In this post, I’m going to solve a logic puzzle using C# and F#. First, I’ll define the problem being solved and what our restrictions are. Next, I’ll show how I’d break down the problem and write an easy-to-read, extendable solution using idiomatic C#. Afterwards, I’ll solve the same problem and write an easy-to-read, extendable solution writing in idiomatic F#. Finally, we’ll compare the two solutions and see why the F# solution is the better solution.

The Problem

For this problem, I’m going to write a constraint solver (thanks to Geoff Mazeroff for the inspiration).

If you’re not familiar with the concept, a constraint is simply some rule that must be followed (such as all numbers must start with a 4). So a constraint solver is something that takes all the constraints and a source of inputs and returns all values that fit all the constraints.

With that being said, our source will be a list of positive integers and our constraints are the following:

  • 4 digits long (so 1000 – 9999)
  • Must be even (so 1000, 1002, 1004, etc…)
  • The first digit must match the last digit (2002, 2012, 2022, etc…)

To further restrict solutions, all code will be production ready. This includes handling error conditions (like input being null), being maintainable (easily adding more constraints) and easy to read.

To quickly summarize, we need to find a robust, maintainable, and readable solution to help us find all 4 digit number that are even and that the first and last digit are equal.

Implementing a Solution in C#

For the C# solution, I’m going to need a class for every constraint, a class to execute all constraints against a source (positive integers) and a runner that ties all the pieces together.

Starting with the smaller blocks and building up, I’m going to start with the constraint classes. Each constraint is going to take a single number and will return true if the number follows the constraint, false otherwise.

With that being said, I’d first implement the constraint that all numbers must be 4 digits long

Second, I’d write the constraint that all numbers must be even

Third, I’d implement the constraint that all numbers must have the same first digit and the last digit

At this point, I have the constraints written, but I need them to follow a general contract so that the Constraint Solver (about to be defined) can take a list of these constraints. I’ll introduce an interface, IConstraint and update my classes to implement that interface.

So now I have the constraints defined and they’re now implementing a uniform interface, I can now create the constraint solver. This class is responsible for taking the list of numbers and the list of constraints and then returning a list of numbers that follow all constraints.

Finally, I can put all the pieces together using LINQPad (Full C# solution can be found here).

This solution is easily extendable because if we need to add another constraint, we just add another class that implements the IConstraint interface and change the Main method to add an instance of the new constraint to the list of constraints.

Implementing a Solution in F#

Now that we have the C# solution, let’s take a look at how I would solve the problem using F#.

Similar to the C# solution, I’m going to create a function for every constraint, a function to execute all constraints against a source (positive integers) and a runner that ties all the pieces together.

Also similar to the C# solution, I’m going to start with creating the constraints and then work on the constraint solver function.

First, I’d implement that the number must be four digits long constraint.

Next, the number must be even constraint.

Lastly, the first digit is the same as the last digit constraint.

At this stage in the C# solution, I had to create an interface, IConstraint, so that the constraint solver could take a list of constraints. What’s cool with F# is that I don’t have to define the interface. The F# type inference is saying that each of these functions are taking the same input (some generic `a) and returning a bool, so I can add all of them to the list. This is pretty convenient since I don’t have to worry about this piece of plumbing.

Now that the different constraints are defined, I’d go ahead and write the last function that takes a list of constraints and a list of numbers and returns the numbers that the constraints fit. (Confused by this function? Take a look at Implementing your own version of # List.Filter)

Finally, all the pieces are in place, so I can now put all the pieces together using LINQPad (Full F# solution can be found here).

Comparing Both Solutions

Now that we have both solutions written up, let’s compare and see which solution is better.

First, the same design was used for both solutions. I decided to use this design for both because it’s flexible enough that we could add new constraints if needed (such as, the 2nd digit must be odd). As an example, for the C# solution, I’d create a new class that implemented IConstraint and then update the runner to use the new class. For the F# solution, I’d create a new function and update the runner. So, I’d think it’s safe to say that both solutions score about the same from a maintainability and extendability point of view.

From an implementation perspective, both solutions are production ready since the code handles possible error conditions (C# with null checks in the ConstraintSolver class, F# with none because it doesn’t support null). In addition to being robust, both solutions are readable by using ample whitespace and having all variables, classes, and interfaces clearly described.

