Tag Archives: DRY

A couple of years back I wrote an article exploring a way to apply Robert C. Martins guidelines on how to create clean functions, taken from his book Clean Code. This created quite a stir on DZone where I also published it.

A comment from Steven Jeuris about a week ago on my blog made me want to revisit the subject again.

Since I wrote the original article I have leant a lot about creating clean functions that does one thing. And as I replied in to his comment I would not design the code in the original article the same way now.

The key to applying the guidelines in the book is pragmatism. There are a number of benefits from working as is suggested there. I will cover some of them below. But it cannot be followed religiously. It is after all software we are creating, not religion.

Steven Jeuris writes, in his blog on the subject, that a perfectly good way to make code in a function readable is to create blocks with comments. This to me is an anti pattern. There are few reasons to ever create blocks in a function, if they are required it generally indicates that the function is to long. Comments are almost always a bad idea. If a comment is required the block should be put within a function bearing a name that explains what the block does.

Steven Jeuris further writes that new small functions litters the name space of the class. I can see where he is coming from but again I have to disagree. If the name space of a class is littered by explanatory function names then it sounds like the class is to big and needs to be refactored. To me it sounds very much like it does more then one thing.

There is also an interesting side effect to creating smaller more atomic functions that does one thing. It is easier to find commonalities between the other, higher level, functions within the same class, or even related classes. This actually makes it easier to keep the code DRY (Don't Repeat Yourself).

Another very important factor to keep in mind when working like this is what patterns the rest of the team are used to. The size of functions and the way to work with them are not a one size fits all. If a team is used to procedural code the step to extract till you drop style functions will be really confusing. If, on the other hand, the team is used to atomic, do one thing only, functions it is hell to work with larger functions littered with comments and artificial blocks.

In any case, I know that if I would do the same exercise to day as I did when writing my original article on the subject it would look different, and the original definitely takes a good thing way to far.

DRY (as in Don't Repeat Yourself) is one of the corner stones of a clean and agile code. This is especially so with the production code but also with test code where finding good abstractions will greatly increase speed and lower maintenance.

When working with python I have found a very nice tool called Clone Digger that helps finding repetitions in code. It is easy to set up and work with and the report is clear and easy to read.

Install it with pip:

pip install clonedigger

When installed it is automatically added to the tool chain. To run it use the following command:

clonedigger package

Package is the directory where the python code is to be found. Clone digger takes a list of packages separated with space. --help will show help on options and configuration.

Clone digger will work it's way through all files in each package and compare them, both within each package and across package listed in the same invocation. If there is code, such as auto generated code, that is not modifiable, it needs to be removed from the packages added in order for the tool not to analyse them.

It will generate a report looking like the one below to output.html (this is configurable).

Clone Digger output

Working with tools like this makes it easy to spot where new abstractions should be added to remove code duplication. Both in test and production code. Where ever repetition is found which does not fill an important documenting function (which is sometimes the case in tests) it is easy to spot where and how a method should be added to pull the functionality together. It will also help in naming such methods. There are of cause some false positives. Especially in the test code where method bodies are often descriptive rather then dry. But even here it helps to find common assertions which can be pulled up and made into new descriptive methods. This becomes especially valuable when working with given-when-then in tests.