Tags: , , , , , , , , , | Categories: Event Posted by bsstahl on 6/23/2017 8:10 AM | Comments (0)

The slide deck for my talk “A Developer’s Survey of AI Techniques” can be found below, while the demo code can be found on GitHub.



The talk explores some of the different techniques used to create Artificial Intelligences using the example of a Chutes & Ladders game.  Various AIs are developed using different strategies for playing a variant of the game, using different techniques for deciding where on the game board to move.

If you would like me to deliver this talk, or any of my talks, at your User Group or Conference, please contact me.

Tags: , , , , , , , | Categories: Event Posted by bsstahl on 5/7/2017 3:40 AM | Comments (0)

The slide deck for my presentation “Examples of Microservice Architectures” can be found here.

There isn't one clear answer to the question "what does a micro-service architecture look like?" so it can be very enlightening to see some existing implementations. In this presentation, we will look at 2 different applications that would not traditionally be thought of as candidates for a service-oriented approach. We'll look at how they were implemented and what benefits the micro-services architecture brought to the table for each application.

The demo code for my presentation on Testing in Visual Studio 2017 at the VS2017 Launch event can be found on GitHub.  There are 2 branches to this repository, the Main branch which holds the completed demo, and the DemoStart branch which holds the starting point of the demonstration in case you would like to implement the sample yourself.

The demo shows how Microsoft Fakes (formerly Moles) can be used to create tests against code that does not implement a reusable interface. This can be done  without having to resort to integration style tests or writing extra wrapper code just to implement an interface.  During my launch presentation, I also use this code to demonstrate the use of Intellitest (formerly Pex) to generate exploratory tests.

I really enjoy working with .NET Core.  I like the fact that my code is portable to many platforms and that the footprint is so much smaller than with traditional .NET applications.  Unfortunately, the tooling has not quite reached the level that we expect from a Microsoft finished product (which it isn’t – yet). As a result, there are some additional actions we need to take when setting up our solutions in Visual Studio 2015 to allow us to unit test our code properly.  The following are the steps that I currently take to setup and test a .NET Core library using XUnit and Moq.  I know that a number of these steps will be done for us, or at least made much easier, by the tooling in the coming months, either by Visual Studio 2017, or by enhancements to the Visual Studio 2015 environments.

  1. Create the library to be tested in Visual Studio 2015
    1. File > New Project > .Net Core > Class Library
    2. Notice that this project is created in a solution folder called ‘src’
  2. Create a solution folder named ‘test’ to hold our test projects
    1. Right-click on the Solution > Add > New Solution Folder
  3. Add a new console application to the test folder as our test project
    1. Right-click on the ‘test’ folder > Add > New Project > .Net Core > Console Application
  4. Add a reference to the library being tested in the test project
    1. Right-click on the test project > Add > Reference > Select the library to be tested
  5. Install packages needed for unit testing from NuGet to the test project
    1. Right-click on the test project > Manage NuGet Packages > Browse
    2. Install ‘xunit’ as our unit test runner
      1. The current version for .Net Core is ‘2.2.0-beta4-build3444’
    3. Install ‘dotnet-test-xunit’ to integrate xunit with the Visual Studio test tools
      1. The current version for .Net Core is ‘2.2.0-preview2-build1029’
    4. Install ‘Moq’ as our mocking library
      1. The current version for .Net Core is ‘4.6.38-alpha’
  6. Edit the project.json of the test library
    1. Change the “EmitEntryPoint” option to false
    2. Add “testrunner” : “xunit” node

Some other optional steps include:

  • Install the ‘Microsoft.CodeCoverage’ package from NuGet to enable the code coverage tooling
  • Install the ‘Microsoft.Extension.DependencyInjection’ package from NuGet to enable DI
  • Install the ‘TestHelperExtensions’ package from NuGet to add extensions that assist with writing good unit tests
  • Add any additional runtimes that might be needed. Some options are:
    • win10-x86
    • win10-x64
    • win7-x86
    • win7-x64
  • Set ‘Run tests after build’ in Visual Studio so tests run automatically

There will likely be better ways to do many of these things shortly, but if you know a better way now, please let me know via Twitter.

One of the techniques I recommend highly in my Simplify Your API talk is the use of extension methods to hide the complexity of lower-level API functionality.  A good example of a place to use this methodology came-up last night in a great Reflection talk by Jeremy Clark (Twitter, Blog) at the NorthWest Valley .NET User Group


Jeremy was demonstrating a method that would spin-through an assembly and load all classes within that assembly that implemented a particular interface.  The syntax to do the checks on each type were just a bit more obtuse than Jeremy would have liked them to be.  As we left that talk, I only half-jokingly told Jeremy that I was going to write him an extension method to make that activity simpler.  Being a man of my word, I present the code below to do just that.