With that being said, this is where the similarities end. When we look at how much code was written to solve the problem, we have a stark difference. For the C# solution, I ended up with 48 lines of code (omitting blank lines), however, for the F# solution, it only took 19. Now you could argue that I could have written the C# solution in fewer lines of code by removing curly braces around one line statements or ignoring null inputs. However, this would lead the code to be less robust.

Another difference between the F# solution and C# is that I was able to focus on the solution without having to wire up an interface. You’ll often hear developers talk about the how little plumbing you need for F# to “just work” and this small example demonstrates that point.

Another difference (albeit subtle) is that the F# solution is that I can use the findValidNumbers function with any generic list of values and any generic list of functions that take the generic type and return true/false.

In other words, if I had another constraint problem using strings, I’d still implement the different constraints, but I could use the same findValidNumbers function. At that point, however, I’d probably rename it to findValidValues to signify the generic solution.

What’s awesome about this is that I didn’t have to do any more work to have a generic solution, F# did that for me. To be fair, the C# solution can easily be made generic, but that would have to be a conscious design decision and I think that’s a downside.


In this post, we took a look at solving a number constraint problem by using idiomatic C# and F#. Even though both solutions are easy to read and easy to extend, the F# version was less than 1/2 the size of the C# solution. In addition, I didn’t have to do any plumbing for the F# version, but had to do some for the C# solution, and to top it off, the F# solution is generically solved, whereas the C# solution is not.


Welcome to the final installment of Establishing a SOLID Foundation series. In this post, we’ll be exploring the fifth part of SOLID, the Dependency Inversion Principle.

What is the Dependency Inversion Principle?

When working with object-oriented languages, we take large problems and break them down into smaller pieces. These smaller pieces in turn are broken down into even smaller, more manageable pieces to work on. As part of the breaking down process, we inherently have to introduce dependencies between the larger pieces and the smaller pieces.

How we weave these dependencies together is the difference between easily changing behavior and spending the next week pulling your hair out.

When working with classes, dependencies are usually introduced by constructing them in the class that they’re used in. For example, let’s say that we’ve been asked to write an utility that emulates an calculator but it also keeps a transaction log for record keeping purposes.

So far so good, we have two classes (Logger and Calculator) that is responsible for logging and the calculations. Even though this is a small amount of code (~50 lines for both classes), there are three dependencies in the code. The first dependency that I notice is that Calculator depends on Logger. We can see this by looking at the initialize method for Calculator (as hinted above):

The second dependency is a bit trickier to find, but in the Logger class’ log method, we use a hard coded file path.

The third dependency is probably the hardest to find, but in the the Logger class’ log method, we are also depending on the file system for the machine by using Ruby’s File class. But wait a minute, I hear you say, why is the File class considered a dependency, that’s a part of the Ruby language. I agree with you, but it’s still a dependency in the code and something that we should keep in mind.

From these three dependencies, we’re going to focus on resolving the first two. We could make resolve the dependency on the file system, but it would take so much effort for so little gain.

Why don’t we resolve the file dependency issue?

One thing that I keep in mind when identifying which dependencies to invert is to focus on inverting dependencies outside of the framework. In this case, we’re going to ignore the file system dependency because I, the programmer, depend on Ruby to behave correctly. If Ruby stops working, then a broken file system is the least of my worries. Therefore, it’s not worth the effort to resolve.

Making the Calculator and Logger more flexible

In order to resolve these DIP violations, we need to expose ways to drop in these dependencies. There are two ways of doing this. We can either:

  • Expose the dependency via the constructor
  • Expose the dependency via a public property

Using the Calculator example, we can expose the logger dependency via the constructor and we can expose the filePath dependency by exposing it as a property.

For the Calculator class, I’m going to first change the initialize method so that it takes a logger instead of constructing it’s own.

Next, I will construct the logger in the usage and pass the logger as part of the constructor.

A quick run of our program tells us that everything is still working correctly.

