DDD North 2015 In Review

On Saturday 24th October 2015, DDD North held its 5th annual Developer Developer Developer event.  This time the event was held in the North-East, at the University of Sunderland.

As is customary for me now, I had arrived the evening before the event and stayed with family in nearby Newcastle-Upon-Tyne.  This allowed me to get to the University of Sunderland bright and early for registration on the morning of the event.

After checking in and receiving my badge, I proceeded to the most important area of the communal reception area, the tea and coffee urns!  After grabbing a cup of coffee and waiting patiently whilst further attendees arrived, there was soon a shout that breakfast was ready.  Once again, DDD North and the University of Sunderland provided us all with a lovely breakfast baguette, with a choice of either bacon or sausage.  

After enjoying my bacon baguette and washing it down with a second cup of coffee, it was soon time for the first session of the day. The first session slot was a tricky one, as all of the five tracks of sessions appealed to me, however, I could only attend one session, so decided somewhat at the last minute it would be Rik Hepworth’s The ART of Modern Azure Deployments.

The main thrust of Rik’s session is to explain Azure Resource Templates (ART). Rik says he’s going to explain the What, the Why and the How of ART’s. Rik first reminds us that every resource in Azure (from virtual networks, to storage accounts, to complete virtual machines) is handled by the Azure Resource Manager.  This manager can be used and made to perform the creation of resources in an ad-hoc manner using numerous fairly arcane PowerShell commands, however, for repeatability in creating entire environments of Azure resources, we need Azure Resource Templates.

Rik first explains the What of ART’s.  They’re quite simply a JSON format document that conforms to the required ART schema.  They can be split into multiple files, one which supplies the “questions” (i.e. the template of the required resource – say a virtual network) and the other file can supply the “answers” to fill-in-the-blanks of the question file. (i.e. the parameterized IP address range of the required virtual network).  They are idempotent too, which means that the templates can be run against the Azure Resource Manager multiple times without fear of creating more resources than are required or destroying resources that already exist.

Rik proceeds with the Why of ART’s.   Well, firstly since they’re just JSON documents and text files, they can be version controlled.  This fits in very nicely with the “DevOps” culture of “configuration as code”, managed and controlled in the same way as our application source code is.  And being JSON documents, they’re much easier to write, use and maintain than large and cumbersome PowerShell scripts composed of many PowerShell commands with difficult to remember parameters.  Furthermore, Rik tells us that, eventually, Azure Resource Templates will be the only way to manage and configure complete environments of resources within Azure in the future.

Finally, we talk about the How of ART’s.  Well, they can be composed with Visual Studio 2013/2015. The only other tooling required is the Azure SDK and PowerShell.  Rik does mentions some caveats here as the Azure Resource API – against which the ART’s run – is currently moving and changing at a very fast pace.  As a result of this, there’s frequent updates to both the Azure SDK and the version of PowerShell needed to support the latest Azure Resource API version.  It’s important to ensure you keep this tooling up-to-date and in sync in order to have it all work correctly.

Rik goes on to talk about how monitoring of running the resource templates has improved vastly.  We can now monitor the progress of a running (or previously run) template file from portal.azure.com and resource.azure.com, which is the Resource Manager in the Azure portal.  This shows the complete JSON of the final templates, which may have consisted of a number of “question” and “answer” files that are subsequently merged together to form the final file of configuration data.  From here, we can also inspect each of the individual various resources that have been created as part of running the template, for example, virtual machines etc.

Rik then mentions something called DSC.  This is Desired State Configuration.   This is now an engineering requirement for all MS products that will be cloud-based.  DSC effectively means that the “product” can be entirely configured by declarative things such as scripts, command line commands and parameters. etc.  Everything can be set and configured from here without needing to resort to any GUI.

Rik talks about how to start creating your own templates.  He says the best place to start is probably the Azure Quickstart Templates that are available from a GitHub repository.  They contain both very simple templates to ease you into getting started with something simple, but also contain some quite complex templates which will help you should you need to create a template to deploy a complete environment of numerous resources.  Rik also mentions that next year will see the release of something called the “Azure Stack” which will make it even easier to create scripts and templates that will automate the creation and management of your entire IT infrastructure, both in the cloud and on-premise, too.

As well as supporting basic parameterization of values within an Azure Resource Template, you can also define entire sections of JSON that define a complete resource (i.e. an entire virtual machine complete with an instance of SQL Server running on it).  This JSON document can then be referenced from within other ART files, allowing individual resources to be scripted once and reused many times.  As part of this, Azure resources support many different types of extensions for extending state configuration into other products.  For example, there is an extension that allows an Azure VM to be created with an Octopus Deploy tentacle pre-installed, as well as an extension that allows a Chef client to be pre-installed on the VM, for example.

Rik shows us a sample layout of a basic Azure Resource Template project within Visual Studio.  It consists of 3 folders, Scripts, Templates and Tools.  There's a blank template in the template folder and this defines the basic "shape" of the template document.  To get started within a simple template for example, a Windows VM needs a Storage account (which can be an existing one, or can create new) and a Virtual Network before the VM can be created.

