October 16, 2014

Dear Aunty Jason, I'm An Architect Who Wants To be Relevant Again...

"Dear Aunty Jason,

I am a software architect. You know, like in the 90's.

For years, my life was great. I was the CTO's champion in the boardroom, fighting great battles against the firebreathing dragons of Ad Hoc Design and the evil wizards of Commercial Off-The-Shelf Software. The money was great, and all the ordinary people on the development teams would bow down to me and call me 'Sir'.

Then, about a decade ago, everything changed.

A man called Sir Kent of Beck wrote a book telling the ordinary people that dragons and wizards don't actually exist, and that the songs I'd been singing of my bravery in battle had no basis in reality. 'You', Sir Kent told them, 'are the ones who fight the real battles.'

And so it was that the ordinary people starting coming up with their own songs of bravery, and doing their own software designs. If a damsel in distress needed saving, they would just save her, and not even wait for me - their brave knight - to at the very least sit astride my white horse and look handsome while they did it.

I felt dejected and rejected; they didn't need me any more. Ever since then, I have wandered the land, sobbing quietly to myself in my increasingly rusty armour, looking for a kingdom that needs a brave knight who can sings songs about fighting dragons and who looks good on a horse.

Do you know of such a place?

Your sincereley,

Sir Rational of Rose."

I can sympathise with your story, Sir Rational. I, too, was once a celebrated knight of the UML Realm about whom many songs were sung of battles to the death with the Devils of Enterprise Resource Planning. And, I, too, found myself marginalised and ignored when the ordinary folk started praying at the altar of Agile.

And, I'm sorry to say, there are lots of kingdoms these days where little thought is given to design or to architecture. You can usually spot them from a distance by their higgledy-piggledy, ramshackle rooftops, and the fact that they dug the moat inside the city walls.

But, take heart; there are still some kingdoms where design matters and where architecture is a still a "thing". Be warned, though, that many of them, while they may look impressive from the outside, are in fact uninhabited. Such cities can often be distinguished by gleaming spires and high white walls, beautiful piazzas and glistening fountains, and the fact that nobody wants to live there because they built it on the wrong place.

You don't want to go to either of those kinds of kingdom.

Though very rare, there are a handful of kingdoms where design matters and it matters that people want to live in that design. And in these places, there is a role for you. You can be relevant once again. Once again, people will sing songs of your bravery. But you won't be fighting dragons or wizards in the boardroom. You'll be a different kind of knight.

You'll be fighting real battles, embedded in the ranks of real soldiers. Indeed, you'll be a soldier yourself. And you'll be singing songs about them.




September 17, 2014

The 4 C's of Continuous Delivery

Continuous Delivery has become a fashionable idea in software development, and it's not hard to see why.

When the software we write is always in a fit state to be released or deployed, we give our customers a level of control that is very attractive.

The decision when to deploy becomes entirely a business decision; they can do it as often as they like. They can deploy as soon as a new feature or a change to an existing feature is ready, instead of having to wait weeks or even months for a Big Bang release. They can deploy one change at a time, seeing what effect that one change has and easily rolling it back if it's not successful without losing 1,001 other changes in the same release.

Small, frequent releases can have a profound effect on a business' ability to learn what works and what doesn't from real end users using the software in the real world. It's for this reason that many, including myself, see Continuous Delivery as a primary goal of software development teams - something we should all be striving for.

Regrettably, though, many software organisations don't appreciate the implications of Continuous Delivery on the technical discipline teams need to apply. It's not simply a matter of decreeing from above "from now on, we shall deliver continuously". I've watched many attempts to make an overnight transition fall flat on their faces. Continuous Delivery is something teams need to work up to, over months and years, and keep working at even after they've achieved it. You can always be better at Continuous Delivery, and for the majority of teams, it would pay dividends to improve their technical discipline.

So let's enumerate these disciplines; what are the 4 C's of Continuous Delivery?

1. Continuous Testing

Before we can release our software, we need confidence that it works. If our aim is to make the software available for release at a moment's notice, then we need to be continuously reassuring ourselves - through testing - that it still works after we've made even a small change. The secret sauce here is being able to test and re-test the software to a sufficiently high level of assurance quickly and cheaply, and for that we know of only one technical practice that seems to work: automate our tests. It's for this reason that a practice like Test-driven Development, which leaves behind a suite of fast-running automated tests (if you're doing TDD well) is a cornerstone of the advice I give for transitioning to Continuous Delivery.

2. Continuous Integration

As well as helping us to flag up problems in integrating our changes into a wider system, CI is also fundamental to Continuous Delivery. If it's not in source control, it's going to be difficult to include it in a release. CI is the metabolism of software development teams, and a foundation for Continuous Delivery. Again, automation is our friend here. Teams that have to manually trigger compilation of code, or do manual testing of the built software, will not be able to integrate very often. (Or, more likely, they will integrate, but the code in their VCS will likely as not be broken at any point in time.)