Tags: , , , , , , | Categories: Development Posted by bsstahl on 10/16/2016 2:11 AM | Comments (0)

The slide deck for my presentation on Optimization for Developers (A Developer’s Guide to Finding Optimal Solutions) can be found here.  I hope that if you attended one of my code camp sessions on the topic, you enjoyed it and found it valuable.  I am happy to accept any feedback via Twitter.

Tags: , , , , , , | Categories: Development Posted by bsstahl on 7/1/2016 10:49 PM | Comments (0)

Dynamic Programming (DP) is a mathematical tool that can be used to efficiently solve certain types of problems and is a must-have in any software developer's toolbox. A lot has been written about this process from a mathematician's perspective but there are very few resources out there to help software developers who want to implement this technique in code. In this article and the companion conference talk "Dynamic Optimization - One Algorithm All Programmers Should Know", I attempt to demystify this simple tool so that developer's can implement it for their customers.

What is Combinatorial Optimization?

Mathematical or Combinatorial Optimization is the process of finding the best available solution to a problem by minimizing or eliminating undesirable factors and maximizing desirable ones.  For example, we might want to find the best path through a graph that represents the roads and intersections of our city.  In this case, we might want to minimize the distance travelled, or the estimated amount of time it will take to travel that distance.  Other examples of optimization problems include determining the best utilization of a machine or device, optimal assignment of scarce resources, and a spell-checker determining the most likely word being misspelled.

We want to make sure that we do not conflate combinatorial optimization with code optimization.  It is certainly important to have efficient code when running an optimization algorithm, however there are very different techniques for optimizing code than for optimizing the solution to a problem. Code optimization has to do with the efficiency of the implementation whereas combinatorial optimization deals with the efficiency of the algorithm itself.  Efficiency in both areas will be critical for solving problems in large domains.

What is Dynamic Programming?

Ultimately, DP is just a process, a methodology for solving optimization problems that can be defined recursively1.  It is really about a way of attacking a problem that, if it were addressed naïvely, might not produce the best possible answer, or might not even converge to a solution in an acceptable amount of time.  Dynamic Programming provides a logical approach to these types of problems through a 2-step process that has the effect of breaking the problem into smaller sub-problems and solving each sub-problem only once, caching the results for later use2.

The steps in the process are as follows:

  1. Fill out the cache by determining the value of each sub-problem, building each answer based on the value of the previous answers
  2. Use the values in the cache to answer questions about the problem

Since we fill-out the entire cache for each problem3, we can be 100% certain that we know what the best possible answers to the questions are because we have explored all possibilities.

Dynamic Programming in Action

Let's look at one of the canonical types of problems that can be solved using Dynamic Programming, the knapsack problem.  A knapsack problem occurs in any situation where you have a limited capacity that can be consumed by a number of different possible options.  We need to look for the best fit and optimize for the maximum based on the definition of value in our problem.  This class of problem gets its name from the story of the archeologist in the collapsing ruin.  She has a knapsack that can hold a known weight without tearing and she needs to use it to rescue artifacts from the ruin before it collapses entirely.   She wants to maximize the value of artifacts she can save, without exceeding the capacity of her knapsack, because it would then tear and she wouldn't be able to carry anything.

We can solve this type of problem using Dynamic Programming by filling-out a table that holds possible capacities, from 0 to the capacity of our known knapsack, and each of the possible items to use to fill that space, as shown below.

In this example, there are 3 items with weights of 4, 5 and 2.  These items have values of 5, 6 and 3 respectively and can be placed in a knapsack with capacity of 9. The leftmost column of the table represents the capacities of knapsacks from 0, up to and including the capacity of our knapsack.  The next column represents the best value we would get in the knapsack if we had the option of putting 0 items in our knapsack. The next, the best value if we had the option of taking the 1st item, the next column, the option to take the 2nd item on top of any previous items, and so forth until we complete the table.  As you can see, the most value we can get in our knapsack with the option of picking from these 3 items is 11, as found in the last row of the last column. That is, the cell that represents a knapsack with our known capacity, with the option to chose from all of the items.