We can use the GUI tooling within Visual Studio to create the basic JSON document with the correct properties, but can then manually tweak the values required in order to script our resource creation.  This is often the best way to get started.  Once we’ve used the GUI tooling to generate the basics of the template, we can then remove duplication by "collapsing" lots of the properties and extracting them into separate files to be included within the main template script.  (i.e. deploy location is repeated for each and every VM.  If we’re deploying multiple VMs, we can remove this duplication by extracting into a separate file that is referenced by each VM).

One thing to remember when running and deploying ART’s, Rik warns us, is that the default lifetime of an Azure Access Token is only 1 hour.  Azure Access Tokens are required by the template in order to prove authorisation for creating Azure resources.  However, in the event that the ART is deploying a complete environment consisting of numerous resources, this can be a time-consuming process – often taking a few hours.  For this reason, it’s best to extend the lifetime of the Azure Access Tokens, at least during development of the templates, otherwise the tokens will expire during the running of the template, thereby making the resource creation fail.

Rik wraps us with a summary, and opens the floor to any questions.  One question that is posed is whether existing Azure Resources can be “reverse-engineered” to ART scripts.  Rik states that so long the existing resources are v2 resources (that have been created with Azure Resource Manager) then you can turn these resources into templates BUT, if existing resources are V1 (also known as Classic resources and created using the older Azure Service Management) they can't be reverse-engineered into templates.

After a short coffee break back in the main communal area, it’s time for the second session of the day.  For this session, I decided to go with Gary Short’s Deep Dive into Deep Learning.

Gary’s session was all about the field of data science and of things like neural networks and deep learning.   Gary starts by asking who knows what Neural Networks are and asks what Deep Learning is and the difference between them.  Not very many people know the difference here, but Gary assures us that by the end of his talk, we all will.

Gary tells us that his talk’s agenda is about looking at Neural Networks, being the first real mechanism by which “deep learning” was first implemented, but how today’s “deep learning” has improved on the early Neural Networks.  We first learn that the phrase “deep learning” is itself far too broad to really mean anything!

So, what is a Neural Network?  It’s a “thing” in data science.  It’s a statistical learning model and can be used to estimate functions that can depend on a large number of inputs.  Well, that’s a rather dry explanation so Gary gives us an example.  The correlation between temperature over the summer months and ice cream sales over the summer months.  We could use a Neural Network to predict the ice cream sales based upon the temperature variance.  This is, of course, a very simplistic example and we can simply guess ourselves that as the temperature rises, ice cream sales would predictably rise too.  It’s a simplistic example as there’s exactly one input and exactly one output, so it’s very easy for us to reason about the outcome with really relying upon a Neural Network.  However, in a more realistic example using “big data”, we’d likely have hundreds if not many thousands of inputs for which we wish to find a meaningful output.

Gary proceeds to explain that a Neural Network is really a weighted directed graph.  This is a graph of nodes and the connections between those nodes.  The connections are in a specific direction, from one node to another, and that same node can have a connection back to the originating node.  Each connection has a “weight” or a probability.  In the diagram to the left we can see that node A has a connection to node E and also a separate connection to node F.  The “weight” of the connection to node F is 0.9 whilst the weight of the connection to node E is 0.1.  This means there a 10% chance that a message or data coming from node A will be directed to node E and a 90% chance that a message coming from node A will be directed to node F.  The overall combination of nodes and connections between the nodes overall gives us the Neural Network.

Gary tells us how Neural Networks are not new, they were invented in 1943 by two mathematicians, Warren McCulloch and Walter Pitts.  Back then, they weren’t referred to as Neural Networks, but were known as “Threshold Logic”.  Later on, in the late 1940's, Donald Hebb created a hypothesis based on "neural plasticity" which is the ability of a Neural Network to be able to “heal itself” around “injuries” or bad connections between nodes.  This is now known as Hebbian Learning.  In 1958, mathematicians Farley and Wesley A. Clark used a calculator to simulate a Hebbian Machine at MIT (Massachusetts Institute of Technology).

So, just how did today’s “Deep Learning” improve upon Neural Networks.  Well, Neural Networks originally had two key limitations.  Firstly, they couldn't process exclusive or (XOR) logic in a single-layer network, and secondly, computers (or rather calculators) simply weren't really powerful enough to perform the extensive processing required.  Eventually, in 1975, a mathematician named Werbos discovered something called “back propagation”, which is the backwards propagation of error states allowing originating nodes to learn of errors further down a processing chain and perform corrective measures (self-learning) to mitigate further errors.  Back propagation helped to solve the XOR problem.  It was only through the passage of a large amount of time, though, that yesterday’s calculators became today’s computers – which got ever more powerful with every passing year – and allowed Neural Networks to come into their own.  So, although people in academia were teaching the concepts of Neural Networks, they weren’t really being used, preferring instead alternative learning mechanisms like “Support Vector Machines” (SVM) which could work with the level of computing that was available at that time.  With the advent of more powerful computers, however, Neural Networks really started to take off after the year 2000.

