The most popular structure for BDD today is called the Gherkin format and follows a Given/When/Then format, like…

Given a new bowling game
When all frames are strikes
Then the score should be 300

There are frameworks like SpecFlow to help you arrange your specifications (test) into this format. However, I find this format awkward and forced. I prefer the simpler format known as Context/Specification (aka When/Should)…

When all frames are strikes
Should have a score of 300

There are frameworks, like MSpec, that attempt to make the specifications read more like English sentences. However, I find that these frameworks get in the way as much as they help. It is also nice to be able to write readable tests with just PONU (plain old NUnit). Over time, I’ve developed a convention that I find easy to write and easy to read. I’ve also developed a tool  that turns the tests into a markdown file that can be turned into a pretty HTML report.

To show the approach at work, I present some snippets from a hypothetical order pricing system I created as a “developer test” provided by a prospective employer last summer. Here is what I was given:

You can see that there are quite a few specifications here. It’s a perfect scenario for a BDD approach. Lets take a look at the specification that most states charge taxes on the discounted price, while a few states require taxes to be calculated on the original price.

Here is a specification that a standard tax state calculates taxes on the discounted price…

namespace Acme.Tests.ConcerningCoupons
{
    [TestFixture]
    public class When_coupon_is_applied_to_item_on_order_in_standard_tax_state
    {
        private Order _order;
        [TestFixtureSetUp] public void Context()
        {
            Product product = new Product(10);
            Coupon coupon = CreateCoupon.For(product).WithDiscountOf(.5m);
            _order = CreateOrder.Of(product).Apply(coupon).In(StateOf.NC);
        }

        [Test] public void Should_calculate_tax_on_discounted_price()
        {
            _order.Tax.ShouldEqual(.25m);
        }
    }
}

You can see that the test fixture class name defines the context (the when) The test method name specifies the specification (the should). The Context method sets us the context in the class name. Also note the ConcerningCoupons in the namespace. This allows us to categorize the specification.

Here is the code that specifies the prediscount tax states…

namespace Acme.Tests.ConcerningCoupons
{
    [TestFixture]
    public class When_coupon_is_applied_to_item_on_order_in_prediscount_tax_state
    {
        private Order _order;
        [TestFixtureSetUp] public void Context()
        {
            Product product = new Product(10);
            Coupon coupon = CreateCoupon.For(product).WithDiscountOf(.5m);
            _order = CreateOrder.Of(product).Apply(coupon).In(StateOf.FL);
        }

        [Test] public void Should_calculate_tax_on_full_price()
        {
            _order.Tax.ShouldEqual(.50m);
        }
    }
}

Now take a look at a section of the report generated from the tests…

Anyone can now compare the generated report to the original specification to verify we hit the mark. It’s a little more work to structure your tests this way, but the benefits are worth it.

The full source for the sample and the report generator are available here.

Ever since I stumbled across the original C++ Report articles that have become known as the SOLID principles, I have been a disciple of Robert Martin (aka Uncle Bob). He is a leader within the agile and software craftsmanship movements. He has as good a sense of what makes good software as anyone currently writing and teaching. If he thinks it’s worth writing, then it’s worth reading.

I’m not usually a fan of video for learning. I like the random access referenceability of books and I like the on-the-go accessibility of audio recordings. I find video to be the worst of both worlds: I cannot flip to the a particular page or go at my own pace, nor can I consume it while driving or mowing the lawn. Further, there are so many good conference videos available for free that I find it difficult to justify paying for video content.

However, when Uncle Bob began releasing the Clean Code video series, I thought I’d at least check them out. I found them to be so good, that I’m now having my development team watch them together over lunch hours. Uncle Bob does a great job teaching (and preaching) the techniques that lead to clean, maintainable software. As we watch the sessions together, we are creating a common baseline of understanding that we can all refer to as we work together.

I highly recommend them for any software development team that is looking to get better (and if your team is not working to get better, it is on it’s way to obsolescence).  If you have trouble justifying the cost ($12 per viewer per video) to management, have them take a look at this excellent explanation of the value of software craftsmanship and professionalism.

I’ve written in the past about how the relentless march of progress in software has made yesterday’s innovations today’s commodity.

