There still seems to be some confusion about what is Dependency Injection (DI) and what is a DI Container, so in this post I will try to sort it out as explicitly as possible.

DI is a set of principles and patterns that enable loose coupling.

That’s it; nothing else. Remember that old quote from p. 18 of Design Patterns?

Program to an interface; not an implementation.

This is the concern that DI addresses. The most useful DI pattern is Constructor Injection where we inject dependencies into consumers via their constructors. No container is required to do this.

The easiest way to build a DI-friendly application is to just use Constructor Injection all the way. Conversely, an application does not automatically become loosely coupled when we use a DI Container. Every time application code queries a container we have an instance of the Service Locator anti-pattern. The corollary leads to this variation of the Hollywood Principle:

Don’t call the container; it’ll call you.

A DI Container is a fantastic tool. It’s like a (motorized) mixer: you can whip cream by hand, but it’s easier with a mixer. On the other hand, without the cream the mixer is nothing. The same is true for a DI Container: to really be valuable, your code must employ Constructor Injection so that the container can auto-wire dependencies.

A well-designed application adheres to the Hollywood Principle for DI Containers: it doesn’t call the container. On the other hand, we can use the container to compose the application – or we can do it the hard way; this is called Poor Man’s DI. Here’s an example that uses Poor Man’s DI to compose a complete application graph in a console application:

            private
            static
            void Main(string[]
args)

{

    var msgWriter
= newConsoleMessageWriter();

    newCoalescingParserSelector(

        newIParser[]

        {

            newHelpParser(msgWriter),

            newWineInformationParser(

                newSqlWineRepository(),

                msgWriter)

        })

        .Parse(args)

        .CreateCommand()

        .Execute();

}

Notice how the nested structure of all the dependencies gives you an almost visual idea about the graph. What we have here is Constructor Injection all the way in.

CoalescingParserSelector’s constructor takes an IEnumerable<IParser> as input. Both HelpParser and WineInformationParser requires an IMessageWriter, and WineInformationParser also an IWineRepository. We even pull in types from different assemblies because SqlWineRepository is defined in the SQL Server-based data access assembly.

Another thing to notice is that the msgWriter variable is shared among two consumers. This is what a DI Container normally addresses with its ability to manage component lifetime. Although there’s not a DI Container in sight, we could certainly benefit from one. Let’s try to wire up the same graph using Unity (just for kicks):

            private
            static
            void Main(string[]
args)

{

    var container
= newUnityContainer();

    container.RegisterType<IParser, WineInformationParser>("parser.info");

    container.RegisterType<IParser, HelpParser>("parser.help");

    container.RegisterType<IEnumerable<IParser>, IParser[]>();

    container.RegisterType<IParseService, CoalescingParserSelector>();

    container.RegisterType<IWineRepository, SqlWineRepository>();

    container.RegisterType<IMessageWriter, ConsoleMessageWriter>(

        newContainerControlledLifetimeManager());

    container.Resolve<IParseService>()

        .Parse(args)

        .CreateCommand()

        .Execute();

    container.Dispose();

}

We are using Constructor Injection throughout, and most DI Containers (even Unity, but not MEF) natively understands that pattern. Consequently, this means that we can mostly just map interfaces to concrete types and the container will figure out the rest for us.

Notice that I’m using the Configure-Resolve-Release pattern described by Krzysztof Koźmic. First I configure the container, then I resolve the entire object graph, and lastly I dispose the container.

The main part of the application’s execution time will be spent within the Execute method, which is where all the real application code runs.

In this example I wire up a console application, but it just as well might be any other type of application. In a web application we just do a resolve per web request instead.

But wait! does that mean that we have to resolve the entire object graph of the application, even if we have dependencies that cannot be resolved at run-time? No, but that does not mean that you should pull from the container. Pull from an Abstract Factory instead.

Another question that is likely to arise is: what if I have dependencies that I rarely use? Must I wire these prematurely, even if they are expensive? No, you don’t have to do that either.

In conclusion: there is never any reason to query the container. Use a container to compose your object graph, but don’t rely on it by querying from it. Constructor Injection all the way enables most containers to auto-wire your application, and an Abstract Factory can be a dependency too.

Occasionally I get a question about whether it is reasonable or advisable to let domain objects implement IDataErrorInfo. In summary, my answer is that it’s not so much a question about whether it’s a leaky abstraction or not, but rather whether it makes sense at all. To me, it doesn’t.

Let us first consider the essence of the concept underlying IDataErrorInfo: It provides information about the validity of an object. More specifically, it provides error information when an object is in an invalid state.

This is really the crux of the matter. Domain Objects should be designed so that they cannot be put into invalid states. They should guarantee their invariants.

