when run by our CI (Continuous Integration), no logs are output to the console. However, if a test fails, the test_framework.log and bitcoind debug.logs will all be dumped to the console to help troubleshooting.

These log files can be located under the test data directory (which is always printed in the first line of test output):

• <test data directory>/test_framework.log
• <test data directory>/node<node number>/regtest/debug.log.

The node number identifies the relevant test node, starting from node0, which corresponds to its position in the nodes list of the specific test, e.g. self.nodes[0].

To change the level of logs output to the console, use the -l command line argument.

test_framework.log and bitcoind debug.logs can be combined into a single aggregate log by running the combine_logs.py script. The output can be plain text, colorized text or html. For example:

build/test/functional/combine_logs.py -c <test data directory> | less -r

will pipe the colorized logs from the test into less.

Use --tracerpc to trace out all the RPC calls and responses to the console. For some tests (eg any that use submitblock to submit a full block over RPC), this can result in a lot of screen output.

By default, the test data directory will be deleted after a successful run. Use --nocleanup to leave the test data directory intact. The test data directory is never deleted after a failed test.

Attaching a debugger

A python debugger can be attached to tests at any point. Just add the line:

import pdb; pdb.set_trace()

anywhere in the test. You will then be able to inspect variables, as well as call methods that interact with the bitcoind nodes-under-test.

If further introspection of the bitcoind instances themselves becomes necessary, this can be accomplished by first setting a pdb breakpoint at an appropriate location, running the test to that point, then using gdb (or lldb on macOS) to attach to the process and debug.

For instance, to attach to self.node[1] during a run you can get the pid of the node within pdb.

(pdb) self.node[1].process.pid

Alternatively, you can find the pid by inspecting the temp folder for the specific test you are running. The path to that folder is printed at the beginning of every test run:

2017-06-27 14:13:56.686000 TestFramework (INFO): Initializing test directory /tmp/user/1000/testo9vsdjo3

Use the path to find the pid file in the temp folder:

cat /tmp/user/1000/testo9vsdjo3/node1/regtest/bitcoind.pid

Then you can use the pid to start gdb:

gdb /home/example/bitcoind <pid>

Note: gdb attach step may require ptrace_scope to be modified, or sudo preceding the gdb. See this link for considerations: https://www.kernel.org/doc/Documentation/security/Yama.txt

Often while debugging RPC calls in functional tests, the test might time out before the process can return a response. Use --timeout-factor 0 to disable all RPC timeouts for that particular functional test. Ex: build/test/functional/wallet_hd.py --timeout-factor 0.

Profiling

An easy way to profile node performance during functional tests is provided for Linux platforms using perf.

Perf will sample the running node and will generate profile data in the node's datadir. The profile data can then be presented using perf report or a graphical tool like hotspot.

To generate a profile during test suite runs, use the --perf flag.

To see render the output to text, run

perf report -i /path/to/datadir/send-big-msgs.perf.data.xxxx --stdio | c++filt | less

For ways to generate more granular profiles, see the README in test/functional.

Lint tests

See the README in test/lint.

Writing functional tests

You are encouraged to write functional tests for new or existing features. Further information about the functional test framework and individual tests is found in test/functional.