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See CONTRIBUTING.rst for details: https://github.com/nexB/deltacode/blob/develop/CONTRIBUTING.rst
Source code is in src/. Tests are in tests/.
Each test script is named test_XXXX and while we love to use py.test as a test runner, most tests have no dependencies on py.test, only on the unittest module (with the exception of some command line tests that depend on pytest monkeypatching capabilities.
When source or tests need data files, we store these in a data subdirectory.
We use PEP8 conventions with a relaxed line length that can be up to 90'ish characters long when needed to keep the code clear and readable.
We store pre-built bundled native binaries in bin/ sub-directories of each src/ packages. These binaries are organized by OS and architecture. This ensure that DeltaCode works out of the box either using a checkout or a download, without needing a compiler and toolchain to be installed.
We store bundled thirdparty components and libraries in the thirdparty directory. Python libraries are stored as wheels, eventually pre-built if the corresponding wheel is not available in the Pypi repository.
Some of these components may be advanced builds with bug fixes or advanced patches.
We write tests, a lot of tests, thousands of tests. Several tests are data-driven and use data files as test input and sometimes data files as test expectation (in this case using either JSON or YAML files). The tests should pass on Linux 64 bits, Windows 32 and 64 bits and on MacOSX 10.6.8 and up. We maintain two CI loops with Travis (Linux) at https://travis-ci.org/nexB/deltacode and Appveyor (Windows) at https://ci.appveyor.com/project/nexB/deltacode
When finding bugs or adding new features, we add tests. See existing test code for examples.
DeltaCode comes with over 130 unit tests to ensure detection accuracy and stability across Linux, Windows and macOS OSes: we kinda love tests, do we?
We use pytest to run the tests: call the py.test script to run the whole test suite. This is installed by pytest which is bundled with a DeltaCode checkout and installed when you run ./configure).
If you are running from a fresh git clone and you run ./configure and then source bin/activate the py.test command will be available in your path.
Alternatively, if you have already configured but are not in an activated "virtualenv" the py.test command is available under <root of your checkout>/bin/py.test
(Note: paths here are for POSIX, but mostly the same applies to Windows)
If you have a multiprocessor machine you might want to run the tests in parallel (and faster)
For instance: py.test -n4 runs the tests on 4 CPUs.
We typically run the tests in verbose mode with py.test -vvs -n4
See also https://docs.pytest.org for details or use the py.test -h command to show the many other options available.
One useful option is to run a select subset of the test functions matching a pattern with the -k option for instance: py.test -vvs -k tcpdump would only run test functions that contain the string "tcpdump" in their name or their class name or module name.
Another useful option after a test run with some failures is to re-run only the failed tests with the --lf option for instance: py.test -vvs --lf would only run only test functions that failed in the previous run.
Alternatively, if you want to run a single test(let's assume the test is in the file test_cli.py having the module as TestCLI and the test function as test_help) you can use
py.test tests/test_cli.py::TestCLI::test_help
DeltaCode use the configure and configure.bat (and etc/configure.py behind the scenes) scripts to install a virtualenv, install required packaged dependencies as pip requirements and more configure tasks such that DeltaCode can be installed in a self-contained way with no network connectivity required.
In a somewhat unconventional way, all the required libraries are bundled aka. copied in the repo itself in the thirdparty/ directory. If DeltaCode were only a library it would not make sense. But its is first an application and having a well defined frozen set of dependent packages is important for an app.
The benefit of this approach (combined with the configure script) means that a mere checkout of the repository contains everything needed to run DeltaCode except for a Python interpreter.
(Coming Soon) DeltaCode can be used alright as a Python library and is available as as a Python wheel in Pypi and installed with pip install deltacode.