Let us return to the good old DanishPhoneNumber example. Instead of accepting or representing a Danish phone number as a string or integer, we model it as a Value Object that encapsulates the appropriate domain logic.

More specifically, the class’ constructor guarantees that you can’t create an invalid instance:

            private
            readonly
            int number;

            public DanishPhoneNumber(int number)

{

    if ((number
< 112) ||

        (number > 99999999))

    {

        thrownewArgumentOutOfRangeException("number");

    }

    this.number
= number;

}

Notice that the Guard Clause guarantees that you can’t create an instance with an invalid number, and the readonly keyword guarantees that you can’t change the value afterwards. Immutable types make it easier to protect a type’s invariants, but it is also possible with mutable types – you just need to place proper Guards in public setters and other mutators, as well as in the constructor.

In any case, whenever a Domain Object guarantees its invariants according to the correct domain logic it makes no sense for it to implement IDataErrorInfo; if it did, the implementation would be trivial, because there would never be an error to report.

Does this mean that IDataErrorInfo is a redundant interface? Not at all, but it is important to realize that it’s an Application Boundary concern instead of a Domain concern. At Application Boundaries, data entry errors will happen, and we must be able to cope with them appropriately; we don’t want the application to crash by passing unvalidated data to DanishPhoneNumber’s constructor.

Does this mean that we should duplicate domain logic at the Application Boundary? That should not be necessary. At first, we can apply a simple refactoring to the DanishPhoneNumber constructor:

            public DanishPhoneNumber(int number)

{

    if (!DanishPhoneNumber.IsValid(number))

    {

        thrownewArgumentOutOfRangeException("number");

    }

    this.number
= number;

}

            public
            static
            bool IsValid(int number)

{

    return (112
<= number)

        && (number <= 99999999);

}

We now have a public IsValid method we can use to implement an IDataErrorInfo at the Application Boundary. Next steps might be to add a TryParse method.

IDataErrorInfo implementations are often related to input forms in user interfaces. Instead of crashing the application or closing the form, we want to provide appropriate error messages to the user. We can use the Domain Object to provide validation logic, but the concern is completely different: we want the form to stay open until valid data has been entered. Not until all data is valid do we allow the creation of a Domain Object from that data.

In short, if you feel tempted to add IDataErrorInfo to a Domain Class, consider whether you aren’t about to violate the Single Responsibility Principle. In my opinion, this is the case, and you would be better off reconsidering the design.

It seems to me that I’ve lately encountered a particular mindset towards Dependency Injection (DI). People seem to think that it’s only really good for replacing one data access implementation with another. Once you get to that point, you know that the following argument isn’t far behind:

“That’s all well and good, but we know for certain that we will never exchange [insert name of RDBMS here] with anything else in this application.”

Apart from the hubris of making such a bold statement about the future of any software endeavor, such a statement reveals the narrow view on DI that its only purpose is for replacing data access components – and perhaps for unit testing.

Those are relevant reasons for using DI, but they are only some of the reasons. Let’s briefly revisit why we employ DI.

We use DI to enable loose coupling.

DI is only a means to an end. Even if you never intend to replace your database and even if you never want to write a single unit test, DI still offers benefits in form of a more maintainable code base. The loose coupling gives you better separation of concerns because it allows you to apply the Open/Closed Principle.

Example coming right up:

Imagine that we need to implement a PrécisViewModel class with a TopSellers property that returns an IEnumerable<string>. To implement this class, we have a data access component. Let’s use the ubiquitous Repository pattern and define IProductRepository to see where that leads us:

            public
            interface
            IProductRepository
          

{

    IEnumerable<Product>
SelectTopSellers();

}

We can now implement PrécisViewModel like this:

            public
            class
            PrécisViewModel
          

{

    privatereadonlyIProductRepository repository;

    public PrécisViewModel(IProductRepository repository)

    {

        if (repository
== null)

        {

            thrownewArgumentNullException("repository");

        }

        this.repository
= repository;

    }

    publicIEnumerable<string>
TopSellers

    {

        get

        {

            var topSellers
= 

                this.repository.SelectTopSellers();

            returnfrom p in topSellers

                   select p.Name;

        }

    }

}

Nothing fancy is going on here. It’s just straight Constructor Injection at work.

Obviously, we can now implement and use a SQL Server-based repository:

            var repository
= newSqlProductRepository();

            var vm
= newPrécisViewModel(repository);

So what does all this loose coupling buy us? It doesn’t seem to help us a lot.

The real benefit is not yet apparent, but it should become more obvious when we start adding requirements. Let’s start with some caching. It turns out that the SelectTopSellers implementation is slow, so we would like to add some caching somewhere.