So, as Neural Networks started to get used, another limitation was found with them.  It took a long time to “train” the model with the input data.  Gary tells us of a Neural Network in the USA, used by the USPS (United States Postal Service) that was designed to help recognise hand-written zip codes.  Whilst this model was effective at it’s job, it took 3 full days to train the model with input data!  This had to be repeated continually as new “styles” of hand-writing needed to be recognised by the Neural Network.

Gary continues by telling us that by the year 2006, the phrase “deep learning” had started to take off, and this arose out of the work of two mathematicians called Geoffrey Hinton and Ruslan Salakhutdinov and showed that many-layered, feed-forward Neural Networks could be trained far more effectively, thus reducing the time required to train the network.  So really, “deep learning” is really just modern day Neural Networks, but ones that have been vastly improved over the original inventions of the 1940’s. 

Gary talks about generative models and stochastic models.   Generative models will “generate” things in a random way, whilst stochastic model will generate things in an unpredictable way. Very often this is the very same thing.  It’s this random unpredictability that exists in the problem of voice recognition.  This is now a largely “solved” problem from around 2010.  It’s given rise to Apple’s Siri, Google’s Google Now and most recently, and apparently most advanced, Microsoft’s Cortana.

At this point, Gary shows us a demo of some code that will categorise Iris plants based upon a diverse dataset of a number of different criteria.  The demo is implemented using the F# language, however, Gary states that the "go to" language for Data Science is R.  Gary says that whilst it’s powerful, it not a very nice language and this is primarily put down to the fact that whilst languages like C, C#, F# etc. are designed by computer scientists, R is designed by mathematicians.  Gary’s demo can use F# as it has a “type provider” mechanism which allows it to “wrap” and therefore largely abstract away the R language.  This can be downloaded from NuGet and you’ll also need the FsLab NuGet package.

Gary explains that the categorisation of Irises is the canonical example for data science categorisation.  He shows the raw data and how the untrained system initially thinks that there are three classifications of irises when we know there's only really two.  Gary then explains that, in order to train our Neural Network to better understand our data, we need to start by "predicting the past".  This is simply what is says, for example, by looking at the past results of (say) football matches, we can use that data to to help predict future results.

Gary continues and shows how after "predicting the past" and using the resulting data to then train the Neural Network, we can once again examine the original data.  The graph this time is correctly showing only two different categorisations of irises.  Looking closer at the results and we can see that of a data set that contains numerous metrics for 45 different iris plants, our Neural Network was able to correctly classify 43 out of the 45 irises, with only two failures.  Looking into the specific failures, we see that they were unable to be classified due to their data being very close between the two different classifications.  Gary says how we could probably “fine tune” our Neural Network by looking further info the data and could well eradicate the two classification failures.

After Gary’s session, it’s time for another tea and coffee break in the communal area, after which it’s time for the 3rd and final session before lunch.  There had been a couple of last-minute cancellations of a couple of sessions due to speaker ill health, and one of those sessions was unfortunately the one I had wanted to attend in this particular time slot, Stephen Turner’s “Be Reactive, Think Reactive”.  This session was rescheduled with Robert Hogg delivering a presentation on Enterprise IoT (Internet of Things), however, the session I decided to attend, was Peter Shaw’s Microservice Architecture, What It Is, Why It Matters And How To Implement It In .NET.

Peter starts his presentation with a look at the talk’s agenda.   He’s going to define what Microservices are and their benefits and drawbacks.  He’ll explain how, within the .NET world, OWIN and Katana help us with building Microservices, and finally he is going to show a demo of some code that uses OWIN running on top of IIS7 to implement a Microservice.

Firstly, Peter tells us that Microservices are not a software design pattern, they’re an architectural pattern.  They represent a 100-foot view of your entire application, not the 10-foot view, and moreover, Microservices provide a set of guidelines for deployment of your project.

Peter then talks about monolithic codebases and how we scale them by duplicating entire systems.  This can be wasteful when you only need to scale up one particular module as you’ll end up duplicating far more than you need.  Microservices is about being able to scale only what you need, but you need to find the right balance of how much to break down the application into it’s constituent modules or discreet chunks of functionality.  Break it down too much and  you'll get nano-services – a common anti-pattern - and will then have far too much complexity in managing too many small parts.  Break it down too little, and you’re not left with Microservices.  You’ve still got a largely monolithic application, albeit a slightly smaller one.

Next, Peter talks about how Microservices communicate to each other.  He states how there’s two schools of thought to approaching the communication problem.  One school of thought is to use an ESB (Enterprise Service Bus).  The benefits of using an ESB are that it’s a robust communications channel for all of the Microservices, however, a drawback is that it’s also a single point of failure.  The other school of thought is to use simple RESTful/HTTP communications directly between the various Microservices.  This removes the single point of failure, but does add the overhead of requiring the ability of each service to be able to “discover” other services (their availability and location for example) on the network.  This usually involves an additional tool, something like Consul, for example.

