In “Negative Architecture”, Michael Feathers speaks about certain aspects of software architecture leading to some kind of negative architecture. Feathers mentions the IO Monad from Haskell (functional programming) as an example, but there are parallel examples in object-oriented programming. For example, by using layers and the dependency inversion principle you can “guarantee” that a certain class, in a certain layer (e.g. domain, application) won’t do any IO - no talking to a database, no HTTP requests to some remote service, etc.

Consider the following rule:

When you create an object, it should be complete, consistent and valid in one go.

It is derived from the more general principle that it should not be possible for an object to exist in an inconsistent state. I think this is a very important rule, one that will gradually lead everyone from the swamps of those dreaded “anemic” domain models. However, the question still remains: what does all of this mean?

System and integration tests need database fixtures. These fixtures should be representative and diverse enough to “fake” normal usage of the application, so that the tests using them will catch any issues that might occur once you deploy the application to the production environment. There are many different options for dealing with fixtures; let’s explore some of them.

Generate fixtures the natural way

The first option, which I assume not many people are choosing, is to start up the application at the beginning of a test, then navigate to specific pages, submit forms, click buttons, etc. until finally the database has been populated with the right data. At that point, the application is in a useful state and you can continue with the act/when and assert/then phases. (See the recent article “Pickled State” by Robert Martin on the topic of tests as specifications of a finite state machine).

A very important “trick” in finding the flow in life is: do what you like most. Of course, you have to do things you don’t like (and then you need different life hacks), but when you can do something you like, you’ll find that you’ll be more successful at it.

When it comes to blogging, I find that it helps to follow my instincts, to write about whatever I like to write about at the moment. I can think of a list of things that need blogging about, but I end up not writing about them because they don’t light the fire inside me (anymore).

The downsides of starting with the domain model

All the architectural focus on having a clean and infrastructure-free domain model is great. It’s awesome to be able to develop your domain model in complete isolation; just a bunch of unit tests helping you design the most beautiful objects. And all the “impure” stuff comes later (like the database, UI interactions, etc.).

However, there’s a big downside to starting with the domain model: it leads to inside-out development. The first negative effect of this is that when you start with designing your aggregates (entities and value objects), you will definitely need to revise them when you end up actually using them from the UI. Some aspects may turn out to be not so well-designed at all, and will make no sense from the user’s perspective. Some functionality may have been designed well, but only theoretically, since it will never actually be used by any real client, except for the unit test you wrote for it.

I’d like to start this article with a quote from Ross Tuck’s article “Persisting Value Objects in Doctrine”. He describes different ways of persisting value objects when using Doctrine ORM. At the end of the page he gives us the following option - the “nuclear” one:

[…] Doctrine is great for the vast majority of applications but if you’ve got edge cases that are making your entity code messy, don’t be afraid to toss Doctrine out. Setup an interface for your repositories and create an alternate implementation where you do the querying or mapping by hand. It might be a PITA but it might also be less frustration in the long run.

Statically fetching dependencies

I’ve worked with several code bases that were littered with calls to Zend_Registry::get(), sfContext::getInstance(), etc. to fetch a dependency when needed. I’m a little afraid to mention façades here, but they also belong in this list. The point of this article is not to bash a certain framework (they are all lovely), but to show how to get rid of these “centralized dependency managers” when you need to. The characteristics of these things are:

I wanted to share an important lesson I learned from my colleague Ramon de la Fuente. I was explaining to him how I made a big effort to preserve some existing behavior, when he said something along the lines of: the people who wrote this code, may or may not have known what they were doing. So don’t worry too much about preserving old stuff.

These wise words eventually connected to other things I’ve learned about programming, and I wanted to combine them under the umbrella of a blog post titled “Deliberate coding”. Doing something “deliberately” means to do it consciously and intentionally. It turns out that not everyone writes code deliberately, and at the very least, not everyone does it all the time.

This is a question that always pops up during my workshops: when and where to determine the ID of an entity? There are different answers, no best answer. Well, there are two best answers, but they apply to two different situations.

Auto-incrementing IDs, by the database

Traditionally, all you need for an entity to have an ID is to designate one integer column in the database as the primary key, and mark it as “auto-incrementing”. So, once a new entity gets persisted as a record in the database (using your favorite ORM), it will get an ID. That is, the entity has no identity until it has been persisted. Even though this happens everywhere, and almost always; it’s a bit weird, because:

This article is part 2 of my review of the book “Fifty quick ideas to improve your tests”. I’ll continue to share some of my personal highlights with you.

Replace multiple steps with a higher-level step

If a test executes multiple tasks in sequence that form a higher-level action, often the language and the concepts used in the test explain the mechanics of test execution rather than the purpose of the test, and in this case the entire block can often be replaced with a single higher-level concept.