3. Continuous Inspection

With the best will in the world, if our code is hard to change, changing it will be hard. Code tends to deteriorate over time; it gets more complicated, it fills up with duplication, it becomes like spaghetti, and it gets harder and harder to understand. We need to be constantly vigilant to the kind of code smells that impede our progress. Pair Programming can help in this respect, but we find it insufficient to achieve the quality of code that's often needed. We need help in guarding against code smells and the ravages of entropy. Here, too, automation can help. More advanced teams use tools that analyse the code and detect and report code smells. This may be done as part of a build, or the pre-check-in process. The most rigorous teams will fail a build when a code smell is detected. Experience teaches us that when we let code quality problems through the gate, they tend to never get addressed. Implicit in ContInsp is Continuous Refactoring. Refactoring is a skill that many - let's be honest, most - developers are still lacking in, sadly.

Continuous Inspection doesn't only apply to the code; smart teams are very frequently showing the software to customers and getting feedback, for example. You may think that the software's ready to be released, because it passes some automated tests. But if the customer hasn't actually seen it yet, there's a significant risk that we end up releasing something that we've fundamentally misunderstood. Only the customer can tell us when we're really "done". This is a kind of inspection. Essentially, any quality of the software that we care about needs to be continuously inspected on.

4. Continuous Improvement

No matter how good we are at the first 3 C's, there's almost always value in being better. Developers will ask me "How will we know if we're over-doing TDD, or refactoring?", for example. The answer's simple: hell will have frozen over. I've never seen code that was too good, never seen tests that gave too much assurance. In theory, of course, there is a danger of investing more time and effort into these things than the pay-offs warrant, but I've never seen it in all my years as a professional developer. Sure, I've seen developers do these things badly. And I've seen teams waste a lot of time because of that. But that's not the same thing as over-doing it. In those cases, Continuous Improvement - continually working on getting better - helped.

DevOps in particular is one area where teams tend to be weak. Automating builds, setting up CI servers, configuring machines and dealing with issues like networking and security is low down on the average programmer's list of must-have skills. We even have a derogatory term for it: "shaving yaks". And yet, DevOps is pretty fundamental to Continuous Delivery. The smart teams work on getting better at that stuff. Some get so good at it they can offer it to other businesses as a service. This, folks, is essentially what cloud hosting is - outsourced DevOps.

Sadly, software organisations who make room for improvement are in a small minority. Many will argue "We don't have the time to work on improving". I would argue that's why they don't have the time.







September 16, 2014

Why We Iterate

So, in case you were wondering, here's my rigorous and highly scientific process for buying guitars...

It starts with a general idea of what I think I need. For example, for a couple of years now I've been thinking I need an 8-string electric guitar, to get those low notes for the metalz.

I then shop around. I read the magazines. I listen to records and find out what guitars those players used. I visit the manufacturers websites and read the specifications of the models that might fit. I scout the discussion forums for honest, uncensored feedback from real users. And gradually I build up a precise picture of exactly what I think I need, down to the wood, the pickups, the hardware, the finish etc.

And then I go to the guitar shop and buy a different guitar.

Why? Because I played it, and it was good.

Life's full of expectations: what would it be like to play one of Steve Vai's signature guitars? What would it be like to be a famous movie star? What would it be like to be married to Uma Thurman?

In the end, though, there's only one sure-fire way to know what it would be like. It's the most important test of all. Sure, an experience may tick all of the boxes on paper, but reality is messy and complicated, and very few experiences can be completely summed up by ticks in boxes.

And so it goes with software. We may work with the customer to build a detailed and precise requirements specification, setting out explicitly what boxes the software will need to tick for them. But there's no substitute for trying the software for themselves. From that experience, they will learn more than weeks or months or years of designing boxes to tick.

We're on a hiding to nothing sitting in rooms trying to force our customers to tell us what they really want. And the more precise and detailed the spec, the more suspicious I am of it. bottom line is they just don't know. But if you ask them, they will tell you. Something. Anything.

Now let me tell you how guitar custom shops - the good ones - operate.

They have a conversation with you about what guitar you want them to create for you. And then they build a prototype of what you asked for. And then - and this is where most of the design magic happens - they get you to play it, and they watch and they listen and they take notes, and they learn a little about what kind of guitar you really want.

Then they iterate the design, and get you to try that. And then rinse and repeat until your money runs out.

With every iteration, the guitar's design gets a little bit less wrong for you, until it's almost right - as right as they can get it with the time and money available.

Custom guitars can deviate quite significantly from what the customer initially asked for. But that is not a bad thing, because the goal here is to make them a guitar they really need; one that really suits them and their playing style.

In fact, I can think of all sorts of areas of life where what I originally asked for is just a jumping-off point for finding out what I really needed.

This is why I believe that testing - and then iterating - is most importantly a requirements discipline. It needs to be as much, if not more, about figuring out what the customer really needs as it is about finding out if we delivered what they asked for.

The alternative is that we force our customers to live with their first answers, refusing to allow them - and us - to learn what really works for them.

And anyone who tries to tell you that it's possible to get it right - or even almost right - first time, is a ninny. And you can tell them I said that.


September 8, 2014

Iterating Is Fundamental

Just like it boggles my mind that, in this day and age of electric telephones and Teh Internets, we still debate whether an invisible man in the sky created the entire universe in 6 days, so too is my mind boggled that - in 2014 - we still seem to be having this debate about whether or not we should iterate our software designs.

To me, it seems pretty fundamental. I struggle to recall a piece of software I've worked on - of any appreciable complexity or sophistication - where getting it right first time was realistic. On my training courses, I see the need to take multiple passes on "trivial" problems that take maybe an hour to solve. Usually this is because, while the design of a solution may be a no-brainer, it's often the case that the first solution solves the wrong problem.