Now that we’ve finished up exposing the logger dependency, it’s time to expose the file path. First, I’m going to add a public property on the Logger class call filePath and use that property in the log method

Now that we’ve introduced a seam for the file path dependency, we use that seam in the program and assign the property

Multiple ways of solving the issue, which one is best?

When using the constructor method, it’s very clear to see what dependencies the class has, just look at the constructor. However, adding a new dependency to the constructor may cause other code to fail because the signature of the constructor has changed. This in turn can lead to cascading changes where multiple places of code need to be updated to pass in the dependency.

On the other hand, using the property method, the change is less invasive because the property can be set independently of the when the object was constructed. However, it’s harder to see the dependencies for the class because now the properties are containing the dependencies. Also, it’s very easy to forget to set a property before using the object.

Both of these methods are valid, but when I’m working with DIP, I prefer to expose the dependencies via the constructor because if my class starts to gain more and more dependencies, then it’s a sign that my class is doing too much and violating the Single Responsibility Principle (SRP). You can say that DIP is the canary in the coal mine for SRP.


In summary, the Dependency Inversion Principle (DIP) tells us that we should we should have the outside world pass in our dependencies. If we don’t follow this rule, then we will not. In order to resolve violations, we need to determine what dependencies we have and modify our constructor to accept those dependencies. If our constructor becomes too large, then our class might be violating the Single Responsibility Principle. By following the DIP, we expose the dependencies our classes require and allows for greater decoupling. As always, don’t forget to refactor as you go along.

All code shown/used can be found here on TheSoftwareMentor’s repository at Bitbucket

Establishing a SOLID Foundation Series


Welcome to the fourth installment of Establishing a SOLID Foundation series. In this post, we’ll be exploring the fourth part of SOLID, the Interface Segregation Principle and how by following this principle, you will write more robust code.

What is the Interface Segregation Principle?

The Interface Segregation Principle (ISP) tell us that clients should not be forced to use an interface that defines methods that it does not use. But what does this mean? Let’s say that we have the following ContactManager class and a ContactFinder class.

So far, so good, the ContactManager is responsible for holding onto the list of names and some basic methods and the ContactFinder is responsible for determining which contacts are in our list.

However, there is a problem with this example code. We have this ContactManager class that has a lot methods defined, but the only method that’s required is the DoesNameExist method.

Another concept ISP tells in a roundabout fashion is that objects should depend on interfaces, not concrete classes. This allows us to switch out dependencies much easier when we code to the interface.

What’s the big deal, I don’t see what the issue is

The big issue that comes up is that it’s hard to figure out what methods that the ContactFinder actually needs. We say that it needs a ContactManager which would lead us to assume that it needs all of those methods. However, that’s not the case. So by violating ISP, it’s easy to make the wrong design decision.

Another issue arises when it comes to creating the interface for the dependency. If we assume that all methods for the ContactManager is required, then any class that implements that interface has to also implement those unneeded methods. How many times have you seen an object implement an interface with a lot of methods that did nothing or just threw exceptions?

Fixing the issue

Alright, alright, I hear you say, you’ve convinced me, how do I fix this problem? The steps are pretty simple:

  1. First, you need to identify which methods the client needs
  2. Next, you need to create an interface that contains the methods that the client uses
  3. After creating the interface, have the dependency implement the interface
  4. Finally, change the signature client so that it uses the interface instead of the concrete class

Using our code base, first, we look at the ContactFinder class and see what methods from ContactManager that it uses.

So far, it looks like it only needs the DoesNameExist method. So let’s create an interface, called IContactSearcher that contains the single method.

Now that we’ve extracted the interface, it’s time to have the ContactManager class implement the interface:

Finally, we update the references in ContactFinder to use the IContactSearcher interface instead of the concrete class ContactManager.

With this last step, we’ve now resolved the ISP violation.


In summary, the Interface Segregation Principle (ISP) tells us that we should interfaces instead of concrete classes for our dependencies and that we should use the smallest interface for our client to work. If we don’t follow these rules, then it’s easy to create bloated interfaces that clutter up readability. In order to resolve violations, we need to determine what methods our client require and code an interface to contains those methods. Finally, we have our class implement those methods and pass it to the client. By following the ISP, we reduce the complexity required by our code and reduce the coupling between client and dependency. As always, don’t forget to refactor as you go along.

