Tag: clean code

Clean Code – Functions – Revisited

November 18th, 2011 · · Clean code No comments - Tags: ,

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.

Valuable returns for writing Clean Code

November 2nd, 2011 · · Clean code No comments - Tags:

In the project I am currently working on I have a lot of code to write. It’s a green field project delivering a new platform and I have no frameworks to borrow from or extend. So I have to write the code for each single feature by hand.

I am not complaining. I like writing code. But it takes time, especially since it’s only me on the project at the moment.

Thankfully it’s all written in Python so it’s not to verbose, though even Python is verbose when you have to write this amount of repetitive code.

Which is how we come to the value of clean code. The code is slowly transforming by refactoring and transformations. The effect of this is that there is no repetition in production code, it is completely DRY. In tests there is little repetition, this to make the tests document the production code.

The effect of this is that although the upfront cost, before any abstractions had been created, was slightly higher the cost of adding new features now is very small. To create a function that initially required 40 lines of code excluding tests now requires 5 to 10 lines. The test abstractions are so easy to use that it takes next to no time even though some repetition (sequentially calling the same methods in each test method to explain the steps required to use the function) is needed.

The conclusion to this is that the returns of clean code is rather immediate. Many seems to think that the returns are slow and far in the future. I know this to be the opposite. The returns are almost immediate and are currently cutting development time radically. And that is on a code base which has only a couple of thousand lines of production code.

Test coverage statistics with nosetests

October 26th, 2011 · · Python No comments - Tags: , , , , , ,

I use nosetests when running my python tests. It is a really neat little tool that will automatically discover and run all tests in the project with one single command line command – nosetests.

But it has more neatness then this. With a simple option it will also print test coverage for the project: --with-coverage, and if coverage should be calculated base on specific packages then use --cover-package which takes a list of packages to calculate coverage on. This is useful when there are library packages for a virtual env or similar within the same directory as you run nose.

I don’t like to leave lose ends so to ensure that there are no lingering compiled *.pyc files sitting around I run the following script before commit:

#!/bin/sh
#find all .pyc files and remove them
find  ...  -name '*.pyc' -exec rm {} \;
#Execute tests
nosetests --with-coverage --cover-package=

Where <source-dir> should be replaced with directories where source files are. This is a space delimited list. And <package> is replaced with packages which should have coverage statistics calculated.

Keeping your Python code DRY using Clone Digger

October 21st, 2011 · · Python No comments - Tags: , ,

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.

Choosing between table mappers or DeclerativeBase when working with SQLAlchemy

October 18th, 2011 · · Python No comments - Tags: , , , , ,

I have recently been looking how to create a clean Domain Driven Design architecture using SQLAlchemy and specifically how to map domain classes to tables. This post will discuss the two techniques available and why to choose one over the other.

Lets start with looking at the two strategies available. SQLAlchemy offers a Data Mapper out of the box. It is also possible to use an Active Record approach by using the Declarative Base ORM extension.

The Active Record approach is a very attractive way of working. It adds metadata to the domain objects that tells the engine how to save them to the database. It’s quick and easy to use and avoids extra classes or configurations that handles the database mapping. It’s a perfect fit for most web applications. The main drawback is that storage logic is tightly coupled with the domain objects. This can make the domain difficult to work with over time. It also means that any changes required in storage logic will force changes into the domain breaking the Single Responsibility Principle. Active Record will also prove very costly if the project finds that a change of storage technology would be beneficial. A not to unlikely event with such a variation in industry strength storage technologies available.

Data Mappers provides high decoupling by keeping all meta data for mapping objects to tables separate from the domain. The mapping can be placed in its own module which makes it easy to divide the domain into coherent modules with no need to cater for storage concerns. It also makes it easy to model more advanced relational concepts in a large database. Since it allows for complete separation of concerns for domain and data access it is also easy to change or swap out the data storage technology completely without any needs to change the domain. It does however increase the complexity of the application since it adds an extra module/package.

If the project in question is going to use a clean implementation of Domain Driven Design only the Data Mapper works. It is also the only possible choice if it is likely that the project will be changing storage technology.

If, however, the project is a traditional web application with a domain model that is simple or close to non-existing then Active Record is a good choice. It will provide more speed of development. Making a clear distinction between domain logic tests and data access tests will make it easier to later extract data access out into Data Mappers if needed.