Try as I might to spell out the requirements for a problem in clear, plain English, there's still a need for me to hover over developers' shoulders and occasionally prod them to let them know that was not what I meant.

That's an example of early feedback. I would estimate that at least half the pairs in the average course would fail to solve the problem if I didn't clear up these little misunderstandings.

It's in no way an indictment of those developers. Put me in the exact same situation, and I'm just as likely to get it wrong. It's just the lossy, buggy nature of human communication.

That's why we agree tests; to narrow down interpretations until there's no room for misunderstandings.

In a true "waterfall" development process - bearing in mind that, as I've said many times, in reality there's no such thing - all that narrowing down would happen at the start, for the entire release. This is a lot of work, and requires formalisms and rigour that most teams are unfamiliar with and unwilling to attempt.

Part of the issue is that, when we bite off the whole thing, it beecomes much harder to chew and much harder to digest. Small, frequent releases allow us to focus on manageable bitesized chunks.

But the main issue with Big Design Up-Front is that, even if we pin down the requirements precisely and deliver a bug-free implementation of exactly what was required, those requirements themselves are open to question. Is that what the customer really needs? Does it, in reality, solve their problem?

With the best will in the world, validating a system's requirements to remove all doubt about whether or not it will work in the real world, when the system is still on the drawing board, is extremely difficult. At some point, users need something that's at the very least a realistic approximation of the real system to try out in what is, at the very least, a realistic approximation of the real world.

And here's the the thing; it's in the nature of software that a realistic approximation of a program is, in effect, the program. Software's all virtual, all simulation. The code is is the blueprint.

So, in practice, what this means is that we must eventually validate our software's design - which is the software itself - by trying out a working version in the kinds of environments it's intended to be used in to try and solve the kinds of problems the software's designed to solve.

And the sooner we do that, the sooner we learn what needs to be changed to make the software more fit for purpose.

Put "agility" and "business change" to the back of your mind. Even if the underlying problem we want to solve stays completely static throughout, our understanding of it will not.

I've seen it time and again; teams agonise over features and whether or not that's what the customer really needs, and then the software's released and all that debate becomes academic, as we bump heads with the reality of what actually works in the real world and what they actually really need.

Much - maybe most - of the value in a software product comes as a result of user feedback. Twitter is a classic example. Look how many features were actually invented by the users themselves. We invented the Retweet (RT). We invented addressing tweets to users (using @). We invented hastags (#) to follow conversations and topics. All of the things that make tweets go viral, we invented. Remember that the founders of Twitter envisioned a micro-blogging service in the beginning. Not a global, open messaging service.

Twitter saw what users were doing with their 140 characters, and assimilated it into the design, making it part of the software.

How much up-front design do you think it would have taken them to get it right in the first release? Was their any way of knowing what users would do with their software without giving them a working version and watching what they actually did? I suspect not.

That's why I believe iterating is fundamental to good software design, even for what many of us might consider trivial problems like posting 140-character updates on a website.

There are, of course, degrees of iterativeness (if that's a word). At one extreme, we might plan to do only one release, to get all the feedback once we think the software is "done". But, of course, it's never done. Which is why I say that "waterfall" is a myth. What typically happens is that teams do one very looooong iteration, which they might genuinely believe is the only pass they're going to take at solving the problem, but inevitably when the rubbers meets the road and working software is put in front of end users, changes become necessary. LOTS OF CHANGES.

Many teams disguise these changes by re-classifying them as bugs. Antony Marcano has written about the secret backlogs lurking in many a bug tracking system.

Ambiguity in the original spec helps with this disguise: is it what we asked for? Who can tell?

Test-driven design processes re-focus testers on figuring our the requirements. So too does the secret backlog, turning testers into requirements analysts in all but name only, who devote much of their time to figuring out in what ways the design needs to change to make it more useful.

But the fact remains that producing useful working software requires us to iterate, even if we save those iterations for last.

It's for these reasons that, regardless of the nature of the problem, I include iterating as one of my basics of software development. People may accuse me of being dogmatic in always recommending that teeams iterate their designs, but I really do struggle to think of a single instance in my 30+ years of programming when that wouldn't have been a better idea than trying to get it absolutely right in one pass. And, since we always end up iterating anyway, we might as well start as we will inevitably go on, and get some of that feedback sooner.

There may be those in the Formal Methods community, or working on safety-critical systems, who argue that - perhaps for compliance purposes - they are required to follow a waterfall process. But I've worked on projects using Formal Methods, and consulted with teams doing safety-critical systems development, and what i see the good ones doing is faking it to tick all the right boxes. The chassis may look like a waterfall, but under the hood, it's highly iterative, with small internal releases and frequent testing of all kinds. Because that's how we deliver valuable working software.








August 12, 2014

TDD is TDD (And Far From Dead)

Now, it would take enormous hubris for me to even suggest that this blog post that follows is going to settle the "What is TDD?" "Is TDD dead?" "Did weazels rip my TDD?" debates that have inexplicably sprung up around and about the countryside of late.

But it will. (In my head, at any road.)