Where should we add this caching functionality? Without loose coupling, we would more or less be constrained to adding it to either PrécisViewModel or SqlProductRepository, but both have issues:

• First of all we would be violating the Single Responsibility Principle (SRP) in both cases.
• If we implement caching in PrécisViewModel, other consumers of the SelectTopSellers would not benefit from it.
• If we implement caching in SqlProductRepository, it wouldn’t be available for any other IProductRepository implementations.

Since the premise for this post is that we will never use any other database than SQL Server, implementing caching directly in SqlProductRepository sounds like the correct choice, but we would still be violating the SRP, and thus making our code more difficult to maintain.

A better solution is to introduce a caching Decorator like this one:

            public
            class
            CachingProductRepository : IProductRepository

{

    privatereadonlyICache cache;

    privatereadonlyIProductRepository repository;

    public CachingProductRepository(

        IProductRepository repository, ICache cache)

    {

        if (repository
== null)

        {

            thrownewArgumentNullException("repository");

        }

        if (cache
== null)

        {

            thrownewArgumentNullException("cache");

        }

        this.cache
= cache;

        this.repository
= repository;

    }

               
#region IProductRepository Members

    publicIEnumerable<Product>
SelectTopSellers()

    {

        returnthis.cache

            .Retrieve<IEnumerable<Product>>("topSellers",

                this.repository.SelectTopSellers);

    }

               
#endregion
          

}

For completeness sake is here the definition of ICache:

            public
            interface
            ICache
          

{

    T Retrieve<T>(string key, Func<T>
readThrough);

}

The point is that CachingProductRepository extends any IProductRepository we provide to it (including SqlProductRepository) without modifying it. Thus, we have satisfied both the OCP and the SRP.

Just to drive home the point, let us assume that we also wish to record execution times for various methods for purposes of SLA compliance. We can do this by introducing yet another Decorator:

            public
            class
            PerformanceMeasuringProductRepository : 

    IProductRepository

{

    privatereadonlyIProductRepository repository;

    privatereadonlyIStopwatch stopwatch;

    public PerformanceMeasuringProductRepository(

        IProductRepository repository, 

        IStopwatch stopwatch)

    {

        if (repository
== null)

        {

            thrownewArgumentNullException("repository");

        }

        if (stopwatch
== null)

        {

            thrownewArgumentNullException("stopwatch");

        }

        this.repository
= repository;

        this.stopwatch
= stopwatch;

    }

               
#region IProductRepository Members

    publicIEnumerable<Product>
SelectTopSellers()

    {

        var timer
= this.stopwatch

            .StartMeasuring("SelectTopSellers");

        var topSellers
= 

            this.repository.SelectTopSellers();

        timer.StopMeasuring();

        return topSellers;

    }

               
#endregion
          

}

Once again, we modified neither SqlProductRepository nor CachingProductRepository to introduce this new feature. We can implement security and auditing features by following the same principle.

To me, this is what loose coupling (and DI) is all about. That we can also replace data access components and unit test using dynamic mocks are very fortunate side effects, but the loose coupling is valuable in itself because it enables us to write more maintainable code.

We don’t even need a DI Container to wire up all these repositories (although it sure would be helpful). Here’s how we can do it with Poor Man’s DI:

            IProductRepository repository
=

    newPerformanceMeasuringProductRepository(

        newCachingProductRepository(

            newSqlProductRepository(), newCache()

            ),

        newRealStopwatch()

    );

            var vm
= newPrécisViewModel(repository);

The next time someone on your team claims that you don’t need DI because the choice of RDBMS is fixed, you can tell them that it’s irrelevant. The choice is between DI and Spaghetti Code.

About a week ago Uncle Bob published a post on Dependency Injection Inversion that caused quite a stir in the tiny part of the .NET community I usually pretend to hang out with. Twitter was alive with much debate, but Ayende seems to sum up the .NET DI community's sentiment pretty well:

if this is a typical example of IoC usage in the Java world, then [Uncle Bob] should peek over the fence to see how IoC is commonly implemented in the .Net space

Despite having initially left a more or less positive note to Uncle Bob's post, after having re-read it carefully, I am beginning to think the same, but instead of just telling everyone how much greener the grass is on the .NET side, let me show you.

First of all, let's translate Uncle Bob's BillingService to C#:

It's nice how easy it is to translate Java code to C#, but apart from casing and other minor deviations, let's focus on the main difference. I've added Guard Clauses to protect the injected dependencies against null values as I consider this an essential and required part of Constructor Injection – I think Uncle Bob should have added those as well, but he might have omitted them for brevity.