Some of the benefits of adopting a Microservices architecture are that software development teams can be formed around each individual service.  These would be full teams with developers, project managers etc. rather than having specific technical silos within one large team.  Other benefits are that applications become far more flexible and modular and can be composed and changed easily by simply swopping out one Microservice for another.

Some of the drawbacks of Microservices are that they have a potentially higher maintenance cost as your application will often be deployed across different and more expansive platforms/servers.  Other drawbacks are the potential for “data islands” to form.  This is where your application’s data becomes disjointed and more distributed due to the nature of the architecture.  Furthermore, Microservices, if they are to be successful, will require extensive monitoring.  Monitoring of every available metric of the applications and the communications between them is essential to enable effective support of the application as a whole.

After this, Peter moves on to show us some demo code, built using OWIN and NancyFX.  OWIN is the Open Web Interface for .NET and is an open framework for decoupling .NET web applications from the underlying web server that powers the application.  Peter tells us that Microsoft’s own implementation of the OWIN standard is called Katana.  NancyFX is a lightweight web framework for .NET, and is built on top of the OWIN standard, thus decoupling the Nancy codefrom the underlying web server (i.e. there’s no direct references to HttpContext or other such objects).

Peter shows us how simple some of Nancy’s code can be:

public dynamic Something(){
    var result = GetSomeData();
    return result==null ? 404 : Result.AsJson();    
}

The last line of the code is most interesting.   Since the method returns a dynamic type, returning an integer that has the same value as a HTTP Status Code will be inferred by the Nancy framework to actually return that status code from the method!

Peter shows us some more code, most of which is very simple and tells us that the complete demo example is available to download from GitHub.

After Peter’s talk wrapped up, it was time for lunch.  Lunch at the DDD events is usually a “brown bag” affair with a sandwich, crisps, some fruit and/or chocolate.  The catering at DDD North, and especially at the University of Sunderland, is always excellent and this year was no exception.   There was a large selection of various combinations of crisp flavours, chocolate bars and fruit along with a large selection of very nice sandwiches, including some of the more “basic” sandwich fillings for fusspots like me!  I don’t like mayonnaise, so pre-packed sandwiches are usually a tricky proposition.  This year, though, they had “plain” cheese and ham sandwiches with no additional condiments, which was excellent for me.

The excellent food was accompanied by a drink. I opted for water.  After collecting my lunch, I went off to find somewhere to sit and eat as well as somewhere that would be fairly close to a power point as I needed to charge my laptop.

I duly found a spot that allowed me to both eat my lunch, charge my laptop and look out of the window onto the River Wear at what was a very nice day outside in sunny Sunderland!

After fairly quickly eating my lunch, it was time for some lunchtime Grok Talks.  These are the 15-minute, usually fairly informal talks that often take place over lunch hour at many of these type of conferences and especially at DDD conferences.  During the last few DDD’s that I’d attended, I’d missed most of the Grok Talks for various reasons, but today, having already consumed my delicious lunch, I decided that I’d try to take them in.

By the time I’d reached the auditorium for the Grok Talks, I’d missed the first few minutes of the talk by Jeff Johnson all about Microsoft Azure and the role of Cloud Solution Architect at Microsoft.

Jeff first describes what Azure is, and explains that it’s Microsoft’s cloud platform offering numerous services and resources to individuals and companies of all sizes to be able to host their entire IT infrastructure – should they so choose – in the cloud. 

Next, Jeff shows us some impressive statistics on how Azure has grown in only a few short years.  He says that the biggest problem that Microsoft faces with Azure right now is that they simply can’t scale their infrastructure quick enough to keep up with demand.  And it’s a demand that is continuing to grow at a very fast rate.  He says that Microsoft’s budget on expenditure for expanding and growing Azure is around 5-6 billion dollars per annum, and that Azure has a very large number of users even today.

Jeff proceeds by talking about the role of Cloud Solutions Architect within Microsoft.  He explains that the role involves working very closely with customers, or more accurately potential customers to help find projects within the customers’ inventory that can be migrated to the cloud for either increased scalability, general improvement of the application, or to make the application more cost effective.  Customers are not charged for the services of a Cloud Solutions Architect, and the Cloud Solutions Architects themselves seek out and identify potential customers to see if they can be brought onboard with Azure.

Finally, Jeff talks about life at Microsoft.  He states how Microsoft in the UK has a number of “hubs”, one each in Edinburgh, Manchester and London, but that Microsoft UK employees can live anywhere.  They’ll use the “hub” only occasionally, and will often work remotely, either from home or from a customer’s site.