First of all, what is TDD? I'm a bit dismayed that this debate is still going on, all these years later. TDD is what it always was, right from the time the phrase appeared:

Test-driven Development = Test-driven Design + Refactoring

Test-driven Design is the practice of designing our software to pass tests. They can be any kind of tests that software can pass: unit tests, integration tests, customer acceptance tests, performance tests, usability tests, code quality tests, donkey jazz hands tests... Any kind of tests at all.

The tests provide us with examples of how the software must be - at runtime, at design time, at tea time, at any time we say - which we generalise with each new test case to evolve a design for software that does a whole bunch of stuff, encompassed by the set of examples (the suite of tests) for that software.

We make no distinction in the name of the practice as to what kind of tests we're aiming to pass. We do not call it something else just because the tests we're driving our design with happen not to be unit tests.

Refactoring is the practice of improving the internal design of our software to make it easier to change. This may mean making the code easier for programmers to understand, or generalising duplicate code into some kind of reusable abstraction like a parameterised method or a new module, or unpicking a mess of dependencies to help localise the impact of making changes.

As we're test-driving our designs, it's vitally important to keep our code clean and maintainable enough to allow us to evolve it going forward to pass new tests. Without refactoring, Test-driven Design quickly becomes hard going, and we lose the ability to adapt to changes and therefore to be agile for our customer.

The benefits of TDD are well understood, and backed up by some hard data. Software that is test-driven tends to be more reliable. It tends to be simpler in its design. Teams that practice TDD tend to find it easier to achieve continuous delivery. From a business perspective, this can be very valuable indeed.

Developers who are experienced in TDD know this to be true. Few would wish to go back to the Bad Old Days before they used it.

That's not say that TDD is the be-all and end-all of software design, or that the benefits it can bring are always sufficient for any kind of software application.

But it is very widely applicable in a wide range of applications, and as such has become the default approach - a sort of "start for ten" - for many teams who use it.

It is by no means dead. There are more teams using it today than ever before. And, as a trainer, I know there are many more that aspire to try it. It's a skill that's highly in demand.

Of course, there are teams who don't succeed at learning TDD. Just like there are people who don't succeed at learning to play the trombone. The fact that not everybody succeeds at learning it does not invalidate the practice.

I've trained and coached thousands of developers in TDD, so I feel I have a good overview of how folk get on with it. Many - most, let's be honest - seriously underestimate the learning curve. Like the trombone, it may take quite a while to get a tune out of it. Some teams give up too easily, and then blame the practice. Many thousands of developers are doing it and succeeding with it. I guess TDD just wasn't for you.

So there you have it, in a nutshell: TDD is what it always was. It goes by many names, but they're all pseudonyms for TDD. It's bigger today than it ever was, and it's still growing - even if some teams are now calling it something else.

There. That's settled, then.





July 31, 2014

My Top 5 Most Under-used Dev Practices

So, due to a last-minute change of plans, I have some time today to fill. I thought I'd spend it writing about those software development practices that come highly recommended by some, but - for whatever reason - almost no teams do.

Let's count down.

5. Mutation Testing - TDD advocates like me always extol the benefits of having a comprehensive suiite of tests we can run quickly, so we can get discover if we've broken our code almost immediately.

Mutation testing is a technique that enables us to ask the critical question: if our code was broken, would our tests show it?

We deliberately introduce a programming error - a "mutation" - into a line of code (e.g., turn a + into a -, or > into a <) and then run our tests. If a test fails, we say our test suite has "killed the mutant". We can be more assured that if that particular line of code had an error, our tests would show it. If no tests fail, that potentially highlights an gap in our test suite that we need to fill.

Mutation testing, done well, can bring us to test suites that offer very high assurance - considerably higher than I've seen most teams achieve. And that extra assurance tends to bring us economic benefits in terms of catching more bugs sooner, saving us valuable time later.

So why do so few teams do it? Well, tool support is one issue. The mutation testing tools available today tend to have a significant learning curve. They can be fiddly, and they can throw up false positives, so teams can spend a lot of time chasing ghosts in their test coverage. It takes some getting used to.

In my own experience, though, it's worth working past the pain. The pay-off is often big enough to warrant a learning curve.

So, in summary, reason why nobody does it : LEARNING CURVE.

4. Visualisation - pictures were big in the 90's. Maybe a bit too big. After the excesses of the UML days, when architects roamed the Earth feeding of smaller prey and taking massive steaming dumps on our code, visual modeling has - quite understandably - fallen out of favour. So much so that many teams do almost none at all. "Baby" and "bathwater" spring to mind.

You don't have to use UML, but we find that in collaborative design, which is what we do when we work with customers and work in teams, a picture really does speak a thousand words. I still hold out hope that one day it will be commonplace to see visualisations of software designs, problem domains, user interfaces and all that jazz prominently displayed in the places where development teams work. Today, I mainly just see boards crammed with teeny-weeny itty-bitty index cards and post-it notes, and the occasional wireframe from the UX guy, who more often than not came up with that design without any input at all from the team.

The effect of lack of visualisation on teams can be profound, and is usually manifested in the chaos and confusion of a code base that comprises several architectures and a domain model that duplicates concepts and makes little to no sense. If you say you're doing Domain-driven Design - and many teams do - then where are your shared models?

There's still a lot of mileage in Scott Ambler's "Agile Modeling" book. Building a shared understanding of a complex problem or solution design by sitting around a table and talking, or by staring at a page of code, has proven to be uneffective. Pictures help.

