Tag Archives: Unit Testing

How to Avoid Producing Legacy Code at the Speed of Typing

This blog post provides a recipe on how to avoid producing legacy code at the speed of typing by using a proper architecture and unit testing.

Introduction

As an enterprise software developer, you are in a constant fight against producing legacy code – code that is no longer worthy of maintenance or support. You are constantly struggling to avoid re-writing stuff repeatedly in a faint hope that next time you will get it just right.

The characteristics of legacy code is, among others, poor design and architecture or dependencies on obsolete frameworks or 3rd party components. Here are a few typical examples that you might recognize:

You and your team produced a nice, feature-rich Windows application. Afterwards, you realize that what was really needed was a browser or mobile application. That is when you recognize the tremendous effort it would take to provide an alternative UI to your application, because you have embedded too much domain functionality within the UI itself.

Another scenario might be that you made a backend that is deeply infiltrated in a particular ORM – such as NHibernate or Entity Framework – or highly dependent on a certain RDBMS. At one point, you want to change backend strategy to avoid ORM and use file-based persistence, but then you realize it is practically impossible because your domain functionality and the data layer are tightly coupled.

In both of the above scenarios, you are producing legacy code at the speed of typing.

However, there is still hope. By adapting a few simple techniques and principles, you can change this doomed pattern of yours forever.

The Architectural Evolution

In the following, I will describe 3 phases in a typical architectural evolution for a standard enterprise software developer. Almost any developer will make it to phase 2, but the trick is to make it all the way through phase 3 which will eventually turn you into an architectural Ninja.

Evolution to Ninja

Phase 1 – Doing it Wrong

Most developers have heard about layered architecture, so very often the first attempt on an architecture will look something like this – two layers with separated responsibilities for frontend and backend functionality:

phase_1

So far so good, but quit soon you will realize that it is a tremendous problem that the domain logic of your application is entangled into the platform-dependent frontend and backend.

Phase 2 – A Step Forward

Thus, the next attempt is to introduce a middle layer – a domain layer – comprising the true business functionality of your application:

phase_2

This architecture looks deceptively well-structured and de-coupled. However, it is not. The problem is the red dependency arrow indicating that the domain layer has a hard-wired dependency on the backend – typically, because you are creating instances in the domain layer of backend classes using the new keyword (C# or Java). The domain layer and the backend are tightly coupled. This has numerous disadvantages:

  • The domain layer functionality cannot be reused in isolation in another context. You would have to drag along its dependency, the backend.
  • The domain layer cannot be unit tested in isolation. You would have to involve the dependency, the backend.
  • One backend implementation (using for example a RDBMS for persistence) cannot easily be replaced by another implementation (using for example file persistence).

All of these disadvantages dramatically reduces the potential lifetime of the domain layer. That is why you are producing legacy code at the speed of typing.

Phase 3 – Doing it Right

What you have to do is actually quite simple. You have to turn the direction of that red dependency arrow around. It is a subtle difference, but one that makes all the difference:

phase_3a

This architecture adheres to the Dependency Inversion Principle (DIP) – one of the most important principles of object-oriented design. The point is that once this architecture is established – once the direction of that dependency arrow is turned around – the domain layer dramatically increases its potential lifetime. UI requirements and trends may switch from Windows to browsers or mobile devices, and your preferred persistence mechanism might change from being RDBMS-based to file-based, but now that is all relatively easily exchangeable without modifying the domain layer. Because at this point the frontend as well as backend is de-coupled from the domain layer. Thus, the domain layer becomes a code library that you theoretically never ever have to replace – at least as long as your business domain and overall programming framework remain unchanged. Now, you are efficiently fighting that legacy code.

On a side note, let me give you one simple example on how to implement DIP in practice:

Maybe you have a product service in the domain layer that can perform CRUD operations on products in a repository defined in the backend. This very often leads to a dependency graph like the one shown below, with the dependency arrow pointing in the wrong direction:

DI_1

This is because somewhere in the product service you will “new” up a dependency to the product repository:

To inverse the direction of the dependency using DIP, you must introduce an abstraction of the product repository in form of an IProductRepository interface in the domain layer and let the product repository be an implementation of this interface:

DI_2

Now, instead of “newing” up an instance of the product repository in the product service, then you inject the repository into the service through a constructor argument:

This is known as dependency injection (DI). I have previously explained this in much more detail in a blog post called Think Business First.

Once you have established the correct overall architecture, the objective of the fight against legacy code should be obvious: move as much functionality as you can into the domain layer. Make those frontend and backend layers shrink and make that domain layer grow fat:

phase_3b

A very convenient bi-product of this architecture is that it makes it easy to establish unit tests of the domain functionality. Because of the de-coupled nature of the domain layer and the fact that all of its dependencies are represented by abstractions (such as an interface or an abstract base class), it is quite easy to establish fake objects of these abstractions and use them when establishing unit test fixtures. So it is “a walk in the park” to guard the entire domain layer with unit tests. You should strive for nothing less than a 100% unit test coverage – making your domain layer extremely robust and solid as a rock. Which again will increase the lifetime of the domain layer.