After Jeff’s talk, we had Peter Bull and his In The Groove talk all about developing for Microsoft’s Groove Music.  Peter explains that Groove Music is Microsoft’s equivalent to Apple’s iTunes and Google’s Google Play Music and was formerly called Xbox Music.  Peter states that Groove Music is very amenable to allowing developers to create new applications using Groove Music as it offers both an API and an SDK.  The SDK is effectively a wrapper around the raw API.  Peter then shows us a quick demo of some of the nice touches to the API which includes the retrieval of album artwork.  The API allows retrieving album artwork of varying sizes and he shows us how, when requesting a small version of some album artwork that, for example, contains a face, the Groove API will use face detection algorithms to ensure that when dynamically resizing the artwork, the face remains visible and is not cropped out of the picture.

The next Grok talk was by John Stovin and was all about a unit testing framework called Fixie.  John starts by asking, Why another unit testing framework?  He explains that Fixie is quite different from other unit testing frameworks such as NUnit or xUnit.  The creator of Fixie, Patrick Lioi, stated that he created Fixie as he wanted as much flexibility in his unit testing framework as he had with other frameworks he was using in his projects.  To this end, Fixie does not ship with any assertion framework, unlike NUnit and xUnit, allowing each Fixie user to choose his or her own Assertion framework.  Fixie is also very simple in how you author tests.   There’s no [Test] style attributes, no using Fixie statements at the top of test classes.   Each test class is simply a standard public class and each test method is simply a public method whos name ends in “Test”.  Test setup and teardown is similar to xUnit in that it simply uses the class constructor and Dispose methods to perform these functions.

Interestingly, Fixie tests can inherit from a “Convention” base class which can change the behaviour of Fixie.  For example, a custom convention class can be implemented very simply to alter the behaviour of Fixie to be more like that of NUnit, with test classes decorated by a [TestFixture] attribute and test methods decorated by a [Test] attribute.  Conventions can control the discovery of tests, how tests are parameterized, how tests are executed and also how test output is displayed.

Fixie currently has lots of existing test-runners, including a command-line runner and a runner for the Visual Studio test explorer.  There’s currently a plug-in to allow ReSharper 8 to run Fixie tests, and a new plug-in/extension is currently being developed to work with ReSharper 10.  Fixie is open-source and is available on GitHub.

After John’s talk, we had the final Grok Talk of the lunch time, which was Steve Higgs’s ES6 Right Here, Right Now.  Steve’s talk is how developers can best use and leverage ES6 (ECMAScript 6 aka JavaScript 2015) today.  Steve starts by stating that, contrary to some beliefs, ES6 is no longer the “next” version of JavaScript, but is actually the “current” version.  The standard has been completely ratified, but most browsers don’t yet fully support it.

Steve talks about some of the nice features of ES6, many of which had to be implemented with 3rd-party libraries and frameworks.  ES6 has “modules” baked right in, so there’s no longer any need to use a 3rd-party module manager.  However, if we’re targeting today's browsers and writing JavaScript targeting ES5, we can use 3rd-party libraries to emulate these new ES6 features (for example, require.js for module management).

Steve continues by stating that ES6 will now (finally) have built-in classes.  Unfortunately, they’re not “full-featured” classes like we get in many other languages (such as C#, Java etc.) as they only support constructors and public methods, and have no support for things like private methods yet.  Steve does state that private methods can be “faked” in a bit of a nasty, hacky way, but ES6 classes definitely do not have support for private variables.  Steve states that this will come in the future, in ES7.

ES6 gets “arrow functions”, which are effectively lambda functions that we know and love from C#/LINQ, for example:

var a = [
  "Hydrogen",
  "Helium",
  "Lithium",
  "Beryl­lium"
];

// Old method to return length of each element.
var a2 = a.map(function(s){ return s.length });

// New method using the new "arrow functions".
var a3 = a.map( s => s.length );

Steve continues by stating that ES6 introduces the let and const keywords.  let gives a variable block scoping rather than JavaScript’s default function scoping.  This is a welcome addition, and helps those of us who are used to working with languages such as C# etc. where our variable scoping is always block scoped.  const allows JavaScript to declare a constant.

ES6 now also has default parameters which allow us to define a default value for a function’s parameter in the event that code calling the function does not supply a value:

function doAlert(a=1) {
    alert(a);
}

// Calling doAlert without passing a value will use the
// default value supplied in the function definition.
doAlert();

Steve also mentions how ES6 now has string interpolation, also known a “template strings”, so that we can finally write code such as this:

// Old way of outputting a variable in a string.
var a = 5;
var b = 10;
console.log("Fifteen is " + (a + b) + " and\nnot " + (2 * a + b) + ".");

// New ES6 way with string interpolation, or "template strings".
var a = 5;
var b = 10;
console.log(`Fifteen is ${a + b} and\nnot ${2 * a + b}.`);

One important point to note with string interpolation is that your string must be quoted using backticks (`) rather than the normal single-quote (‘) or double-quote (“) characters!  This is something that will likely catch a lot of people out when first using this new feature.