This article from HBR captures the essence of how the software landscape is in the process of making traditional, data-oriented, system-of-record, transactional systems into commodity systems. Companies that have made a living with these traditional systems are going to wake up very shortly and find that their customers have become someone else’s community participants.

The combination of ubiquitous mobile connection, cloud computing, and the general adoption of social media is in the process of changing the expectations of software users. Keeping their data safe is no longer enough. They now expect (or soon will) a more immersive experience.

This doesn’t mean a link to a Facebook fan page that is merely a shallow marketing ploy. Users expect that their hosted application will allow them to interact with all of the other uses of your software (see Spiceworks as the best example of this). They expect that the software they run their business on enlists them into the community of users of your software. Don’t have a “community” of users? Your competition soon will.

The Field Service domain is all about getting people to Locations to perform Activities. The Activities and the Location are defined by a Work Order. The person performing the Activities (and possibly the time) are defined by an Appointment. You can think of the Work Order as the what and where and the Appointment as the who and when.

The model is simple as long as there is one Work Order and one Appointment. In fact, you’d be tempted to combine them into one entity. However, things get more complicated when you have one Work Order that requires multiple Appointments to complete it. It might be two technicians at one time or the same technician on two different visits or multiple technicians over multiple visits.

In these cases, we are splitting the Activities of the Work Order over multiple Appointments. Some of the Activities are associated with one Appointment and some with another.

The Work Order is complete when all Activities over all Appointments are complete. Not too complicated.

Now also imagine that a technician goes out on one Appointment but services more than Work Order.

In simplest terms, we have a many-to-many between Appointments and Work Orders. You can imagine the twisted case where a given Appointment services two different Work Orders, and each of those Work Orders has other Appointments serving them.

We’ve all implemented many-to-many relationships in databases and in object models. However, this situation is a little different because of the role of the Activity. The linkage between the Appointment and the Work Order is through their association with a common Activity.

A given Activity is associated with one Work Order and one Appointment. We can now traverse that relationship to discover the relationship between Appointment and Work Order.

We simply have Work Orders with collections of Activities and Appointments with collections of Activities. With that, the complex relationship between Appointment and Work Order is completely modeled, with no redundant connections (like something directly linking Appointments and Work Orders). I thought this was pretty cool. And it was.

But here is where the problem comes in… In the real world, Appointments really are associated with Work Orders—and not just by the happenstance of common Activities. (One could argue this actually is true because you would only schedule an Appointment to service a Work Order if there was some Activity to perform, but this is not how anyone thinks about the relationship.)

With our model, to answer the question “Where does this Appointment take place?” we have to go to one of the Activities in our collection, navigate to its Work Order, and look at its Location property. This not a big deal to do in code, and we can even put a property on the Appointment that hides this messiness and gives the illusion that an Appointment has a Location. But this is not the only difficulty.

Try looking in our database to see what Work Order a given Appointment is associated with. You have to go to the Activity table and find all the Activities associated with this Work Order, then look at the Work Order column of those Activities to find the ID(s) of the Work Order(s). One day I tried to create a view that shows an Appointment and its first Work Order (there was only ever one Work Oder per Appointment in practice). I gave up after 20 minutes.

Now imagine explaining this model to a programmer trying to integrate with our system. Its possible, but there is much more confusion than you’d like.

We had a technically tight and elegant implementation, but it ended up obscuring one of the most fundamental relationships in our domain.

Remedy

As much as we liked the normalization of our current implementation, the team decided that it would be better to model the relationship between Appointment and Work Order explicitly. If we had focused more on the domain and less on the cleverness of the implementation, we could have avoided this rework of the design.

Two forces have converged: 1) Mobility has gone from an optional differentiator to an expected component of any software offering, and 2) HTML5 has been crowned as the solution that will solve the cross-platform problem that Java, Flash, and Silverlight failed to solve before it.

This collision of forces has turned HTML5 into a buzzword with a life of its own. In fact, it appears to be on its way to becoming as detached from reality as the all-time-champion of promising technology turned meaningless buzzword: SOA.

Don’t get me wrong, I believe HTML5 is the best current answer to cross-platform mobile software. Before recommending to my executive management that we build our cross-platform offering in JavaScript, using the family of features loosely known as HTML5, I looked at pure native, cross-platform native with Mono, frameworks like Titanium.  I made that recommendation before HTML5 became the cool thing to do, and I haven’t regretted it for a second.

A sure sign that a technology has reached fad level is when articles start to appear pointing out that said new technology will not, in fact, usher in world peace. Such an article was the popular 11 hard truths about HTML5.

The title was a little ominous and I began to read it with some trepidation, as we were still a couple of months away from being ready to ship our HTML5 client application. However, as I read, I was comforted by the fact that although all 11 truths were valid challenges, our team had faced and dealt with each of them.

The article lays out eleven challenges when building an HTML5 application:

1. Security is a nightmare.
2. Local data storage is limited
3. Local data can be manipulated.
4. Offline apps are a nightmare to sync.
5. The cloud owes you nothing.
6. Forced upgrades aren’t for everyone.
7. Web Workers offer no prioritization.
8. Format incompatibilities abound.
9. Implementations are browser-dependent.
10. Hardware idiosyncrasies bring new challenges.
11. Politics as usual.

It’s a scary list, and they are all true. My team was able to handle and mitigate each of these challenges largely because we had years of experience building native mobile applications and desktop Web applications and much of what we learned there applied in the HTML5 world.

If you don’t have solid answers for each of these challenges, you really ought to get someone on the team who has confidence in dealing with each of them.

You can check my current availability here.

This article explores the physiological and psychological effects of fatigue brought on by making decisions. The fatigue that comes from making decision after decision immediately reminded me of my team’s Scrum sprint planning days.

The Scrum method breaks software development into iterative cycles called sprints. Our sprints were the highly typical two weeks in length. The idea is that two weeks is a short enough planning horizon that we can pull in enough work from the backlog to fill that time period. Then we demo what we’ve done to the various stakeholders in the company, adjust existing backlog priorities, plan another sprint, and on it goes.

Sprint planning day looked something like this…

9:00 Demos (any stories that haven’t been shown yet)

9:30 Close out the Previous Sprint (closing stories in VersionOne, splitting any unfinished stories, etc.)

10:00 Retrospective (look back over the prior sprint and identify things that worked well that we want to do more of, what didn’t work so well, and identify any impediments to progress)

10:45 Start Sprint Planning (Story Breakdown)

1:30 Finish Sprint Planning (Story Breakdown)

We often wouldn’t finish sprint planning until after 4:30. 

Story Breakdown

Sprint planning is the process of taking the high-level stories and breaking them down into tasks. We did this as a team; so, we had everyone’s input and everyone knew how we were going to go about implementing each story. This is vital to maintaining a team approach to building the product.

This story breakdown, however, is the hardest part of the whole sprint. We have to make decision after decision about how we are going to implement a feature…

Will there be a new database table? Will it be a variant of some existing feature or something new? Is there some new UI element that we haven’t tackled before? and so on.

Then for every decision, we have one more decision: how long do we think it will take.

I believe the hardest part is that we move from one decision to the next without actually doing anything. We are simply adding our decisions to the inventory to be acted on over the next two week. This makes the decision fatigue factor even greater.

By the time we got to 3:00 or 3:30 the team would often be so fatigued that we would start placing two tasks on each story: Plan it and Do it. During the sprint, if we came across a story with a “Do it” task, it was a safe bet that it was planned late in the day.

Remedy

I can see two ways to reduce the decision fatigue that comes with Scrum planning day: 1) reduce the Sprint length, or 2) don’t do Scrum.

For most teams doing Scrum, I think shrinking the Sprint length to one week will reduce the planning day fatigue. Our team, for other reasons, switched to a Kanban continuous flow model. Under that model, we did the story breakdown as the queue of planned stories got low. It was never two weeks worth at one time, and we had fewer “Do it” tasks.

The Advantage We Got

We used the Repository pattern and built our domain model out of plain old CLR objects (POCOs). This meant that we could have the same rich domain model both on the client where the user is interacting through a UI and on the sever where changes from the field and from host system integrations are being processed. This allowed us to keep our code DRY. There was no repetition among the entities, behaviors, relationships between entities, and tests. We never had to worry about model inconsistencies between the client and the server. It seemed like a big win, and in some ways it was.