If you disregard the Guard Clauses, the C# version is a logical line of code shorter than the Java version because it has no DI attribute like Guice's @Inject.

Does this mean that we can't do DI with the C# version of BillingService? Uncle Bob seems to imply that we can do Dependency Inversion, but not Dependency Injection - or is it the other way around? I can't really make head or tails of that part of the post…

The interesting part is that in .NET, there's no difference! We can use DI Containers with the BillingService without sprinkling DI attributes all over our code base. The BillingService class has no reference to any DI Container.

It does, however, use the central DI pattern Constructor Injection. .NET DI Containers know all about this pattern, and with .NET's static type system they know all they need to know to wire dependencies up correctly. (I thought that Java had a static type system as well, but perhaps I am mistaken.) The .NET DI Containers will figure it out for you – you don't have to explicitly tell them how to invoke a constructor with two parameters.

We can write an entire application by using Constructor Injection and stacking dependencies without ever referencing a container!

Like the Lean concept of the Last Responsible Moment, we can wait until the application's entry point to decide how we will wire up the dependencies.

As Uncle Bob suggests, we can use Poor Man's DI and manually create the dependencies directly in Main, but as Ayende correctly observes, that only looks like an attractive alternative because the example is so simple. For complex dependency graphs, a DI Container is a much better choice.

With the C# version of BillingService, which DI Container must we select?

It doesn't matter: we can choose whichever one we would like because we have been following patterns instead of using a framework.

Here's an example of an implementation of Main using Castle Windsor:

This looks a lot like Uncle Bob's first Guice example, but instead of injecting a BillingModule into the container, we can configure it inline or in a helper method:

This corresponds more or less to the Guice-specific BillingModule, although Windsor also requires us to register the concrete BillingService as a component (this last step varies a bit from DI Container to DI Container – it is, for example, redundant in Unity).

Imagine that in the future we want to rewire this program to use a different DI Container. The only piece of code we need to change is this Composition Root. We need to change the container declaration and configuration and then we are ready to use a different DI Container.

The bottom line is that Uncle Bob's Dependency Injection Inversion is redundant in .NET. Just use a few well-known design patterns and principles and you can write entire applications with DI-friendly, DI-agnostic code bases.

I recently posted a first take on guidelines for writing DI-agnostic code. I plan to evolve these guiding principles and make them a part of my upcoming book.

My previous post led to this comment by Phil Haack:

Your LazyOrderShipper directly instantiates an OrderShipper. What about the dependencies that OrderShipper might require? What if those dependencies are costly?

I didn't want to make my original example more complex than necessary to get the point across, so I admit that I made it a bit simpler than I might have liked. However, the issue is easily solved by enabling DI for the LazyOrderShipper itself.

As always, when the dependency's lifetime may be shorter than the consumer, the solution is to inject (via the constructor!) an Abstract Factory, as this modification of LazyOrderShipper shows:

            public
            class
            LazyOrderShipper2 : IOrderShipper

{

    privatereadonlyIOrderShipperFactory factory;

    privateIOrderShipper shipper;

    public LazyOrderShipper2(IOrderShipperFactory factory)

    {

        if (factory
== null)

        {

            thrownewArgumentNullException("factory");

        }

        this.factory
= factory;

    }

               
#region IOrderShipper Members

    publicvoid Ship(Order order)

    {

        if (this.shipper
== null)

        {

            this.shipper
= this.factory.Create();

        }

        this.shipper.Ship(order);

    }

               
#endregion
          

}

But, doesn't that reintroduce the OrderShipperFactory that I earlier claimed was a bad design?

No, it doesn't, because this IOrderShipperFactory doesn't rely on static configuration. The other point is that while we do have an IOrderShipperFactory, the original design of OrderProcessor is unchanged (and thus blissfully unaware of the existence of this Abstract Factory).

The lifetime of the various dependencies is completely decoupled from the components themselves, and this is as it should be with DI.

This version of LazyOrderShipper is more reusable because it doesn't rely on any particular implementation of OrderShipper – it can Lazily create any IOrderShipper.

Jeffrey Palermo recently posted a blog post titled Constructor over-injection anti-pattern – go read his post first if you want to be able to follow my arguments.

His point seems to be that Constructor Injection can be an anti-pattern if applied too much, particularly if a consumer doesn't need a particular dependency in the majority of cases.

The problem is illustrated in this little code snippet:

            bool isValid
= _validator.Validate(order);  

            if (isValid) 

{

    _shipper.Ship(order);  

}

If the Validate method returns false often, the shipper dependency is never needed.

This, he argues, can lead to inefficiencies if the dependency is costly to create. It's not a good thing to require a costly dependency if you are not going to use it in a lot of cases.