Steve rounds off his talk by stating that there’s lots of other features in ES6, and it’s best to simply browse through them all on one of the many sites that detail them.  Steve says that we can get started with ES6 today by using something like babeljs.io, which is a JavaScript compiler (or transpiler) and allows you to transpile JavaScript code that targets ES6 into JavaScript code that is compatible with the ES5 that is fully supported by today’s browsers.

After Steve’s talk, the Grok Talks were over, and with it the lunch break was almost over too.  There was a few minutes left to head back to the communal area and grab a cup of coffee and a bottle of water to keep me going through the two afternoon sessions and the two final sessions of the day.

The first session of the afternoon was another change to the advertised session due to the previously mentioned cancellations.  This session was Pete Smith’s Beyond Responsive Design.  Pete’s session was aimed at design for modern web and mobile applications.  Pete starts with looking at a brief history of web development.  He says that the web started solely on the desktop and was very basic at first, but very quickly grew to become better and better.  Eventually, the Smartphone came along and all of these good looking desktop websites suddenly didn’t look so good anymore.

So then, Responsive Design came along.  This attempted to address the disconnect and inconsistencies between the designs required for the desktop and designs required for the mobile.  However, Responsive Design brought with it it’s own problems.  Our designs became awash with extensive media queries in order to determine which screen size we were rendering for, as well as became dependent upon homogenous (and often large) frameworks such as Zurb’s Foundation and Bootstrap.  Pete says that this is the focus of going “beyond” responsive design.  We can solve these problems by going back to basics and simplifying what we do.

So, how do we know if we've got a problem?  Well, Pete explains that there are some sites that work great on both desktop and mobile, but overall, they’re not as widespread as we would like given where we are in our web evolution.  Pete then shows some of the issues.  Firstly, we have what Pete calls the "teeny tiny" problem.  This is where the entire desktop site is scaled and shrunk down to display on the smaller mobile screen size.  Then there's another problem that Pete calls "Indiana’s phone and the temple of zoom" which is where a desktop site, rendered on a mobile screen, can be zoomed in continuously until it becomes completely unusable.

Pete asks “what is a page on today’s modern web?”  Well, he says there’s no such thing as a single-page application.  There’s really no difference between SPA’s and non-SPA sites that use some JavaScript to perform AJAX requests to retrieve data from the server.  Pete states that there’s really no good guiding design principles.  When we’re writing apps for Android or iOS, there’s a wealth of design principles that developers are expected to follow, and it’s very easy for them to do so.  A shining example of this is Google’s Material Design. When we’re designed for the web, though, not so much.

So how do we improve?  Pete says we need to “design from the ground up”.  We need to select user-interface patterns that work well on both the desktop and on mobile.  Pete gives examples and states that UI elements like modal pop-ups and alerts work great on the desktop, but often not so well on mobile.  An example of a UI pattern that does work very well on both platforms are the “panes” (sometimes referred to as property sheets) that slide in from the side of the screen.  We see this used extensively on mobile due to the limited screen real estate, but not so much on the desktop, despite the pattern working well here also.  A great example of effective use of this design pattern is the new Microsoft Azure Preview Portal.  Pete states we should avoid using frameworks like Bootstrap or Foundation.  We should do it all ourselves and we should only revert to “responsive design” when there is a specific pattern that clearly works better on one medium than another and where no other pattern exists that works well on all mediums.

At this point in the talk, Pete moves on to show us some demo code for a website that he’s built to show off the very design patterns and features that he’s been discussing.  The code is freely available from Pete’s GitHub repository. Pete shows his website first running on a desktop browser, then he shows the same website running on an iPad and then on a Smartphone.  Each time, due to clever use of design patterns that work well across screens of differing form factors, the website looks and feels very similar.  Obviously there are some differences, but overall, the site is very consistent.

Pete shows the code for the site and examines the CSS/LESS styles.  He says that absolute positioning for creating these kind of sites is essential.  It allows us to ensure that certain page elements (i.e.. the left-hand menu bar) are always displayed correctly and in their entirety.  He then shows how he's used CSS3 transforms to implement the slide in/out panels or “property sheets”, simply transforming them with either +100% or -100% of their horizontal positioning to display to the left or right of the element’s original, absolute position.  Pete notes how there’s extensive use of HTML5 semantic tags, such as <nav> <content> <footer> etc.  Pete reminds us that there’s no real behaviour attached to using these tags but that they make things far easier to reason about than simply using <div> tags for everything.

Finally, Pete summarises and says that if there’s only one word to take away from his talk it’s “Simplify”.  He talks about the future and mentions that the next “big thing” to help with building sites that work well across all of the platforms that we use to consume the web is Web Components.  Web Components aid encapsulation and re-usability.  They’re available to use today, however, they’re not yet fully supported.  In fact, they are only currently supported in Chrome and Opera browsers and need a third-party JavaScript library, Polymer.js, in order to work.

The final session of the day was Richard Fennell’s Monitoring and Addressing Technical Debt With SonarQube.

