Blog article

Life doesn’t stand still.

Neither can the code that we write. In order to keep up with today’s near-frantic pace of change, we need to make every effort to write code that’s as loose—as flexible—as possible. Otherwise we may find our code quickly becoming outdated, or too brittle to fix, and may ultimately be left behind in the mad dash toward the future.

This quote, taken from the highly regarded The Pragmatic Programmer book, depicts beautifully an ongoing, everyday challenge software developers face: writing flexible enough code which smoothly adapts to change.

What Andy Hunt and Dave Thomas are telling us is that software we develop, just as much as the world we live in, resides in a constant state of evolution. Despite our best efforts, requirements will change, and we’ll be better off if our solution is prepared for it.

In our quest to write flexible code, three concepts arise which I think help tremendously in achieving our ultimate goal, or at least they clear the path towards the consummation of this never ending struggle.


Every piece of knowledge must have a single, unambiguous, authoritative representation within a system.

The acronym DRY stands for “Don’t Repeat Yourself”, that is, not doing what I did on this section’s title. When crafting software solutions, this translates to ACD: “Avoid Code Duplication!”.

Code repetition aggravates the maintainability nightmare software developers are generally used to. Incoming changes affecting a duplicated piece of knowledge in our application would need to be taken care of in more than one place of our codebase. On complex systems, making these changes effectively constitutes a daunting task, which may lead to unexpected and hard to find bugs.

This is why keeping your code DRY can help tremendously in creating flexible systems. When a shift in requirements occurs, or a bug is reported in production, there will only be one place where to make the change.

DRY up in face of the inevitable. As Sam Cook said, a change is gonna come.

At this point, you might be asking yourself:

Not so shy reader:

Alright! But, what does being shy have to do with all of this?

Well, what I meant is that, while being shy in life might sometimes be seen as a disadvantage, writing shy code might be in your best interest.

Not so shy reader:

Right… So what is shy code?

As with shy persons, we can think of shy code as code which keeps to itself, sharing little information about its responsibilities to others, as well as not poking its nose into others’ affairs. Real life examples tend to make things clearer, so let’s go that way.

Tell, don’t ask!

When pizza boy arrives, after handing you that delicious pizza, he will proceed to charge you for it. First, he will reach into your pockets and get hold of your wallet, then he’ll grab the first credit card he finds in it, and finally, he will make the desired payment:


Not so shy reader:

Mmmm… is it supposed to go that way?

Of course…

Well, it’s not.

Most often, delivery guy will simply tell you to pay for the pizza:


It makes no sense for pizza man to keep asking questions about your concerns, poking nose into your own affairs. By simply telling the customer to make the payment, he leaves us with the responsibility of doing it, and abstracts himself of unnecessary details.

Following this same rule, we can build software modules which depend upon interfaces that abstract nasty implementation details from the outside world. This means that — from a modules point of view — changes which might occur in other modules will be less likely to break something in this one, as well as changes to this module not propagating bugs to the whole system.

Back to our real life example. The asking snippet presents the problem that, if tomorrow I decide to keep my credit cards in a drawer instead of my wallet, PizzaBoy module would break. Tell snippet on the other hand, ensures that this modification to Customer internals is transparent to PizzaBoy.

Not so shy reader:

This is kinda’ cool, but we, software developers sometimes need some kind of rule which helps us in spotting those places in our codebase where we are becoming more of the asking guy rather than the telling one…

I thought you would say that. Turns out there is a law for this…

Demeter to the rescue

Only talk to your immediate friends

In 1987, fruit of the work done on The Demeter Project, Ian Holland proposed a style rule for designing object-oriented systems. It was named ”The Law of Demeter”. What this rule defines, is a set of guidelines which restrict the amount of objects to which you may send messages to.

I like Brad Appleton’s phrasing of the style guideline:

A method “M” of an object “O” should invoke only the methods of the following kinds of objects:

  1. itself
  2. its parameters
  3. any objects it creates/instantiates
  4. its direct component objects

Let’s make it clearer with an example:

Choo choo! Here comes the train wreck!

  def make_garden_pretty(gardener)

This small snippet, presents a method which violates Demeter’s law. On the quest to make our garden beautiful, we are taking our chain of calls one step too far. Can you hear the train wreck coming?

Demeter states on it’s second rule that a method can invoke functions of the kind of object it receives as a parameter. However, upon the advent of a new object type, call chaining should stop. Our gardening example shows we’re calling a method defined on the Gardener class, tools, which corresponds to the received parameter’s type. However, chaining continues by invoking the garden_scissors method of the Tool module. Additionally, we’re chaining the cut call, belonging to the Task module. Can you notice the problems this could lead to?

Besides being kind of rude, this presents a bigger headache, very similar to the one depicted on the PizzaBoy examples. We are coupling ourselves to the outside world’s concerns, creating a dependency which could ruin things for us in the future. Let me clarify this. Say some time from now, our gardener decides to stop using scissors in his daily work, and start using a lawn mower, because he determines that is the most effective way to make your garden shine. A change in his set of tools has developed. Scissors no longer exist in the gardener’s toolbox.

Knowing too much about how to make your garden look pretty led to you breaking in response to a change in the Gardener’s implementation. In contrast to our PizzaBoy solution, we’re not being shy here. In fact, we’re not being shy at all.

Picture this: Mr. gardener knocks on your door, you open it and he kindly salutes you. Immediately afterwards, as a result of wanting to make your garden prettier, you proceed to ask the gardener to hand you his set of tools, from which you grab a pair of scissors, and top it off by cutting your own grass. You might be asking yourself… Why did we call the gardener in the first place?

Let’s try a different approach:

  def make_garden_pretty(gardener)

This slight modification, abstracts this module from gardening details, which means that, if in the near future our gardener decides to buy a lawn mower, he can change the way in which he does his thing without affecting us.

On to the latter. Did you notice that we are now telling the gardener what we want him to do, and not making him questions about his responsibilities? We are now adhering to the “Tell, don’t ask” principle, creating a shield around us, isolating ourselves from the outside world, not revealing much about our business, only dependent upon abstractions we actually care about. As a result, we’re also obeying Demeter, by limiting the different kind of objects our method interacts with.

In other words, we are being shy, law abiders.

The Law?

Let’s remember the two basic characteristics all Pragmatic Programmers share:

  • Care About Your Craft
  • Think! About Your Work

When crafting our solutions, we should be constantly thinking about the decisions we make, weighing the possible consequences of each change we apply, and how different techniques or approaches might affect the future course of our project.

If we consider ourselves Pragmatic Programmers, then we shouldn’t embrace something as the ultimate law, and obey it invariably in our development journey. We should assemble a fat and diverse toolbox from which to grab the most adequate tool for the problem we’re facing at any given moment.

All systems are different, and deserve special treatment according to their reality, user requirements and present design. A balance has to be made—that’s what being pragmatic means. However, incorporating this style of coding to your toolbox might give you the means to ease your maintenance nightmares and develop more flexible systems, narrowing the gap to meet our ultimate goal: writing flexible enough code which smoothly adapts to change.

And remember: Sometimes, it’s good to be shy.

Further reading: