Hiding Complexity vs. Too Many Layers
If you’ve ever tried TDD there is a decent chance you’ve written some code like this:
from mock import patch @patch('foo.uploader.upload_client') def test_upload_foo(upload_client): do_upload() upload_client.upload.assert_called_with(new_filename())
In this example, what is happening is we are testing some code that uploads a file somewhere like S3. We patch the actual upload layer to make sure we don’t have to upload anything. We then are asserting that we are uploading the file using the right filename, which is the result of the new_filename function.
The code might look something like this:
from mypkg.uploader import upload_client def new_filename(): return some_hash() + request.path def do_upload(): upload_client.upload(new_filename())
The nice thing about this code it is pretty reasonable to test. But, in this simplified state, it doesn’t reflect what happens when you have a more complex situation with multiple layers.
For example, here is an object that creates a gzipped CSV writer on some parameters and the current time.
class Foo(object): basedir = '/' def __init__(self, bar, baz, now=None): self.bar = bar self.baz = baz self._now = now self._file_handle = None @property def now(self): if not self._now: self._now = datetime.now().strftime('%Y-%m-%d') return self._now def fname(self): return '%s.gz' % os.path.join(self.basedir, self.now, self.bar, self.baz) @property def file_handle(self): if not self._file_handle: self._file_handle = gzip.open(self.fname()) return self._file_handle def writer(self): return csv.writer(self.file_handle)
The essence of this functionality could all be condensed down to a single method:
def get_writer(self): now = self._now if not now: now = datetimetime.now().strftime('%Y-%m-%d') fname = '%s.gz' % os.path.join(self.basedir, now, self.bar, self.baz) # NOTE: We have to keep this handle around to close it and # actually save the data. self.file_handle = gzip.open(fname) return csv.writer(self.file_handle)
The single method is pretty easy to understand, but testing becomes more difficult.
Even though the code is relatively easy to read, I believe it is better to lean towards the more testable code and I’ll tell you why.
Tests Automate Understanding
The goal of readable code and tests is to help those that have to work on the code after you’ve moved on. This person could be you! The code you pushed might have seemed perfectly readable when you originally sent it upstream. Unfortunately, that readability can only measured by the reader. The developer might be new to the project, new to the programming language or, conversely, be an author that predates you! In each of these cases, your perspective on what is easy to understand is rarely going to be nearly as clear to the next developer reading your code.
Tests on the other hand provide the next developer with confidence because they have an automated platform on which to build. Rather than simply reading the code in order to gain understanding, the next developer can play with it and confirm his or her understanding authoritatively. In this way, tests automate your understanding of the code.
Be Cautious of Layers!
Even though hiding complexity by way of layers makes things easier to test and you can automate understanding, layers still present a difficult cognitive load. Nesting objects in order to hide complexity can often become difficult to keep track of, especially when you are in a dynamic language such as Python. In static languages like Java, you have the ability to create tools to help navigate the layers of complexity. Often times in dynamic languages, similar tools are not the norm.
Obviously, there are no hard and fast rules. The best course of action is to try and find a balance. We have rough rules of thumb that help us make sure our code is somewhat readable. It is a good idea to apply similar rules to your tests. If you find that testing some code, that may be reasonably easy to read, is difficult to confirm an isolated detail, then it is probably worth creating a test and factoring out that code. The same goes for writing tons of tests to cover all the code paths.
About the Example
I came up with the example because it was some actual code I had to write. I found that I wanted to be able to test each bit separately. I had a base class that would create the file handles, but the file naming was different depending on the specific class that was inherited. By breaking out the naming patterns I was able to easily test the naming and fix the naming bugs I ran into easily. What’s more, it gave me confidence when I needed to use those file names later and wanted to be sure they were correct. I didn’t have rewrite any code that created the names because there was an obvious property that was tested.
It did make the code slightly more ugly. But, I was willing to accept that ugliness because I had tests that made sure when someone else needed to touch the code, they would have the same guarantees that I found helpful.
Test are NOT Documentation
Lastly, tests are not a replacement for readable code, docs or comments. Code is meant for computers to read and understand, not people. Therefore it is in our best interest to take our surrounding tools and use them to the best of our abilities in order to convey as clearly as possible what the computer will be doing with our text. Test offer a way to automate understanding. Test are not a replacement for understanding.
Finally, it should be clear that my preference for tests and more layers is because I value maintainable code. My definition of maintainable code is defined by years (5-10) and updated by teams of developers. In other words, my assumption is that maintenance of the code is, by far, the largest cost. Other projects don’t have the same requirements, in which case, well commented code with less isolated tests may work just fine.