That sounds like a reasonable statement, but is it really? And is the proposed solution a good solution?

No, this isn't a reasonable statement, and the proposed solution isn't a good solution.

It would seem like there's a problem with Constructor Injection, but in reality the problem is that it is being used incorrectly and in too constrained a way.

The proposed solution is problematic because it involves tightly coupling the code to OrderShipperFactory. This is more or less a specialized application of the Service Locator anti-pattern.

Consumers of OrderProcessor have no static type information to warn them that they need to configure the OrderShipperFactory.CreationClosure static member - a completely unrelated type. This may technically work, but creates a very developer-unfriendly API. IntelliSense isn't going to be of much help here, because when you want to create an instance of OrderProcessor, it's not going to remind you that you need to statically configure OrderShipperFactory first. Enter lots of run-time exceptions.

Another issue is that he allows a concrete implementation of an interface to change the design of the OrderProcessor class - that's hardly in the spirit of the Liskov Substitution Principle. I consider this a strong design smell.

One of the commenters (Alwin) suggests instead injecting an IOrderShipperFactory. While this is a better option, it still suffers from letting a concrete implementation influence the design, but there's a better solution.

First of all we should realize that the whole case is a bit construed because although the IOrderShipper implementation may be expensive to create, there's no need to create a new instance for every OrderProcessor. Instead, we can use the so-called Singleton lifetime style where we share or reuse a single IOrderShipper instance between multiple OrderProcessor instances.

The beauty of this is that we can wait making that decision until we wire up the actual dependencies. If we have implementations of IOrderShipper that are inexpensive to create, we may still decide to create a new instance every time.

There may still be a corner case where a shared instance doesn't work for a particular implementation (perhaps because it's not thread-safe). In such cases, we can use Lazy loading to create a LazyOrderShipper like this (for clarity I've omitted making this implementation thread-safe, but that would be trivial to do):

            public
            class
            LazyOrderShipper : IOrderShipper

{

    privateOrderShipper shipper;

               
#region IOrderShipper Members

    publicvoid Ship(Order order)

    {

        if (this.shipper
== null)

        {

            this.shipper
= newOrderShipper();

        }

        this.shipper.Ship(order);

    }

               
#endregion
          

}

Notice that this implementation of IOrderShipper only creates the expensive OrderShipper instance when it needs it.

Instead of directly injecting the expensive OrderShipper instance directly into OrderProcessor, we wrap it in the LazyOrderShipper class and inject that instead. The following test proves the point:

[TestMethod]

            public
            void OrderProcessorIsFast()

{

    //
Fixture setup

    var stopwatch
= newStopwatch();

    stopwatch.Start();

    var order
= newOrder();

    var validator
= newMock<IOrderValidator>();

    validator.Setup(v => 

        v.Validate(order)).Returns(false);

    var shipper
= newLazyOrderShipper();

    var sut
= newOrderProcessor(validator.Object,

        shipper);

    //
Exercise system

    sut.Process(order);

    //
Verify outcome

    stopwatch.Stop();

    Assert.IsTrue(stopwatch.Elapsed
< 

        TimeSpan.FromMilliseconds(777));

    Console.WriteLine(stopwatch.Elapsed);

    //
Teardown

}

This test is significantly faster than 777 milliseconds because the OrderShipper never comes into play. In fact, the stopwatch instance reports that the elapsed time was around 3 ms!

The bottom line is that Constructor Injection is not an anti-pattern. On the contrary, it is the most powerful DI pattern available, and you should think twice before deviating from it.

The SOLID principles of OOD as originally put forth by Robert C. Martin make for such a catchy acronym, although they seem to originally have been spelled SOLDI.

In any case I've lately been thinking a bit about these principles and it seems to me that the Single Responsibility Principle (SRP) and the Interface Segregation Principle (ISP) seem to be very much related. In essence you could say that the ISP is simply SRP applied to interfaces.

The notion underlying both is that a type should deal with only a single concept. Whether that applies to the public API or the internal implementation is less relevant because a corollary to the Liskov Substitution Principle (LSP) and Dependency Inversion Principle (DIP) is that we shouldn't really care about the internals (unless we are actually implementing, that is).

The API is what matters.

Although I do understand the subtle differences between SRP and ISP I think they are so closely related that one of them is really redundant. We can remove the ISP and still have a fairly good acronym: SOLD (although SOLID is still better).

There's one principle that I think is missing from this set: The principle about Command/Query Separation (CQS). In my opinion, this is a very important principle that should be highlighted more than is currently the case.

If we add CQS to SOLD, we are left with some less attractive acronyms:

• SCOLD
• COLDS
• CLODS

Not nearly as confidence-inspiring acronyms as SOLID, but nonetheless, I'm striving to write COLDS code.