To calculate each of these cells, we build on the values calculated earlier in the process.  For the 1st column, it is easy. If we can chose no items, the value of the items in our knapsack is always 0. The rest of the cells are calculated by determining the greater of the following 2 values:

  • The value if we didn't take the current item, which is always the value of the same capacity knapsack from the previous column
  • The value if we took the current item, which is the value of the current item, added to the value of the knapsack from the previous column if the weight of the current item were removed

So, for the cell in the column labeled "1" with a knapsack capacity of 6, we take the greater of:

  • 0, since we wouldn't have any items in  the knapsack if we chose not to take the item
  • 5, the value of the current item, added to the value of the other items in the knapsack, which was previously empty

For the cell in column "2" with a knapsack capacity of 9, we take the greater of:

  • 5, which is the value of the knapsack with capacity 9 from column "1" indicating that we didn't take the 2nd item
  • 11, which is the value of the current item added to the best value of the knapsack with capacity 4 (subtract the weight of our current item from the capacity of the current knapsack) with the option of taking only the previous items.

Each cell in the table can be filled out by doing these simple calculations, 1 addition and 1 comparison, using the values previously calculated as shown in the annotated table below.

So we've filled out the table and know, from the cell in the bottom right that the maximum value we can get from this knapsack with these items is 11. Great, but that only answers the question of maximum value, it doesn't tell us which items are chosen to achieve this value.  To determine that, we need to work backward from the known best value.

Starting at the known best value in the bottom-right cell, we can look one cell to the left to see that the value there is the same.  Since we know that taking an item would increase the value of the knapsack, we can know that we must not have chosen to take the item in the last column.  We can then repeat the process from there.  From the bottom cell in the column labeled "2", we can look left and see that the value in the previous column did change, so we know we need to take the item in column "2" to get our maximum value.  Since we know that item 2 had a weight of 5, we can subtract that from the capacity of our knapsack, and continue the process from that point, knowing that we now only have 4 more units of capacity to work with.  Comparing the item in the column labeled "1" and a knapsack capacity of 4 with the value of the equivalent knapsack in column "0", we can see that we need to include item 1 in our knapsack to get the optimum result.


What did we actually do here?

There is no magic here. All we did was take a problem that we could describe in a recursive way, and implement a process that used easy calculations that built upon the results of previous calculations, to fill-out a data cache that allowed us to answer the two primary questions of this problem:

  1. What is the maximum value of the knapsack with capacity 9 and the option to take the 3 previously described items up to the capacity of the knapsack?
  2. Which items of the 3 do we need to take to achieve the maximum value described in question

You can probably see that if both axes of this table, the capacity of the knapsack, and the number of items we can chose from, are extremely large, we may run into memory or processing-time constraints when implementing this solutions.  As a result, this may not be the best methodology for solving problems where both the capacity of the knapsack and the number of items is extremely high.  However, if either is a reasonable number, Dynamic Programming can produce a result that is guaranteed to be the optimum solution, in a reasonable amount of time.

Continue the Conversation

I am happy to answer questions or discuss this further. Ping me on Twitter with your comments or questions. I'd love to hear from you.  I am also available to deliver a talk to your conference or user group on this or other topics. You can contact me via my blog, Cognitive Inheritance.


1 In mathematical terms, DP is useful for solving problems that exhibit the characteristics of Overlapping Subproblems and Optimal Substructure.  If a problem is able to be described recursively, it will usually exhibit these traits, but the use of the recursion concept here is a generalization to put the problem in software developer's terms.

2 The process of storing a value for later use is known in mathematics as memoization, an operation which, for all intents and purposes, is equivalent to caching.

3 Variants of certain DP algorithms exist where the process can be cut-off under certain conditions prior to fully populating the cache.  These variants are not discussed here.

Tags: , , , , , , , , , , | Categories: Development Posted by bsstahl on 10/27/2015 3:43 PM | Comments (0)

I don't create collection objects anymore.

I know, I know. I was they guy always preaching that every entity that was being collected had to have its own collection object. It was the right thing at the time; if you needed to take an action on an enumeration or list of objects, those actions needed to be done within a strongly-typed collection object to maintain encapsulation. Even if all that was happening was that an inherited List<T> function was being called, that functionality needed to be called on the TCollection object because, if it wasn't, it was likely that the next time logic needed to be performed on the collection, there wouldn't be a place to put it. Collection logic would end up being spread-out around your code rather than encapsulated in the collection. It was also possible that the implementation might change and need to be updated everywhere, instead of in one place.