In summary, reason why so few do it: MISPLACED AGILE HUBRIS

3. Model Office - I will often tell people about this mystical practice of creating simulated testing environments for our software that enable us to see how it would perform in real-world scenarios.

NASA's Apollo team definittely understood the benefits of a Model Office. Their lunar module simulator enabled engineers to try out solutions to systemm failures on the ground before recommending them to the imperilled astronauts on Apollo 13. Tom Hanks was especially grateful, but Bill Paxton went on to star in the Thunderbirds movie, so it wasn't all good.

I first heard about them doing a summer stint in my local W H Smith in the book department. Upstairs, they had a couple of fake checkouts and baskets of fake goods with barcodes.

Not only did we train in their simulated checkout, but they also used them to analyse system issues and to plan IT changes, as well as to test those changes in a range of "this could actually happen" scenarios.

A Model Office is a potentially very powerful tool for understanding problems, for planning solutions and for testing them - way more meaningful than acceptance tests that were agreed among a bunch of people sitting in a room, many of whom have never even seen the working environment in which the software's going to be used, let alone experienced it for themselves.

There really is no substitute for the real thing; but the real thing comes at a cost, and often the real thing is quite busy, actually, thank you very much. I mean, dontcha just hate it when you're at the supermarket and the checkout person is just learning how it all works while you stand in line? And mistakes that get made get made with real customers and real money.

We can buy ourselves time, control and flexibility by recreating the real thing as faithfully as possible, so we can explore it at our leisure.

Time, because we're under no pressure to return the environment to business use, like we would be if it was a real supermarket checkout, or a real lunar module.

Control, because we can deliberately recreate scenarios - even quite rare and outlandish ones - as often as we like, and make it exactly the same, or vary it, as we wish. One of the key reasons I believe many business systems are not very robust is because they haven't been tested in a wide-enough range of possible circumstances. In real life, we might have to wait weeks for a particular scenario to arise.

Flexibility, because in a simulated environment, we can do stuff that might be difficult or dangerous in the real world. We can try out the most extraordinary situations, we can experiment with solutions when the cost of failure is low, and we can explore the problem and possible solutions in ways we just couldn't or wouldn't dare to if real money, or real lives, or real ponies were at stake.

For this reason,, from me, Model Offices come very highly recommended. Which is very probably why nobody uses them.

Reason why nobody does it - NEVER OCCURRED TO THEM

2. Testing by Inspection - This is another of those blind spots teams seem to have about testing. Years of good research have identified reading the code to look for errors as one of the most - if not the most - effective and efficient ways of finding bugs.

Now, a lot of teams do code reviews. It's a ritual humiliation many of us have to go through. But commonly these reviews are about things like coding style, naming conventions, design rules and so forth. It's vanishingly rare to meet a team who get around a computer, check out some code and ask "okay, will this work?"

Testing by inspection is actually quite a straightforward skill, if we want it to be. A practice like guided inspection, for example, simply requires us to pick some interesting test cases, and step through the code, effectively executing it in our heads, asking questions like "what should be true at this point?" and "when might this line of code not work?"

If we want to, we can formalise that process to a very high degree of rigour. But the general pattern is the same; we make assertions about what should be true at key points during the execution of our code, we read the code and dream up interesting test cases that will cause those parts of the code to be executed and ask those questions at the appropriate times. When an inspection throws up interesting test cases that our code doesn't handle, we can codify this knowledge as, say, automated unit tests to ensure that the door is closed to that particular bug permanently.

Do not underestimate the power of testing by inspection. It's very rare to find teams producing high-integrity software who don't do it. (And, yes, I'm saying it's very rare to find teams producing high-integrity software.)

But, possibly because of associations with the likes of NASA, and safety-critical software engineering in general, it has a reputatioon for being "rocket science". It can be, if we choose to go that far. But in most cases, it can be straightforward, utilising things we already know about computer programming. Inspections can be very economical, and can reap considerable rewards. And pretty much anyone who can program can do them. Which is why, of course, almost nobody does.

Reason why nobody does it - NASA-PHOBIA

1. Business Goals - Okay, take a deep breath now. Imminent Rant Alert.

Why do we build software?

There seems to be a disconnect between the motivations of developers and their customers. Customers give us money to build software that hopefully solves their problems. But, let's be honest now, a lot of developers simply could not give two hoots about solving the customer's problems.

Which is why, on the vast majority of software teams, when I ask them what the ultimate business goals of what they're doing are, they just don't know.

Software for the sake of software is where our heads are mostly at. We buiild software to build software.

Given a free reign, what kind of software do developers like to build? Look on Github. What are most personal software projects about?

We don't build software to improve care co-ordination for cancer sufferers. We don't build software to reduce delivery times for bakeries. We don't build software to make it easier to find a hotel room with fast Wi-Fi at 1am in a strange city.

With our own time and resources, when we work on stuff that interests us, we won't solve a problem in the real world. We'll write another Content Management System. Or an MVC framework. Or another testing tool. Or another refactoring plug-in. Or another VCS.

The problems of patients and bakers and weary travelers are of little interest to us, even though - in real life - we can be all of these things ourselves.

So, while we rail at how crappy and poorly thought-out the software we have to use on a daily basis tends to be ("I mean, have they never stayed in a hotel?!"), our lack of interest in understanding and then solving these problems is very much at the root of that.