You are probably starting to realize that not only traditional frontends or backends, but all other components – including the unit tests or for example an http-based Web API – should act as consumers of the domain layer. Thus, it makes a lot of sense to depict the architecture as onion layers:

phase_3c

The outer layer components consume the domain library code – either by providing concrete implementations of domain abstractions (interfaces or base classes) or as a direct consumer of domain functionality (domain model and services).

However, still remember: the direction of coupling is always toward the center – toward the domain layer.

At this point, it might all seem a bit theoretic and, well…, abstract. Nevertheless, it does not take a lot to do this in practice. In another CodeProject article of mine I have described and provided some sample code that complies with all of the principles in this article. The sample code is simple, yet very close to real production code.

Summary

Being an enterprise software developer is a constant battle to avoid producing legacy code at the speed of typing. To prevail, do the following:

  • Make sure all those dependency arrows point toward the central and independent domain layer by applying the Dependency Inversion Principle (DIP) and Dependency Injection (DI).
  • Constantly nourish the domain layer by moving as much functionality as possible into it. Make that domain layer grow fat and heavy while shrinking the outer layers.
  • Cover every single functionality of the domain layer by unit tests.

Follow these simple rules and it will all come together. The code that you write will potentially have a dramatically longer lifetime than before because:

  • The domain layer functionality can be reused in many different contexts.
  • The domain layer can be made robust and solid as a rock with a 100% unit test coverage.
  • Implementations of domain layer abstractions (for example persistence mechanisms) can easily be replaced by alternative implementations.
  • The domain layer is easy to maintain.

Lightweight Domain Services Library

If you have more than a few years of experience within domain-driven design (DDD), most certainly, you have recognized some kind of overall pattern in the type of problems you have to solve – regardless of the type of applications you are working on. I certainly know that I have.

No matter whether you develop desktop applications, web applications or web API’s, you will almost always find yourself in a situation where you have to establish a mechanism for creating, persisting and maintaining state of various entities in the application domain model. So, every time you start up a new project you have to do a lot of yak shaving to establish this persistence mechanism, when what you really want to do is to work on establishing the domain model – the actual business functionality of your application.

After several iterations through various projects, I have established a practice that works for me in almost any situation. This practice allows you to abstract the entity persistence (the yak shaving…) so that you can easily isolate the nitty-gritty implementation details of this and focus on developing your genuine business functionality. Eventually, of course you have to deal with the implementation of the persistence layer, but the value of being able to develop – and not at least test – your domain model in isolation without having to care about the persistence details is tremendous. Then, you can start out with developing and testing your domain model against fake repositories. Whether you eventually end up making simple file based repositories or decide to go full-blown RDBMS doesn’t matter at this point in time.

I have digested this practice of mine into something I call a Domain Services Library and written a CodeProject article about this. This framework is super lightweight comprising only a few plain vanilla C# classes. No ORM is involved – the repositories can be anything from in-memory objects to RDBMS. No 3rd party dependencies whatsoever. Source code download is provided in the article.

Unit Testing Made Easy – DI part 3

I claimed in a previous post that low coupling using dependency injection made the code base more testable – i.e. properly prepared for unit testing. Let’s dig a bit deeper into that assertion.

The ProductService class is an obvious candidate for unit testing. It is a relatively small component with a well-defined responsibility (adhering to the Single Responsibility Principle). It is also properly isolated from its dependency (the repository), by an abstraction (the interface). Let’s create a unit test method for the ProductService component:

This unit test method verifies that the ProductService functionality for calculating a discounted price of a product works correctly. It follows the standard sequence of a unit test: First it sets up the fixed baseline environment for the test (also called the test fixture). Then it exercises the system under test (in this case the product service). Finally, it verifies the expected outcome. A “tear down” phase is not necessary, as the fixture objects automatically gets out of scope and will be garbage-collected.

As ProductService does not care about the actual implementation of the product repository dependency, you can inject a “stand-in” for this dependency in the test. This stand-in is better known as a test double. The mockRepository variable holds an instance of such a product repository test double.

In the final application you are probably going to implement the repository so that the products are persisted in for example an SQL database, or maybe a file, but the elegant thing is that, at this moment in time, you do not need to care about this. In the context of the unit test, you can just make a mock implementation of the repository which does not implement persistence of the products at all, but just keep them in memory. This is our test double. Obviously, an implementation like this would never make it into the final application, but it is sufficient to test the ProductService functionality in isolation.

Such a mock implementation of a repository is easily done. Of course you make a generic version that can be used as test double for all entity repositories:

A Dictionary object is used to hold the entities in memory during the test.

Testability is not necessarily the main purpose for doing dependency injection, but the ability to replace dependencies with test-specific mock objects is indeed a very useful by-product.

By the way, the unit test method above is written using the xUnit.net testing framework, which explains the Fact attribute and the Equal assertion. xUnit.net is a nice and very lean testing framework – compared to for example the MSTest, which is the one integrated with Visual Studio. With xUnit.net you don’t need to create a specific test unit project. Also, you get rid of the auto-generated .vsmdi files and .testsettings files from MSTest.

To further refine and automate your unit tests, you should consider using supplementary unit test frameworks like AutoFixture and Moq to help you streamline fixture setup and mocking. Both are available from within the “NuGet Package Manager” Visual Studio Extension. I have written a comprehensive CodeProject article about using xUnit.net, AutoFixture and Moq.