At least Clang 7.0 and Clant-tidy 7.0 is required to run the tests.
Before running the tests the CodeChecker package needs to be built!
# Build the package
make package
Every test target has an *in_env
version (Eg. make test_in_env
) which
automatically creates and sources a virtualenv for the tests.
cmd | |
---|---|
make test |
run all tests (unit and functional for the analyzer and the web) |
make test_in_env |
run all tests (unit and functional), automatically setup and source a virtualenv |
make test_unit |
unittests for the analyzer and the web |
make test_functional |
functional tests for the analyzer and the web (SQLite and PostgreSQL) |
make test_web_sqlite |
functional test for the web part (SQLite) |
make TEST="tests/functional/cmdline" test_web_feature |
run only a specific web test (SQLite) |
make TEST="tests/functional/analyze" test_analyzer_feature |
run only a specific analyzer test |
With the CC_TEST_WORKSPACE
environment variable the root directory for the
tests can be changed. With this environment variable multiple test types
(sqlite, postgresql) can be run parallel. Every test should create the
temporary directories under the given root directory.
Sqlite tests with a changed workspace root can be run like this:
CC_TEST_WORKSPACE=/tmp/sqlite_test_workspace make -C web test_sqlite
make test_clean
At least one of the database drivers needs to be available to use the
PostgreSQL database backend.
psycopg2
is used by default if not found pg8000
is used.
pytest.ini
configuration file in the repository root is used to configure running the tests:
Further configuration options can be found here
Pytest configuration.
You can specify additional pytest arguments to the make targets by using the
environmental variable EXTRA_PYTEST_ARGS
:
EXTRA_PYTEST_ARGS='-k test_source_suppress_export' TEST=tests/functional/suppress make test_analyzer_feature
The above example displays how you can select a specific testcase via the -k
option passed through EXTRA_PYTEST_ARGS
, within a specific testfile via
TEST
, in the analyzer library via test_analyzer_feature
.
Test running virtual environment is automatically created and sourced by the
make test*
commands.
Create a python virtualenv for development:
make venv_dev
Use the add_new_unit_test.py script to generate new test files.
add_new_unit_test.py module_name
Add new tests for the created tests/unit/test_module_name.py
file and run
the unit tests:
make test_unit
Use the add_new_func_test.py script to generate new test files.
add_new_func_test.py newfeature
From the repository root run the new test template (should pass):
make TEST="tests/functional/mynewfeature" run_test
If it is possible generate and add the report files used during the test to the repository. It will speed up the tests because no analysis is needed, during each test and the results will not depend on the analyzer version in the test environment.
Make sure to generate plist reports where the source file names do not contain the source file path only the source file name because the reports will be moved during the tests to temporary directories.
A simple test c/cpp should look like this:
Makefile # build and analyze targets for the test source files
main.cpp # cpp file with errors in it
reports/base/*.plist # first report set used for the tests
reports/new/*.plist # second report set used for the tests
In each functional test mynewfeature/__init__.py
is doing the functional test
setup:
- exporting test related data to the test directories
- analyzing a test project (multiple times if needed)
- start a server connected to a specific database (should be stopped in teardown)
- prepare and cleanup the test workspace
- ...
The actual test cases go to mynewfeature/test_mynewfeature.py
file.
The setup part of these test read up the generated test configuration file by __init__.py
.
Test cases should only use test related configuration values ONLY
from the configuration file
generated by the __init__.py
.
The tests should ONLY
modify the files in the workspace provided for them.
Test cases can:
- rerun the analysis
- connect to the server started by the
__init__.py
- run command line commands
- modify configuration files
- ...