The Price We Paid

As we built out the product, we saw that the object model’s usage patterns were not the same on the client as on the server. We ended up with behavior that only applied on the client or on the server.

In an effort to keep from putting code into our domain that would not be relevant on both the client and server, we started putting logic into little services that work with the domain. Most of this logic was on the client side and got applied through use of the Event Aggregator pattern. This worked, but tended to impoverish the domain model itself.

Fixing It

The problem largely fixed itself when Windows Mobile became irrelevant almost overnight. We rebuilt the client in JavaScript that runs on most modern browsers. This gave us a do-over opportunity and removed the option of sharing model code between the server and client.

If I had the Windows Mobile situation to do over again, I would be more careful to define a common structure that the client and server could share, but compose behaviors relevant to the client and server within each of those contexts.

Our team had four fully dedicated developers, a tester, and me. We needed to dedicate 25% of our time to release B. The first option was to simply intersperse the backlog with stories from release A and release B…

Interspersed Backlog

There were two problems with the interspersed backlog. First, release A has a good bit of reactive work; so, the backlog is unpredictable and tends to consume the entire team’s attention. Second, since stories vary in actual effort to complete, we cannot really gauge or control what percentage of our capacity is applied to each effort.

The next idea, was to split the team and apply 25% of the people to release B….

Split the Team

This is the mathematically cleanest solution and the option that traditional software managers would probably pick every time. However, this option has major disadvantages…

1. Only one developer will know anything about how release B was implemented.
2. The many advantages of pairing are lost on both efforts because we have only one developer on release B and an odd number on release A.

This option really just throws the team-based approach to building software out the window; so, not an option for us.

Next thought was to create two separate backlogs….

Split the Backlog

This makes the problem we are trying to solve clearer, but we still have the problem of how do we stay united as a team, yet timebox each backlog.

The next step was to designate days of the week for servicing each backlog. To give the forward-looking release one fourth of our capacity, we dedicated one pair to that backlog for half of the week. To simplify things, and give it a little more than 25%, went ahead and gave it three full days instead of two and a half. So this is what things looked like…

Split the Backlog and the Week

We rotated the release B pair so that there would always be one person that worked on that last week for continuity and one new person.

In our first retrospective after we finished release A (and moved onto release B fulltime), the team was convinced of a few things:

1. One team working on two releases at once is hard.
2. Keeping the team together was really important.
3. Splitting the backlogs and the week turned out to be a great way to do both at the same time.

I couldn’t hold off on designing and building the new product until everyone had gotten the DDD religion. In reality, I don’t think I would have done that even if I had the luxury of that kind of time. Concepts like DDD are best learned by doing them with someone who has done it before. Reading books and hearing presentations can get you excited about them, but only doing them helps you actually learn them.

As I prepared for the first meetings with the domain experts (folks from across the organization who had implemented multiple custom solutions and had a good sense of the domain of our new product), I sent out a copy of Domain Driven Design Quickly. I wanted to give them as much of an idea of what I was after as possible up front, but it mostly came down to me explaining it as we were doing it.

We had some great sessions. The white board photos we made in those early days formed a remarkably useful and resilient core of the domain model that is still reflected in the product today. We hashed out much of the [soon to be] ubiquitous language, and we all had a vision of what the new product would be.

Shortly after this, we hired a big DDD advocate (I’m still amazed at our good fortune in finding him. They are rare now, even more so in 2009). With Jarrel and me on the development team, we spread the DDD mindset organically as we built out the product. This seemed like a good and pragmatic approach.

In late 2010/early 2011, the entire development organization went through the blue book, chapter by chapter, discussing the pros and cons of Evans’ approach, where we’ve been effective, and where we haven’t.

What I learned was loud and clear: We would have had a better product and stayed out of the technological weeds much better if we had formally gone through the DDD material in 2009 instead of 2011. I underestimated how much more effective the team would have been if we took the time to build a common foundation of the DDD concepts. It didn’t mean we had to hold anything up. I just should have made it a priority earlier. I wont make that mistake again.