When I talk with people about TDD and unit testing, the discussion often moves into the area of Testability – that is, the software's susceptibility to unit testing. A couple of years back, Roy even discussed the seemingly opposable forces of Object-Oriented Design and Testability.

Lately, it has been occurring to me that there really isn't any conflict. Encapsulation is important because it manifests expert knowledge so that other developers can effectively leverage that knowledge, and it does so in a way that minimizes misuse.

However, too much encapsulation goes against the Open/Closed Principle (that states that objects should be open for extension, but closed for modification). From a Testability perspective, the Open/Closed Principle pulls object-oriented design in the desired direction. Equivalently, done correctly, making your API Testable is simply opening it up for extensibility.

As an example, consider a simple WPF ViewModel class called MainWindowViewModel. This class has an ICommand property that, when invoked, should show a message box. Showing a message box is good example of breaking testability, because if the SUT were to show a message box, it would be very hard to automatically verify and we wouldn't have fully automated tests.

For this reason, we need to introduce an abstraction that basically models an action with a string as input. Although we could define an interface for that, an Action<string> fits the bill perfectly.

To enable that feature, I decide to use Constructor Injection to inject that abstraction into the MainWindowViewModel class:

            public MainWindowViewModel(Action<string>
notify)

{

    this.ButtonCommand
= newRelayCommand(p
=> 

    { notify("Button
was clicked!"); });

}

When I recently did that at a public talk I gave, one member of the audience initially reacted by assuming that I was now introducing test-specific code into my SUT, but that's not the case.

What I'm really doing here is opening the MainWindowViewModel class for extensibility. It can still be used with message boxes:

            var vm
= newMainWindowViewModel(s
=> MessageBox.Show(s));

but now we also have the option of notifying by sending off an email; writing to a database; or whatever else we can think of.

It just so happens that one of the things we can do instead of showing a message box, is unit testing by passing in a Test Double.

            //
Fixture setup
          

            var mockNotify
= 

    MockRepository.GenerateMock<Action<string>>();

mockNotify.Expect(a => a("Button
was clicked!"));

            var sut
= newMainWindowViewModel(mockNotify);

            //
Exercise system
          

sut.ButtonCommand.Execute(newobject());

            //
Verify outcome
          

mockNotify.VerifyAllExpectations();

            //
Teardown
          

Once again, TDD has lead to better design. In this case it prompted me to open the class for extensibility. There really isn't a need for Testability as a specific concept; the Open/Closed Principle should be enough to drive us in the right direction.

Pragmatically, that's not the case, so we use TDD to drive us towards the Open/Closed Principle, but I think it's important to note that we are not only doing this to enable testing: We are creating a better and more flexible API at the same time.

This is really nothing new, but I don't think I've explicitly stated this before: It makes a lot of sense to view delegates as anonymous one-method interfaces.

Many people liken delegates to function pointers. While that's probably correct (I wouldn't really know), it's not a very object-oriented view to take – at least not when we are dealing with managed code. To me, it makes more sense to view delegates as anonymous one-method interfaces.

Lets consider a simple example. As always, we have the ubiquitous MyClass with its DoStuff method. In this example, DoStuff takes as input an abstraction that takes a string as input and returns an integer – let's imagine that this is some kind of Strategy (notice the capital S – I'm talking about the design pattern, here).

In traditional object-oriented design, we could solve this by introducing the IMyInterface type:

            public
            interface
            IMyInterface
          

{

    int DoIt(string message);

}

The implementation of DoStuff is simply:

            public
            string DoStuff(IMyInterface strategy)

{

    return strategy.DoIt("Ploeh").ToString();

}

Hardly rocket science…

However, defining a completely new interface just to do this is not really necessary, since we could just as well have implemented DoStuff with a Func<string, int>:

            public
            string DoStuff(Func<string, int>
strategy)

{

    return strategy("Ploeh").ToString();

}

This not only frees us from defining a new interface, but also from implementing that interface to use the DoStuff method. Instead, we can simply pass a lambda expression:

            string result
= sut.DoStuff(s => s.Count());

What's most amazing is that RhinoMocks understands and treats delegates just like other abstract types, so that we can write the following to treat it as a mock:

            //
Fixture setup
          

            Func<string, int>
mock =

    MockRepository.GenerateMock<Func<string, int>>();

mock.Expect(f => f("Ploeh")).Return(42);

            var sut
= newMyClass();

            //
Exercise system
          

            string result
= sut.DoStuff(mock);

            // Verify
outcome
          

mock.VerifyAllExpectations();

            //
Teardown
          

Whenever possible, I prefer to model my APIs with delegates instead of one-method interfaces, since it gives me greater flexibility and less infrastructure code.

