August 10, 2015
A Hierarchy Of Software Design NeedsDesign is not a binary proposition. There is no clear dividing line between a good software design and bad software design, and even the best designs are compromises that seek to balance competing forces like performance, readability, testability, reuse and so on.
When I refactor a design, it can sometimes introduce side-effects - namely, other code smells - that I deem less bad than what was there before. For example, maybe I have a business object that renders itself as HTML - bad, bad, bad! Right?
The HTML format is likely to change more often than the object's data schema, and we might want to render it to other formats. So it makes sense to split out the rendering part to a separate object. But in doing so, we end up creating "feature envy" - an unhealthy high coupling between our renderer and the business object so it can get the data in needs - in the process.
I consider the new feature envy less bad than the dual responsibility, so I live with it.
In fact, there tends to be a hierarchy of needs in software design, where one design issue will take precedence over another. It's useful, when starting out, to know what that hierarchy of needs is.
Now, the needs may differ depending on the requirements of our design - e.g., on a small-memory device, memory footprint matters way more than it does for desktop software usually - but there is a fairly consistent pattern that appears over and over in the majority of applications.
There are, of course, a universe of qualities we may need to balance. But let's deal with the top six to get you thinking:
1. The Code Must Work
Doesn't matter how good you think the design is if it doesn't do what the customer needs. Good design always comes back to "yes, but does it pass the acceptance tests?" If it doesn't, it's de facto a bad design, regardless.
2. The Code Must Be Easy To Understand
By far the biggest factor in the maintainability of code is whether or not programmers can understand it. I will gladly sacrifice less vital design goals to make code more readable. Put more effort into this. And then put even more effort into it. However much attention you're paying to readability, it's almost certainly not enough. C'mon, you've read code. You know it's true.
But if the code is totally readable, but doesn't work, then spend more time on 1.
3. The Code Must Be As Simple As We Can Make It
Less code generally means a lower cost of maintenance. But beware; you can take simplicity too far. I've seen some very compact code that was almost intractable to human eyes. Readability trumps simplicity. And, yes, functional programmers, I'm particularly looking at you.
4. The Code Must Not Repeat Itself
The opposite of duplication is reuse. Yes it is: don't argue!
Duplication in our code can often give us useful clues about generalisations and abstractions that may be lurking in there that need bringing out through refactoring. That's why "removing duplication" is a particular focus of the refactoring step in Test-driven Development.
Having said that, code can get too abstract and too general at the expense of readability. Not everything has to eventually turn into the Interpreter pattern, and the goal of most projects isn't to develop yet another MVC framework.
In the Refuctoring Challenge we do on the TDD workshops, over-abstracting often proves to be a sure-fire way of making code harder to change.
5. Code Should Tell, Not Ask
"Tell, Don't Ask" is a core pillar of good modular -notice I didn't say "object oriented" - code. Another way of framing it is to say "put the work where the knowledge is". That way, we end up with modules where more dependencies are contained and fewer dependencies are shared between modules. So if a module knows the customer's date of birth, it should be responsible for doing the work of calculating the customer's current age. That way, other modules don't have to ask for the date of birth to do that calculation, and modules know a little bit less about each other.
It goes by many names: "encapsulation", "information hiding" etc. But the bottom line is that modules should interact with each other as little as possible. This leads to modules that are more cohesive and loosely coupled, so when we make a change to one, it's less likely to affect the others.
But it's not always possible, and I've seen some awful fudges when programmers apply Tell, Don't Ask at the expense of higher needs like simplicity and readability. Remember simply this: sometimes the best way is to use a getter.
6. Code Should Be S.O.L.I.D.
You may be surprised to hear that I put OO design principles so far down my hierarchy of needs. But that's partly because I'm an old programmer, and can vaguely recall writing well-designed applications in non-OO languages. "Tell, Don't Ask", for example, is as do-able in FORTRAN as it is in Smalltalk.
Don't believe me? Then read the chapter in Bertrand Meyer's Object Oriented Software Construction that deals with writing OO code in non-OO languages.
From my own experiments, I've learned that coupling and cohesion have a bigger impact on the cost of changing code. A secondary factor is substitutability of dependencies - the ability to insert a new implementation in the slot of an old one without affecting the client code. That's mostly what S.O.L.I.D. is all about.
This is the stuff that we can really only do in OO languages that directly support polymorphism. And it's important, for sure. But not as important as coupling and cohesion, lack of duplication, simplicity, readability and whether or not the code actually works.
Luckily, apart from the "S" in S.O.L.I.D. (Single Responsibility), the O.L.I.D. is fairly orthogonal to these other concerns. We don't need to trade off between substitutability and Tell, Don't Ask, for example. They're quite compatible, as are the other design needs - if you do it right.
In this sense, the trade off is more about how much time I devote t thinking about S.O.L.I.D. compared to other more pressing concerns. Think about it: yes. Obsess about it: no.
Like I said, there are many, many more things that concern us in our designs - and they vary depending on the kind of software we're creating - but I tend to find these 6 are usually at the top of the hierarchy.
So... What's your hierarchy of design needs?
July 31, 2015
Triangulating Your Test CodeWhile we're triangulating our solutions in TDD, our source code ought to be getting more general with each new test case.
But it's arguably not just the solution that should be getting more general; our test code could probably be generalised, too.
Take a look at this un-generalised code for the first two tests in a TDD'd implementation of a Fibonacci sequence generator:
Jumping in at this point, we see that our solution is still hard-coded. The trick to triangulation is to spot the pattern. The pattern for the first two Fibonacci numbers is that they are the same as their index in the sequence (assuming a zero-based array).
We can generalise our list into a loop that generates the list using the pattern (see Bob Martin's post on the Transformation Priority Premise, or, what I more simply call triangulation patterns).