All code shown/used can be found here on TheSoftwareMentor’s repository at Bitbucket

Establishing a SOLID Foundation Series


Welcome to the third installment of Establishing a SOLID Foundation series. In this post, we’ll be exploring the third part of SOLID, the Liskov Substitution Principle and how following this principle can lead to loose coupling of your code.

So what is the Liskov Substitution Principle?

Before diving into the Liskov Substitution Principle (LSP), let’s look at a code example demonstrating the violation.

Let’s say that we have a Rectangle class:

If we run the following implementation, it’s pretty clear that it works like we would expect:

Seems pretty simple, we have a Rectangle class with two public properties, height and width and the class behaves the way we would expect.

Now let’s add a new class, called Square. Since all Squares are also Rectangles, it makes sense that the Square class should inherit from the Rectangle class. However, since Squares have to maintain the same height and width, we need to add some additional logic for that:

Using a Square instead of a Rectangle and running the same input, we get the following output:

Hold up, why is the area 25? If I read this code, then the height should be 10 and the width should be 5, giving an area of 50. This is no longer the case because of the domain constraint caused by the Square class. By using a Square where the code expected a Rectangle, we get different behavior then we would expect. This is the heart of the Liskov Substitution Principle.

In short, the Liskov Substituion Principle states that if we have an object (Rectangle) in our code and it works correctly, then we should be able to use any sub-type (Square) without the results being modified.

The most common example of LSP violations are when the “is-a” phrase from Object-Oriented Design break down. In the Rectangle-Square example, we say that a Square “is-a” Rectangle which is true. However, when we covert that relationship to code and use inheritance, the relationship does not hold up.

I don’t know, this sounds confusing, what’s the point?

To me, the Liskov Substitution Principle is the hardest part of SOLID to understand. It’s very heavy on the theoretical and it’s not blatantly obvious when a violation has occurred until testing.

However, there are plenty of benefits of following LSP.

First, following LSP reduces the tight coupling involved in your code. Let’s look back at our Recipes class from the Open/Closed Principle post and examine the MakeOrder method:

In this class, you see that we load different recipes and when one’s requested, we call the Cook method. We don’t have to do any set-up, special handling, or other logic, we just trust that the Cook method for whatever recipe we choose works as expected. By following this design, code will be easier to read and to maintain.

Going back to our Square/Rectangle example, if we wanted a method that would return a new Square or Rectangle, it would have to look something like this:

This code works, but there is one major problem. When someone is looking at this code, they’re going to get confused of why the Rectangle and Square are setup differently.

For example, when I see that the Square’s height is being set, but not the width, my first thought is that this is a bug. Then, I’d have to look into the Square’s class definition and then I’d see the logic of where setting the height also sets the width.

Long story short, by identifying and resolving LSP violations, we can make the code easier to read and maintain.

So it looks like LSP is pretty useful, but how do I fix violations?

Now that we’ve talked about spotting LSP violations and why it’s important to follow LSP, let’s discuss how to fix the violations.

To be honest, fixing a LSP violation is not easy. Since the nature of the problem is caused by a broken abstraction, discarding the abstraction is the best option. However, if you absolutely need to use the abstraction, then one solution is to remove the method that causes the violation.

In the Square/Rectangle example, we would remove the setters for height and width from our Rectangle class because that is how the violation can occur. After removing the setters, we need to modify the initialize method of Square to only take one parameter, size, and send that twice to the Rectangle class. Now, our classes look something like this:

With sample implementation and output


In short, the Liskov Substitution Principle (LSP) enforces the idea that if a class has a sub-type (through inheritance or interfaces), then by passing the sub-type, the program should still produce the same results. If you run across a class that violates LSP, then you know that your abstraction is not complete and you can either

  • Remove the offending methods/properties or
  • Abandon the abstraction

As always, don’t forget to refactor and reorganize your code as needed.

All code shown/used can be found here on TheSoftwareMentor’s repository at Bitbucket

Establishing a SOLID Foundation Series


Welcome to the second installment of Establishing a SOLID Foundation series. In this post, we’ll be exploring the second part of SOLID, the Open/Closed Principle and how following this principle can lead to great design choices.