Obviously, this technique only works as long as you only need to abstract a single method. As soon as your abstraction needs a second method, you will need to introduce a proper interface or, preferably, an abstract base class.

Udi recently posted an article on managing loose coupling in Visual Studio. While I completely agree, this is a topic that deserves more detailed treatment. In particular, I'd like to expand on this statement:

"In fact, each component could theoretically have its own solution"

This is really the crux of the matter, although in practical terms, you'd typically need at least a couple of projects per component. In special cases, a component may truly be a stand-alone component, requiring no other dependencies than what is already in the BCL (in fact, AutoFixture is just such a component), but most components of more complex software have dependencies.

Even when you are programming against interfaces (which you should be), these interfaces will normally be defined in other projects.

A component may even use multiple interfaces, since it may be implementing some, but consuming others, and these interfaces may be defined in different projects. This is particularly the case with Adapters.

Finally, you should have at least one unit test project that targets your component.

In essence, while the exact number of projects you need will vary, it should stay small. In the figure above, we end up with five projects, but there's also quite a few abstractions being pulled in.

As a rule of thumb I'd say that if you can't create an .sln file that contains less than ten projects to work on any component, you should seriously consider your decoupling strategy.

You may choose to work with more than ten projects in a solution, but it should always be possible to create a solution to work with a single component, and it should drag only few dependencies along.

As a response to my description of how AutoFixture creates objects, Klaus asked:

“[What] if the constructor of ComplexChild imposes some kind of restriction on its parameter? If, for example, instead of the "name" parameter, it would take a "phoneNumber" parameter (as a string), and do some format checking?”

Now that we have covered some of the basic features of AutoFixture, it’s time to properly answer this excellent question.

For simplicity’s sake, let’s assume that the phone number in question is a Danish phone number: This is pretty good for example code, since a Danish phone number is essentially just an 8-digit number. It can have white space and an optional country code (+45), but strip that away, and it’s just an 8-digit number. However, there are exceptions, since the emergency number is 112 (equivalent to the American 911), and other 3-digit special numbers exist as well.

With that in mind, let’s look at a simple Contact class that contains a contact’s name and Danish phone number. The constructor might look like this:

            public Contact(string name, string phoneNumber)

{

    this.Name
= name;

    this.PhoneNumber
= 

        Contact.ParsePhoneNumber(phoneNumber);

}

The static ParsePhoneNumber method strips away white space and optional country code and parses the normalized string to a number. This fits the scenario laid out in Klaus’ question.

So what happens when we ask AutoFixture to create an instance of Contact? It will Reflect over Contact’s constructor and create two new anonymous string instances – one for name, and one for phoneNumber. As previously described, each string will be created as a Guid prepended with a named hint – in this case the argument name. Thus, the phoneNumber argument will get a value like "phoneNumberfa432351-1563-4769-842c-7588af32a056", which will cause the ParsePhoneNumber method to throw an exception.

How do we deal with that?

The most obvious fix is to modify AutoFixture’s algorithm for generating strings. Here an initial attempt:

fixture.Register<string>(()
=> "112");

This will simply cause all generated strings to be "112", including the Contact instance's Name property. In unit testing, this may not be a problem in itself, since, from an API perspective, the name could in principle be any string.

However, if the Contact class also had an Email property that was parsed and verified from a string argument, we'd be in trouble, since "112" is not a valid email address.

We can't easily modify the string generation algorithm to fit the requirements for both a Danish telephone number and an email address.

Should we then conclude that AutoFixture isn't really useful after all?

On the contrary, this is a hint to us that the Contact class' API could be better. If an automated tool can't figure out how to generate correct input, how can we expect other developers to do it?

Although humans can make leaps of intuition, an API should still go to great lengths to protect its users from making mistakes. Asking for an unbounded string and then expecting it to be in a particular format may not always be the best option available.

In our particular case, the Value Object pattern offers a better alternative. Our first version of the DanishPhoneNumber class simply takes an integer as a constructor argument:

            public DanishPhoneNumber(int number)

{

    this.number
= number;

}

If we still need to parse strings (e.g. from user input), we could add a static Parse, or even a TryParse, method and test that method in isolation without involving the Contact class.

This neatly solves our original issue with AutoFixture, since it will now create a new instance of DanishPhoneNumber as part of the creation process when we ask for an anonymous Contact instance.

The only remaining issue is that by default, the number fed into the DanishPhoneNumber instance is likely to be considerably less than 112 – actually, if no other Int32 instances are created, it will be 1.