We can be so busy dreaming up solutions that we fail to see the real problems. The whole way we do development is often a testament to that, when understanding the business problem is an early phase in a project that, really, shouldn't exist until someone's identified the problem and knows at least enough to know it's worth writing some software to address it.

Software projects and products that don't have clearly articulated, testable and realistic goals - beyond the creation of software for its own sake - are almost guaranteed to fail; for the exact same reason that blindly firing arrows in random directions with your eyes closed is almost certainly not going to hit a valuable target. But this is what, in reality, most teams are doing.

We're a solution looking for a problem. Which ultimately makes us a problem. Pretty much anyone worth listening to very, very strongly recommends that software development should have clear and testable business goals. So it goes without saying that almost no teams bother.

Reason why so few teams do it - APATHY





July 16, 2014

What Level Should We Automate Most Of Our Tests At?

So this blog post has been a long time in the making. Well, a long time in the procrastinating, at any rate.

I have several clients who have hit what I call the "front-end automated test wall". This is when teams place greatest emphasis on automating acceptance tests, preferring to verify the logic of their applications at the system level - often exercised through the user interface using tools like Selenium - and rely less (or not at all, in some cases) on unit tests that exercise the code at a more fine-grained level.

What tends to happen when we do this is that we end up with large test suites that require much set-up - authentication, database stuff, stopping and starting servers to reset user sessions and application state, and all the fun stuff that comes with system testing - and run very slowly.

So cumbersome can these test suites become that they slow development down, sometimes to a crawl. If it takes half an hour to regression test your software, that's going to make the going tough for Clean Coders.

The other problem with these high-level tests is that, when they fail, it can take a while to pin down what went wrong and where it went wrong. As a general rule of thumb, it's better to have tests that only have one reason to fail, so when something breaks it's alreay pretty well pinpointed. Teams who've hit the wall tend to spend a lot time debugging.

And then there's the modularity/reuse issue: when the test for a component is captured at a much higher level, it can be tricky to take that chunk and turn it into a reusable chunk. Maybe the risk calculation component of you web application could also be a risk calculation component of a desktop app, or a smartwatch app. Who knows? But when its contracts are defined through layers of other stuff like web pages and wotnot, it can be difficult to spin it out into a product in its own right.

For all these reasons, I follow the rule of thumb: Test closest to the responsibility.

One: it's faster. Every layer of unnecessary wotsisname the tests have to go through to get an answer adds execution time and other overheads.

Two: it's easier to debug. Searching for lost car keys gets mighty complicated when your car is parked three blocks away. If it's right outside the front door, and you keep the keys in a bowl in the hallway, you should find them more easily.

Three: it's better for componentising your software. You may call them "microservices" these days, but the general principles is the same. We build our applications by wiring together discrete components that each have a distinct responsibility. The tests that check if a component fulfils its reponsibility need to travel with that components, if at all possible. If only because it can get horrendously difficult to figure out what's being tested where when we scatter rules willy nilly. The risk calculation test wants to talk to the Risk Calculator component. Don't make it play Chinese Whsipers through several layers of enterprise architecture.

Sometimes, when I suggest this, developers will argue that unit tests are not acceptance tests, because unit tests are not written from the user's perspective. I believe - and find from experience - that this is founded on an artificial distinction.

In practice, an automated acceptance test is just another program written by a programmer, just like a unit test. The programmer interprets the user's requirements in both cases. One gives us the illusion of it being the customer's test, if we want it to be. But it's all smoke and mirrors and given-when-then flim-flam in reality.

The pattern, known of old, of sucking test data provided by the users into parameterised automated tests is essentially what our acceptance test automation tools do. Take Fitnesse, for example. Customer enters their Risk Calculation inputs and expected outputs into a table on a Wiki. We write a test fixture that inserts data form the table into program code that we write to test our risk calculation logic.

We could ask the users to jot those numbers down onto a napkin, and hardcode them into our test fixture. Is it still the same test? It it still an automated acceptance test? I believe it is, to all intents and purposes.

And it's not the job of the user interface or our MVC implementation or our backend database to do the risk calculation. There's a distinct component - maybe even one class - that has that responsibility. The rest of the architecture's job is to get the inputs to that component, and marshall the results back to the user. If the Risk Calculator gets the calculation wrong, the UI will just display the wrong answer. Which is correct behaviour for the UI. It should display whatever output the Risk Calculator gives it, and display it correctly. But whether or not it's the correct output is not the UI's problem.

So I would test the risk calculation where the risk is calculated, and use the customer's data from the acceptance test to do it. And I would test that the UI displays whatever result it's given correctly, as a separate test for the UI. That's what we mean by "separation of concerns"; works for testing, too. And let's not also forget that UI-level tests are not the same thing as system or end-to-end tests. I can quite merrily unit test that a web template is rendered correctly using test data injected into it, or that an HTML button is disabled running inside a fake web browser. UI logic is UI logic.

And I know some people cry "foul" and say "but that's not acceptance testing", and "automated acceptance tests written at the UI level tend to be nearer to the user and therefore more likely to accurately reflect their requirements."

I say "not so fast".