Today however, that has all changed. Extension methods now allow us, at any time, to add functionality to ICollection<T>, IList<T>, IEnumerable<T> or any other interface or class. We can attach our list or enumeration based actions directly to the list or enumeration class, and do so at any time, since the methods appear the same to the developer as methods directly on the collection type. Thus, the "no place to put it" fear no longer exists. I've even started using this technique for my factory methods to make it clear that what I am creating is, in fact, an IEnumerable<T>, as shown below.

    var stations = (null as IEnumerable<Station>).Create();
    var localStations = stations.GetNearby(currentLocation);

In this example, both the Create and GetNearby methods are extension methods found in a static class called StationExtensions.

So, the big advantage here is that these methods can be added anytime, meaning we don't need to create an object that we MAY need in the future. This is better adherence to the YAGNI principle so it is a better pattern to follow. But what about disadvantages? Does it hurt us in any way to perform our collection actions this way? I'm not comfortable answering that question with an absolute "no" yet because I don't think I've been using this technique long enough to have covered enough ground with it, but I can certainly say that I haven't found any disadvantages yet. It seems like these extension methods are basically perfect for this type of activity. These methods do everything that the methods of a collection object do, can (and should) be put in a separate module to keep the code together, can be navigated to by Visual Studio in the same way as other methods, and have the same access (private, internal, public) restrictions that collection objects have. About the only thing I can say that is not 100% positive about using these techniques is that the (null as IEnumerable<T>) syntax to create a local variable instance to call the class factory from is not quite as elegant as I'd like it to be.

So you tell me, do you still create collection objects? Have you found any reason why using extension methods in this way is not as good as putting those methods into a strongly-typed collection? Sound off on Twitter and let's talk about it.

Tags: , , , , , , , , | Categories: Event Posted by bsstahl on 10/22/2015 2:19 AM | Comments (0)

I hope you’ve had an opportunity to see my presentation, “Dynamic Optimization – One Technique all Programmers Should Know” at a Code Camp or User Group near you.  If so, and you want to have a copy of the slide deck for your very own, you can see it embedded below, or use the direct link to the Powerpoint here

The subject of this presentation is using a technique called Dynamic Programming to solve problems that have more than one possible solution.  This technique works very well when used to solve problems that are recursive in nature.  One of the best things about this technique is that it guarantees that the solution it produces is the best possible solution.

We look at three examples during the presentation, the first is done only “on paper” and is an example of using this technique to solve a knapsack problem.  The second example is done in pseudo-code and solves a linear best-path problem in the game of Chutes & Ladders.  Finally, we drop into Visual Studio to solve a 2-dimensional best-path problem.  Sample code for both of the last 2 examples can be found in GitHub.

Keep an eye on my Speaking Engagements Page for opportunities to see this presentation live. If you are a user group or conference organizer, you can contact me to schedule an in-person presentation.  This presentation is a lot of fun to deliver and has been received extremely well at Code Camps and User Groups across the country.

Tags: , , , , , , , , , , | Categories: Development Posted by bsstahl on 10/12/2015 10:15 PM | Comments (0)

If you are building an API for other Developers to use, you will find out two things very quickly:

  1. Developers don't read documentation (you probably already know this).
  2. If your API depends on its documentation to get developers to understand and discover its features, it is likely that it will not be used.

Fortunately, there are some simple mechanisms for wrapping complex APIs and making their functionality both easy to use, and highly discoverable. An API that uses tools like IntelliSense in Visual Studio to make its features discoverable by the downstream developer is far more likely to be adopted then one that doesn't. In recent years, additions to the C# language have made creating a Domain Specific Language that uses a fluent syntax for nearly any API into a simple process.

Create the Context

The 1st step in simplifying any API is to provide a single starting point for the downstream developer to interact with. In most cases, the best practice is to use the façade pattern to define a context that holds our entity collections. Each collection of entities becomes a property on the context object. These properties all return an IQueryable<Entity>. For example, in the EnumerableStack demo solution on GitHub (https://github.com/bsstahl/SimpleAPI), I created an object Bss.EnumerableStack.Data.EnumerableStack to provide this functionality. It has two properties, Posts and Questions, each of which returns an IQueryable<Post>. It is these properties that will be used to access the data from our API.

The context object, on top of becoming the single point of entry for downstream developers, also hides any complexities in the construction logic of the underlying data source. That is, if there is any configuration or other setup required to access the upstream data provider (such as web service access or database connections), much of the complexity of that construction can be hidden from the API user. A good example of this can be seen in the FluentStack demo solution from the same GitHub repository. There, the Bss.FluentStack.Data.OData.FluentStack context object wraps the functionality of constructing the connection to the StackOverflow OData web service.