Richard starts his session by defining technical debt.  He says it’s something that builds up very slowly, almost sneaks up on you.  It’s the little “cut corners” of our codebases where we’ve implemented code that seems to do the job, but is sub-optimal code.  Richard says that technical debt can grow to become so large that it can stop you in your tracks and prevent you from progressing with a project.

He then discusses the available tools that we currently have to address technical debt, but specifically within the world of Microsoft’s tooling.  Well, firstly we have compiler errors.  These are very easy to fix as we simply can’t ship our software with compiler errors, and they’ll provide immediate feedback to help us fix the problem.  Whilst compiler errors can’t be ignored, Richard says that it’s really not uncommon to come across projects that have many compiler warnings. Compiler warnings aren’t errors as such, and as they don’t necessarily prevent us from shipping our code, we can often live with them for a long time.  Richard mentions the tools Visual Studio Code Analysis (previously known as FXCop) and StyleCop.  Code Analysis/FxCop works on your compiled code to determine potential problems or maintenance issues with the code, whilst StyleCop works on the raw source code, analysing it for style issues and conformance against a set of coding standards.  Richard says that both of these tools are great, but offer only a simple “snapshot in time” of the state of our source code.  What we really need is a much better “dashboard” to monitor the state of our code.

Richard asks, “So what would Microsoft do?”.  He continues to explain that the “old” Microsoft would go off and create their own solution to the problem, however, the “new” Microsoft, being far more amenable to adopting already-existing open source solutions, has decided to adopt the existing de-facto standard solution for analysing technical debt, a product called SonarQube by SonarSource.

Richard introduces SonarQube and states that, firstly, we must understand that it’s a Java based product.  This brings some interesting “gotchas” to the .NET developers when trying to set up a SonarQube solution as we’ll see shortly.  Richard states that SonarQube’s architecture is based upon it having a backend database to store the results of its analysis, and it also has plug-in analyzers that analyze source code.  Of course, being a Java-based product, SonarQube’s analyzers are written in Java too.  The Analyzers examine our source code and create data that is written into the SonarQube database.  From this data, a web-based front-end part of the SonarQube product can render a nice dashboard of this data in ways that help us to "visualise" our technical debt.   Richard points out that analyzers exist for many different languages and technologies, but he also offers a word of caution.  Not all analyzers are free and open source.  He states that the .NET ones currently are but (for example) the COBOL & C++ analyzers have a cost associated with them.

Richard then talks about getting an installation of SonarQube up and running.  As it’s a Java product, there’s very little in the way of nice wizards during the installation process to help us.  Lots of the configuration of the product is performed via manual editing of configuration files.  Due to this, Microsoft’s ALM Rangers group have produced a very helpful guide to help in installing the product.  The system requirements for installing SonarQube are a server running either Windows or Linux with a minimum of 1GB of RAM.  The server will need to have .NET framework 4.5.2 installed also, as this is required by the MSBuild runner which is used to run the .NET analyzer.  As it’s a Java product, obviously, Java is required to be installed on the server – either Oracle’s JRE 7 (or higher) or OpenJDK 7 (or higher).  For the required backend database SonarQube will, by default, install a database called H2, however this can be changed (and probably should be changed) to something more suited to .NET folks such as Microsoft’s SQL Server.  It’s worth noting that the free SQL Server Express will work just fine also.  Richard points out that there are some “gotchas” around the setup of the database, too.  As a Java-based product, SonarQube will be using JDBC drivers to connect to the database, and these place some restrictions on the database itself.  The database must have it’s collation set to Case Sensitive (CS) and Accent Sensitive (AS).  Without this, it simply won’t work!

After setup of the software, Richard explains that we’ll only get an analyzer and runner for Java source code out-of-the-box.  From here we can download and install the analyzer and runner we’ll need for analyzing C# source code.  He then shows how we need to add a special file called sonar-project.properties to the root of our project that will be analyzed.  This file contains four key values that are required in order for SonarQube to perform it’s analysis.  Ideally, we’d set up our installation of SonarQube on a build server, and there we’d also edit the SonarQube.Analyzers.xml file to reflect the correct database connection string to be used.

Richard now moves onto showing us a demo.  He uses the OWASP demo project, WebGoat.NET for his demonstration.  This is an intentionally “broken” ASP.NET application which will allow SonarQube to highlight multiple technical debt issues with the code.  Richard shows SonarQube being integrated into Visual Studio Team Foundation Server 2015 as part of its build process.  Richard further explains that SonarQube analyzers are based upon processing complete folders or wildcards for file names.  He shows the  default SonarQube dashboard and explains how most of the errors encountered can often be found in the various “standard” libraries that we frequently include in our projects, such as jQuery etc.  As a result of this, it’s best to really think about how we structure our solutions as it’s beneficial to keep third-party libraries in folders separate from our own code.  This way we can instruct SonarQube to ignore those folders.