First of all, you cannot automate user acceptance testing. The clue is in the name. The purpose of user acceptance testing is to give the user confidence that we delivered what they asked for. Since our automated tests are interpretations of those requirements - eevery bit as much as the implementations they're testing - then, if it were my money, I wouldn't settle for "well, the acceptance tests passed". I'd want to see those tests being executed with my own eyes. Indeed, I'd wanted to execute them myself, with my own hands.

So we don't automate acceptance tests to get user acceptance. We automate acceptance tests so we can cheaply and effectively re-test the software in case a change we've made has broken something that was previously working. They're automated regression tests.

The worry that the sum total of our unit tests might deviate from what the users really expected is mitigated by having them manually execute the acceptance tests themselves. If the software passes all of their acceptance tests AND passes all of the unit tests, and that's backed up by high unit test assurance - i.e., it is very unlikely that the software could be broken from the user's perspsctive without any unit tests failing - then I'm okay with that.

So I still have user acceptance test scripts - "executable specifications" - but I rely much more on unit tests for ongoing regression testing, because they're faster, cheaper and more useful in pinpointing failures.

I still happily rely on tools like Fitnesses to capture users' test data and specific examples, but the fixtures I write underneath very rarely operate at a system level.

And I still write end-to-end tests to check that the whole thing is wired together correctly and to flush out configuration and other issues. But they don't check logic. They just the engine runs when you turn the key in the ignition.

But typically I end up with a peppering of these heavyweight end-to-end tests, a feathering of tests that are specifically about display and user interaction logic, and the rest of the automated testing iceberg is under the water in the form of fast-running unit tests, many of which use example data and ask questions gleaned from the acceptance tests. Because that is how I do design. I design objects directly to do the work to pass the acceptance tests. It's not by sheer happenstance that they pass.

And if you simply cannot let go of the notion that you must start by writing an automated acceptance test and drive downwards from there, might I suggest that as new objects emerge in your design, you refactor the test assertions downwards also and push them into new tests that sit close to those new objects, so that eventually you end up with tests that only have one reason to fail?

Refactorings are supposed to be behaviour-preserving, so - if you're a disciplined refactorer - you should end up with a cluster of unit tests that are logically directly equivalent to the original high-level acceptance test.

There. I've said it.






June 8, 2014

Reliability & Sustaining Value Are Entirely Compatible Goals

This is a short blog post about having your cake and eating it.

The Agile Software Development movement has quite rightly shifted the focus in what we do from delivering to meet deadlines to delivering sustainable value.

A key component in sustaining the delivery of value through software is how much it costs to change our code.

The Software Craftsmanship schtick identifies primary factors in the cost of changing software; namely:

1. How easy is it to understand the code?

2. How complicated is the code?

3. How much duplication is there in the code?

4. How interdependent are all the things in the code?

5. How soon can we find out if the change we made broke the code?

By taking more care over these factors, we find that it's possible to write software in a way that not only delivers value today, but doesn't impede us from delivering more value tomorrow. In the learning process that is software development, this can be critical to our success.

And it's a double win. Because, as it turns out, when we take more care over readability, simplicity, removing duplication, managing dependencies and automating tests, we also make our software more reliable in the first instance.

Let us count the ways:

1. Code that's easier to understand is less likely to suffer from bugs caused by misunderstandings.

2. Code that is simpler tends to have less ways to go wrong - fewer points of failure - to achieve the same goals

3. Duplicated code can include duplicated bugs. Anyone who's ever "reused" code from sites like The Code Project will know what I mean.

4. Just as changes can propagate through dependencies, so can failures. If a critical function is wrong, and that function is called in many places and in many scenarios, then we have a potential problem. It's possible for a single bug in a single line of code to bring down the entire system. We call them "show-stoppers". It's for this reason I dreamed up the Dependable Dependencies Principle for software design.

5. High levels of automated test assurance - notice I didn't say "coverage" - tends to catch more programming errors, and sooner. This makes it harder for bugs to slip unnoticed into the software, which can also have economic benefits.


So there's your cake. Now eat it.






May 11, 2014

When Really To Use Mocks? First Ask: "What Are Mocks?"

I should probably stay out of this, but just couldn't resist sharing my own thoughts about Uncle Bob's latest blog post on the subject of when to use mock objects.

He makes some fair points about isolation and using test doubles at architectural boundaries, as well as testing exceptional paths and making "random" things repeatable using the built-in features of many mocking frameworks that allow developers to deliberatly throw exceptions and return hardcoded dates and times and all that sort of thing.

But, and we need to be clear about this, this is the not intended purpose of mock objects, as far as I understand it.

Mocks exist to address a specific design need - not a testing need. The inventors of mock objects were answering the question: how do we test-drive the interactions between the key roles in our design?

How can I write a test that fails - a specification in test form - because a collaboration between two objects didn't take place in the way I wanted it to?

Enter stage right mock objects, and their - now legion - associated enabling frameworks, like JMock, Mockito, MockStockAndTwoSmokingBarrels, MockTheWeek, and, of course, Mockney Radio 1 DJ.

The idea's very simple; in thinking about object oriented design, we can organise our thoughts - expressed perhaps using simple modeling tools like index cards or UML diagrams - into three categories:

1. Roles - what roles do objects play in achieving a goal?

2. Responsibilities - what does each role do in achieving the goal?

3. (And here's the crux of good OO design) Collaborations - how do these roles collaborate - by sending messages to each other - to co-ordinate getting the job done?