So what is the Open/Closed Principle?

In order to set the context for discussion, let’s revisit our last example of the Chef class:

So it looks like this Chef is pretty simple, it only has one public method of CookFood and he can only cook ChickenWithBroccoli. However, a Chef that can only cook one thing isn’t very useful. So how about we add some more menu items?

So our new Chef can cook more food, but the code base expanded quite a bit. In order to add more menu choices, we need to add an additional if check in CookFood and to define a new method for CookFood to call. This might not sound like a lot of work because each of these Cook* methods just print something to the screen. However, what if the steps to create each food item were much more complex?

Also, what if we modified how the CookChickenWithBroccoli method worked? We would need to modify the Chef class, but that doesn’t make sense. In the real world, we would modify the recipe and the Chef would then follow the new recipe. This concept that we would have to modify an unrelated object in order to add new functionality is the inspiration for the Open/Closed Principle.

In short, the Open/Closed Principle means that an object should be Open for extension, but Closed for modification. This principle relies on the idea that new functionality is added by creating new classes, not by modifying pre-existing classes’ behavior. By doing this, we’re decreasing the chances of breaking current functionality and increasing code stability.

This sounds good, but is it worth the additional design time?

Now that we’ve discussed the Open/Closed Principle, you might be wondering what some of the benefits are of cleaning up this design.

First, classes that follow the Open/Closed Principle are small and focused in nature playing off the idea of the Single Responsibility Principle. Looking back at our Chef class, it’s very clear that by adding new functionality, Chef is going to be handling way too many things.

Next, by following OCP, there won’t be multiple classes modified just to add new functionality. There’s nothing like a change set containing tons of modified files to make even the most experienced developer shudder in fear.

By definition of OCP, we won’t be modifying a lot of files (ideally only one file should be modified) and we’re adding new classes. Since we’re adding in these new classes, we inherently have the opportunity to bake in testing.

Alright, I get it OCP is awesome, but how do I refactor the Chef class?

In order to fix the violation, we’re going to take each menu item and make them into their own class

Now that we have these different classes, we need to come up with some way for our Chef to interact with them. So why don’t we organize these menu items into a Recipes class?

Now we have this Recipes class that contains all of the menu items for our Chef to use. When adding new menu items to Recipes, all we have to add is the class in the initialize method and add an additional if check in the MakeOrder method. But hold on, I hear you say, This is the same as what we had with the Chef at the beginning, why is this design better? Before, we would have to modify the Chef in order to add more menu items which doesn’t really make sense, now we’ve moved that logic to Recipes which makes sense that it needs to be modified if a new menu item is added.

On the topic of our Chef, after cleaning up to use the Recipes class, our Chef is now very simple and relies on Recipes for the menu items, not itself:

Now that we’ve fixed the violation, let’s go ahead and refactor some. Looking at the menu choices, it’s pretty clear that we can abstract the behavior to a base class called MenuItem for them all to share (Note: By defining Cook by raising an exception, I’m forcing all classes to provide their own implementation).

Also, as part of this refactoring, we’re going to move some of the strings into constants as part of the RecipeNames module so that the Chef and Recipes can communicate with one another:

With these additions, let’s update the menu choices to use the module and the MenuItem base class:

With these changes, we need to update the Recipes class to use the RecipeNames module:

with this current layout, if we needed to add another menu item (let’s say Fish and Chips), we would need to:

  1. Create a new class that extends MenuItem called FishAndChips
  2. Add another string constant to RecipeNames
  3. Add another line in the Recipes initialize method to add it to the array


In short, the Open/Closed Principle (OCP) reinforces the idea that every class should be open for extension and closed to modifications. By following this principle, you’re much more likely to create separated code that allows you to increase functionality and decrease the odds of breaking current functionality. If you run across a class that is doing way too much, use the Single Responsibility Principle to separate the classes and then use a new object that serves as the middle man. In our case, the Recipes class was the middle man between the Chef and the different menu items. As always, don’t forget to refactor and reorganize your code as needed.

All code shown/used can be found here on TheSoftwareMentor’s repository at Bitbucket

Establishing a SOLID Foundation Series