Richard shows us the rules that exist in SonarQube.  There are a number of built-in rules provided by SonarQube itself, but the C# analyzer plug-in will add many of it’s own.  These built-in SonarQube rules are called the “Sonar Way” rules and represent the expected Sonar way of writing code.  These are very Java-centric so may only be of limited use when analyzing C# code.  The various C# rule-sets are obviously more aligned with the C# language.  Some rules are prefixed with “CA” in the rule-set list and these are the FxCop rules, whilst other rules are prefixed with “S” in the rule-set list.  These are the C# language rules and use Roslyn to perform the code analysis (hence the requirement for the .NET framework 4.5.2 to be installed).

Richard continues by showing how we can set up “quality gates” to show if one of our builds is passing or failing in quality.  This is an analysis of our code by SonarQube as part of a build process.  We can set conditions on the history of the analyses that have been run to ensure that, for example, each successive build should have no more than 98% of known bugs of the previous release.  This way, we can reason that our builds are getting progressively better in quality each time.

Finally, Richard sums up by introducing a new companion product to SonarQube called SonarLint. SonarLint is based upon the same .NET Compiler platform, Roslyn, that provides SonarQube’s analysis, however SonarLint is designed to be run inside the Visual Studio IDE and provides near real-time instant feedback on the quality of our source code as we’re editing it.  SonarLint is open source and available on Github.

After Richard’s talk was over, it was time for all of the conference attendees to gather in the main lecture hall for the final wrap-up presentation.  During the presentation, the various sponsors were thanked for all of their support.  The conference organisers did also mention how there had been a number of “no-shows” to the conference (people who’d registered to attend but had not shown up on the day and hadn’t cancelled their tickets despite repeated communication requesting people who can no longer attend to do so).  The organisers told us how every no-show not only costs the conference around £15 per person but also prevents those who were on the waiting list from being able to attend, and there was quite an extensive waiting list for the conference this year.  Here’s hoping that future DDD Conferences have less no-shows.

It was mentioned that DDD North is now the biggest of all of the regional DDD events, with some 450 (approx.) attendees this year – a growth on last year’s numbers – with still over 100 more people on the waiting list.  The organisers told us that they could, if it weren’t for space/venue size considerations, have run the conference for around 600-700 people.  That’s quite some numbers and shows just how popular the DDD conferences, and especially the DDD North conference, are.

One especially nice touch was that I did receive a quick mention from Andy Westgarth, the main organiser of DDD North, during the final wrap-up presentation for the use of one of my pictures for an article that had appeared in the local newspaper, the Sunderland Echo, that very day.  The picture used was one I’d taken in the same lecture hall at DDD North 2013, two years earlier.  The article is available to read online, too.

After the wrapping up came the prize draw.  As always, there was some nice prizes to be given away by both the conference organisers themselves as well as prizes to be given away by the individual sponsors including a Nexus 9 tablet, a Surface Pro 3 and a whole host of other goodies.  As was usual, I didn’t win anything, but I’d had a fantastic day at yet another superb DDD North.  Many thanks to the organisers and the various sponsors for enabling such a brilliant event to happen.

But…  It wasn’t over just yet!   There is usually a “Geek Dinner” after the DDD conferences however on this occasion there was to be a food and drink reception graciously hosted by Sunderland Software City.  So as we shuffled out of the Sunderland University campus, I headed to my car to drive the short distance to Sunderland Software City.

Upon arrival there was, unfortunately, no pop-up bar from Vaux Brewery as there had been two years prior.  This was a shame as I’m quite partial to a nice pint of real ale, however, the kind folks at Sunderland Software City had provided us with a number of buckets of ice-cold beers, wines and other beverages.  Of course, I was driving so I had to stick to the soft drinks anyway!

I was one of the first people to arrive at the Sunderland Software City venue as I’d driven the short distance from the University to get there, whereas most other people who were attending the reception were walking from the University.  I grabbed myself a can of Diet Coke and quickly got chatting to some fellow conference attendees sharing experiences about our day and getting to know each other and finding out what we do for a living and all about the work we do.

Not too long after getting chatting, a few of the staff from the Centre were scurrying towards the door.  What we soon realised was that the “food” element of the “food & drink” reception was arriving.  We were being treated to what must be the single largest amount of pizza I’ve ever seen in one place.  76 delicious pizzas delivered from Pizza Hut!  Check out the photo of this magnificent sight!  (Oh, and those boxes of pizza are stacked two deep, too!)

So, once the pizzas had all been delivered and laid out for us on the extensive table top, we all got stuck in.  A few slices of pizza later and an additional can of Diet Coke to wash it down and it was back to mingling with the crowd for some more networking.

Before leaving, I managed to have a natter with Andy Westgarth, the main conference organiser about the trials and tribulations of running a conference like DDD North.  Despite the fact that Andy should be living and working in the USA by the time the next DDD North conference rolls around, he did assure me that the conference was in very safe hands and should continue on next year.

After some more conversation, it was finally time for me to leave and head off back to my in-laws in Newcastle.  And with that another superb DDD North conference was over.  Here’s looking forward to next year!