Extend Our Language

Now that we have data to access, it's time for us to extend our domain specific language to provide tools to make accessing this data simpler for the API caller. We can use Extension methods on IQueryable<Entity> to create custom filters for our data. By creating extension methods that accept IQueryable<Entity> as a parameter and return the same, we can create methods that can be chained together to form a fluent syntax that will perform complex filtering. For example, in the EnumerableStack solution , the Questions, WithAcceptedAnswer and TaggedWith methods found in the Bss.EnumerableStack.Data.Extensions module, can all be used to execute queries on the data exposed by the properties of our context object, as shown below:

var results = new EnumerableStack().Posts.WithAcceptedAnswer().TaggedWith("odata");

In this case, both the WithAcceptedAnswer and TaggedWith filters are applied to the data. The best part about these methods are that they are visible in Intellisense (once the namespace has been brought into scope with a Using statement) making the functionality easy to discover and use.

Another big advantage of creating these extension methods is that they can hide the complexity of the lower level API. Here, the WithAcceptedAnswer method is wrapping a where clause that filters for those posts that have an AcceptedAnswerId property that is non-null. It may not be obvious to a downstream API consumer that the definition of a post with an "accepted answer" is one where the AcceptedAnswerId has a value. Our API hides that implementation detail and allows the consumer to simply request what is needed. Similarly, the TaggedWith method hides the fact that the StackOverflow API stores tags in lower-case, within angle-brackets, and with all tags on a post joined into a single string. To search for tags, the consumer would need to know this, and take all appropriate actions when searching for a tag if we didn't hide that complexity in the TaggedWith method.

Simplify Query Predicates

A predicate is a function that accepts an entity as a parameter, and returns a boolean value. These functions are often used in the Where clause of a query to indicate which objects should be included in the result set. For example, in the query below

var results = new EnumerableStack().Posts.Where(p => p.Parent == null);

the function expression p => p.Parent == null is a predicate that returns true if the Parent property of the entity is null. For each entity passed to the function, the value of that property is tested, and if null, the entity is included in the results of the query. Here we are using a Lambda Expression to provide a delegate to our function. One of the coolest things about Linq is that we can now represent this expression in a variable of type Expression<Func<Entity, bool>>, that is, a Lambda expression of a function that takes an Entity and returns a boolean. This is pretty awesome because if we can store it in a variable, we can pass it around and enable extension methods like this one, as found in the Asked class of the Bss.EnumerableStack.Data library:

public static Expression<Func<Post, bool>> InLast(TimeSpan span)
   return p => p.CreationDate > DateTime.UtcNow.Subtract(span);

This method accepts a TimeSpan object and returns the Lambda Expression type useable as a predicate. The input TimeSpan is subtracted from the current DateTime UTC value, and compared to the CreationDate property of a Post entity. If the creation date of the Post is later than 30-days prior to the current date, the function returns true. Since this InLast method is static on a class called Asked, we can use it like this:

var results = new EnumerableStack().Questions.Where(Asked.InLast(TimeSpan.FromDays(30));

Which will return questions that were asked in the last 30 days. This becomes even simpler to understand if we add a method extending Int called Days that returns a Timespan, like this:

public static TimeSpan Days(this int value)
   return TimeSpan.FromDays(value);

allowing our expression to become:

var results = new EnumerableStack().Questions.Where(Asked.InLast(30.Days());

Walking through the Process

In my conference sessions, Simplify Your API: Creating Maintainable and Discoverable Code, I walk through this process on the FluentStack demo code. We take a query created against the StackOverflow OData API that starts off looking like this:

var questions = new StackOverflowService.Entities(new Uri(_serviceRoot))
   .Posts.Where(p => p.Parent == null && p.AcceptedAnswerId != null
   && p.CreationDate > DateTime.UtcNow.Subtract(TimeSpan.FromDays(30))
   && p.Tags.Contains("<odata>"));

and convert it, one step at a time, to this:

var questions = new FluentStack().Questions.WithAcceptedAnswer().

a query that is much simpler, easier to understand, easier to create and easier to maintain. The sample code on GitHub, referenced above, and available at http://github.com/bsstahl/SimpleAPI, contains the FluentStack.sln example which shows how to simplify an API created with an OData source. It also contains the EnumerableStack.sln project which walks through the same process on a purely enumerable data source, that is, an implementation that will work with any collection.

Sound Off

Have you used these tools to simplify an API for downstream programmers? Do you have other techniques that you use to do the same, similar, or additional things to make your APIs better? If so, Tweet it to me @bsstahl and let's keep the conversation going.