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Unit testing in Python with folder watching

Unit testing is good. Running unit tests manually is annoying.

Rspec has a great addition in autospec, which automatically re-runs specs that are changed or have the associated code changed. This model of working has a long history in the ruby community, with autotest.

I wanted a similar thing for python/nose, but hadn’t had much luck.

The solution is py.test, with some plugins added.

This was suggested to me at the Stockholm Python user group, and I am very grateful. My testing life is now much simpler.

To get started, try something like:

$ sudo pip install pytest pytest-xdist pytest-cov

The simple way to get started is to watch your tests folder:

py.test -f test/

Now as you change files, the relevant tests will automatically be re-run. Perfect to display on that second monitor.

As I was working on some legacy code, I was looking to improve the test coverage, so wanted to see how that was going with pytest-cov:

py.test -f --cov package.name test/

Adding the package.name for your main package means you won’t generate coverage for libraries you are using. Makes the output simpler.

Still not quite happy, it wasn’t showing me which lines I needed coverage on. No problem:

py.test -f --cov package.name --cov-report term-missing test/

For comparison, here is how to get similar results from nose, but without watch support:

nosetests test/nosetests --with-coverage --cover-erase --cover-package=package.name test/

One hurdle I had with py.test was working with Twisted based test-cases. You need to ensure that you use a version of py.test later than 2.0. Debian squeeze does not package this by default, so use pip to install.

The annoyance is if you change the code so it doesn’t terminate. This still needs a context switch to kill off the tests and re-run. Keeps me focused on getting it right the first time.

pytest-xdist includes lots of really cool options, such as distribution of tests across multiple cores, versions, hosts and platforms.

I’ve since discovered this comprehensive summary of the options available. I’m happy I found this after selecting py.test, or I’d still be stuck in evaluation mode rather than writing code.

Update: check out sniffer if you want a more generic watcher framework.