This will be a problem if we modify the DanishPhoneNumber constructor to look like this:

            public DanishPhoneNumber(int number)

{

    if ((number
< 112) ||

        (number > 99999999))

    {

        thrownewArgumentOutOfRangeException("number");

    }

    this.number
= number;

}

Unless a unit test has already caused AutFixture to previously create 111 other integers (highly unlikely), CreateAnonymous<Contact> is going to throw an exception.

This is easy to fix. Once again, the most obvious fix is to modify the creation algorithm for integers.

fixture.Register<int>(()
=> 12345678);

However, this will cause that particular instance of Fixture to return 12345678 every time you ask it to create an anonymous integer. Depending on the scenario, this may or may not be a problem.

A more targeted solution is to specifically address the algorithm for generating DanishPhoneNumber instances:

fixture.Register<int, DanishPhoneNumber>(i
=> 

    newDanishPhoneNumber(i
+ 112));

Here, I've even used the Register overload that automatically provides an anonymous integer to feed into the DanishPhoneNumber constructor, so all I have to do is ensure that the number falls into the proper range. Adding 112 (the minimum) neatly does the trick.

If you don't like the hard-coded value of 112 in the test, you can use that to further drive the design. In this case, we can add a MinValue to DanishPhoneNumber:

fixture.Register<int, DanishPhoneNumber>(i
=>

    newDanishPhoneNumber(i
+ 

        DanishPhoneNumber.MinValue));

Obvously, MinValue will also be used in DanishPhoneNumber's constructor to define the lower limit of the Guard Clause.

In my opinion, a good API should guide the user and make it difficult to make mistakes. In many ways, you can view AutoFixture as an exceptionally dim user of your API. This is the reason I really enjoyed receiving Klaus' original question: Like other TDD practices, AutoFixture drives better design.

When working with the ObjectContext in LINQ To Entities, a lot of operations are easily performed as long as you work with the same ObjectContext instance: You can retrieve entities from storage by selecting them; update or delete these entities and create new entities, and the ObjectContext will keep track of all this for you, so the changes are correctly applied to the store when you call SaveChanges.

This is all well and good, but not particularly useful when you start working with layered applications. In this case, LINQ To Entities is just a persistence technology that you (or someone else) decided to use to implement the Data Access Layer. A few years ago, I tended to implement my Data Access Components in straight ADO.NET; and a lot of people prefer NHibernate or similar tools – but I digress…

When LINQ To Entities is just an implementation detail of a service, lifetime management becomes important, so it is commonly recommended that any ObjectContext instance is instantiated when needed and disposed immediately after use.

This means that you will have a lot of detached entities in your system. Entities are likely to be returned to the calling code as interface, and when updating, a client will simply pass a reference to some implementation of that interface.

            public
            void CompleteAtSource(IRecord record)

Since we should always follow the Liskov Substitution Principle, we should not even try to cast the interface to an entity. Instead, we must populate a new instance of the entity in question with the correct data and save it.

That’s not hard, but since we are creating a new instance of an entity that represents data that is already in the database, we must attach it to the ObjectContext so that it can start tracking it again.

Now we are getting to the heat of the matter, because this is done with the AttachTo method, which is woefully inadequately documented.

At first, I couldn’t get it to work, and it wasn’t very apparent to me what I did wrong, so although the answer is very simple, this post might save you a bit of time.

This was my first attempt:

            using (MessageEntities store
= 

    newMessageEntities(this.connectionString))

{

    Message m
= newMessage();

    m.Id = record.Id;

    m.InputReference = record.InputReference;

    m.State = 2;

    m.Text = record.Text;

    store.AttachTo("Messages",
m);

    store.SaveChanges();

}

I find this approach very intuitive: Build the entity from the input parameter’s data, attach it to the store and save the changes. Unfortunately, this approach is wrong.

What happens is that when you invoke AttachTo, the state of the entity becomes Unchanged, and thus, not updated.

The solution is so simple that I’m surprised it took me so long to arrive at it: Simply call AttachTo right after setting the Id property:

            using (MessageEntities store
= 

    newMessageEntities(this.connectionString))

{

    Message m
= newMessage();

    m.Id = record.Id;

    store.AttachTo("Messages",
m);

    m.InputReference = record.InputReference;

    m.State = 2;

    m.Text = record.Text;

    store.SaveChanges();

}

You can’t invoke AttachTo before adding the Id, since this method requires that the entity has a populated EntityKey before it can be attached, but as soon as you begin updating properties after the call to AttachTo, the entity’s state changes to Modified, and SaveChanges now updates the data in the database.

That you have to follow this specific sequence when re-attaching data to the ObjectContext is poorly documented and not enforced by the API, so I thought I’d share this in case it would save someone else a bit of time.