Now, however you're driving the implementation of your design, in OO paradigms - indeed, in any paradigm where code is organised into modules that do chunks of work and invoke functions on other modules to do the rest - those 3 things form the basis of how code is organised.

But we can come at an implementation from different angles, all of which can overlap. I might choose to test-drive responsibilities using traditional assertion-based tests, and isolate that code from external systems or frameworks using stubs. I might choose to drive out key collaborations in my code using mock objects. I might sketch out an OO design and build the thing bottom up, I might start by test-driving the outermost objects and work my way in, using test doubles to defer the implementations further down the call stack. Tomato. Tomato. (That sounds funnier than it reads.)

You might just start by passing the tests in the simplest way possible and refactoring to an OO design, allowing roles, responsibilities and collaborations to emerge completely organically, with design decisions coming down purely to "what's the cleanest code that will pass these tests?"

Or you might plan the whole OO design up-front and just implement it.

In practice, these are two extremes: no up-front planning of our OO design requires very strong refactoring muscles. Not recommended for mere mortals like me and you.

Similarly, trying to think of everything up-front tends to take a very long time, and inevitably we discover as we get into the implementation details that the map is not the terrain. Hence, also not recommended for us mere mortals.

And so, we strike a balance. And that balance differs from person to person and from one team cultue to the next.

The same also goes for the balance between "classic TDD", which is mostly feedback-driven when it comes to OO design, but not completely, and the "London School", which relies on more up-front thought and planning about key roles (interfaces) and the collaborations between them than classic TDD.

The lines are necessarily blurred between these different approaches, just as the lines are blurred between mocking and stubbing in many popular mock object frameworks.

At the risk of giving away pithy and meaningless advice, you should use what's appropriate. But it helps to be clear in your mind what question your test is asking. Are you asking whether a calculation was done correctly, perhaps using hardcoded data returned by a stub pretending to be the database, or are you asking if the database request was built and sent correctly, regardless of the response?

You should also be very careful not to fall back on mock objects as a crutch for making code that's untestable because of dependencies more testable. For that way lies the madness of baking in a bad design. Yes, you can get more tests in there, but those tests will likely as not expose the internal interactions of your legacy code, making them doubly difficult to refactor.

Ultimately, remember this: mock obects - test doubles used to test interactions - are supposed to be an OO design tool that enables us to drive out the collaborations in our architecture by writing failing tests. Their purpose is not specifically about isolating code to make it more testable. Indeed, testability is not the point of mock objects - it's a nice side-effect of doing code-level design with them.

Your own comfort-level - your tolerance for mocks, if you like - will probably be different to mine. Mine is quite low. I like to mosty discover designs, but undoubtedly will test-drive collaborations at system boundaries, as Uncle Bob recommends.

But it's not my way or the highway on this matter. If your OO designs are effectively modular, with cohesive and loosely-coupled components that perform a specific job and have few collaborations, mocks won't hurt you and can be an effective TDD aid. That's their original purpose, after all. TDD-ing code in the Tell, Don't Ask style is undoubtedy easier with mocks.

We have no reason to doubt that well-known exponents of the London School of TDD are successful in their approach. Even though we see many teams fall foul of "mock abuse", relying on mocking frameworks to make bad designs testable.

Likewise, we have no reason to doubt that key exponents of classic TDD are not equally successful in the designs their more feedback-driven approach reaps. Even though we see many teams fall foul of both too little up-front planning, and too little after-the-fact refactoring to keep the code clean.

So, when to use mocks? When it makes sense





April 11, 2014

Reliable Everyday Software London, May 15th

Announcing the first of what I hope will be a regular meet-up in London for folk interested in bringing more reliable software to the mainstream.

Reliable Everyday Software London (#RESL) aims to bring together communities of practitioners, researchers, teachers and other interested folk to think about, talk about, and maybe even do something about making some of the techniques and tools we typically associate with more critical software into everyday software development.

Many of us share a belief that some of these techniques have been unwisely overlooked by the vast majority of teams, and know from experience that there are times on every project, and parts of every code base, that would benefit from a more rigorous eye.

We also know that, on a daily basis, we suffer at the hands of software that, while you might not think of it as "critical", can cause a constant low level of pain and inconvenience which every now and again explodes into something serious. Whether it's a utility bill payment that somehow got "lost in the post", or losing that vital piece of data, or a sensitive piece of information accidentally exposed, or getting stuck in an infinite logical loop that means we can't see our email because we forgot our password (and they'll only send a password reminder to that email address we can't see - grrr!), software defects have the potential to ruin our lives. At best, they are a constant low-level annoyance that eats up time and raises blood pressures (thus shortening our lives.)

It doesn't have to be this way. If the communities of practitioners and researchers can get past the "them & us" mentality that currently pervades, with both sides believing the other side are "doing it wrong", we may well see that we have much to learn from each other. The potential benefits of injecting a healthy dose of greater rigour into everyday software development, and a healthy dose of everyday realism into research, cannot be overstated.

So, to foster a spirit of enquiry and co-operation, we'll be meeting at Unruly Media at 6:30-8pm on Thursday May 15th to kick things off. Hope you can joins us. But if you can't, please sign up for our meetup.com group anyway and join in the discussion.