But we can also generalise our test code into a single parameterised test, using the pattern as the test name, so it reads more like the specification we hope our tests in TDD will become:
Now, because all subequent tests are going to follow the same pattern (we provide an index and check what the expected Fibonacci number is at that index), we could carry on reusing this parameterised test for the rest of the problem.
Then we'd have to generalise the name of the test - a key part of our test-driven specification - to the point where every single patterm (every rule) is summarised in one test. I no likey. It's much harder to read, and when a test case fails, it's not entirely clear which rule was broken.
So, what I like to do is keep a bit of duplication in order to have one generalised test for each patterm/rule in the specification.
So, continuing on, I might end up with:
Notice that, although these two test methods are duplication, I've taken the step of refactoring out the duplicated knowledge of how to create and interact with the object being tested. This kind of duplication in test code tends to hurt us most. Many teams report how tight coupling between tests and objects under test led to interfaces being much more expensive to change. So I feel this is a small compromise that aid readability while not sacrificing too much to duplication.
April 25, 2015
Continuous Inspection ScreencastIt's been quite a while since I did a screencast. Here's a new one about Continuous Inspection, which is a thing. (Oh yes.)
March 1, 2015
Continuous Inspection at NorDevConOn Friday, I spent a very enjoyable day at the Norfolk developer's conference NorDevCon (do you see what they did there?) It was my second time at the conference, having given the opening keynote last year, and it's great to see it going from strength to strength (attendance up 50% on 2014), and to see Norwich and Norfolk being recognised as an emerging tech hub that's worthy of inward investment.
I was there to run a workshop on Continuous Inspection, and it was a good lark. You can check out the slides, which probably won't make a lot of sense without me there to explain them - but come along to CraftConf in Budapest this April or SwanseaCon 2015 in September and I'll answer your questions.
You can also take a squint at (or have a play with) some code I knocked up in C# to illustrate a custom FxCop code rule (Feature Envy) to see how I implemented the example from the slides in a test-driven way.
I'm new to automating FxCop (and an infrequent visitor to .NET Land), so please forgive any naivity. Hopefully you get the idea. The key things to take away are: you need a model of the code (thanks Microsoft.Cci.dll), you need a language to express rules against that model (thanks C#), and you need a way to drive the implementation of rules by writing executable tests that fail (thanks NUnit). The fun part is turning the rule implementation on its own code - eating your own dog food, so to speak. Throws up all sorts of test cases you didn't think of. It's a work in progress!
I now plan, before CraftConf, to flesh the project out a bit with 2-3 more example custom rules.
Having enjoyed a catch-up with someone who just happens to be managing the group at Microsoft who are working on code analysis tools, I think 2015-2016 is going to see some considerable ramp-up in interest as the tools improve and integration across the dev lifecycle gets tighter. If Continuous Inspection isn't on your radar today, you may want to put it on your radar for tomorrow. It's going to be a thing.
Right now, though, Continuous Inspection is very much a niche pastime. An unscientific straw poll on social media, plus a trawl of a couple of UK job sites, suggests that less than 1% of teams might even be doing automated code analysis at all.
I predicted a few years ago that, as computers get faster and code gets more complex, frequent testing of code quality using automated tools is likely to become more desirable and more do-able. I think we're just on the cusp of that new era today. Today, code quality is an ad hoc concern relying on hit-and-miss practices like pair programming, where many code quality issues often get overlooked by pair who have 101 other things to think about, and code reviews, where issues - if they get spotted at all in the to-and-fro - are flagged up long after anybody is likely to do anything about them.
In related news, after much discussion and braincell-wrangling, I've chosen the name for the conference that will be superceding Software Craftsmanship 20xx later this year (because craftsmanship is kind of done now as a meme). Watch this space.
February 13, 2015
Intensive TDD, Continuous Inspection Recipes & Crappy Remote Collaboration ToolsA mixed bag for today's post, while I'm at my desk.
First up, after the Intensive TDD workshop on March 14th sold out (with a growing waiting list), I've scheduled a second workshop on Saturday April 11th, with places available at the insanely low price of £30. Get 'em while they're hot!
Secondly, I'm busy working on a practical example for a talk I'm giving at NorDevCon on Feb 27th about Continuous Inspection.
What I'm hoping to do is work through a simple example based on my Dependable Dependencies Principle, where I'll rig up an automated code analysis wotsit to find the most complex, most depended upon and least tested parts of some code to give early warning about where it might be most likely to be broken and might need better testing and simplifying.
To run this metric, you need 3 pieces of information:
* Cyclomatic Complexity of methods
* Afferent couplings per method
* Test coverage per method
Now, test coverage could mean different things. But for a short demonstration, I should probably keeep it simple and fairly brute force - e.g., % LOC reached by the tests. Not ideal, but in a short session, I don't want to get dragged into a discussion about coverage metrics. It's also a readily-available measure of coverage, using off-the-shelf tools, so it will save me time in preparing and allow viewers to try it for themselves without too much fuss and bother.
What's more important is to demonstrate the process going from identifying a non-functional requirement (e.g., "As the Architect, I want early warning about code that presents a highr risk of being unreliable so that I can work with the developers to get better assurance for it"), to implementing an executable quality gate using available tools in a test-driven manner (everybody forgets to agree tests for their metrics!), to managing the development process when the gate is in place. All the stuff that constitutes effective Continuous Inspection.
At time of writing, tool choice is split between a commercial code analysis tool called JArchitect, and SonarQube. It's a doddle to rig up in JArchitect, but the tool costs £££. It's harder to rig up in SonarQube, but the tools are available for free. (Except, of course, nothing's ever really free. Extra time taken to get what you want out of a tool also adds up to £££.) We'll see how it goes.
Finally, after a fairly frustrating remote pairing session on Wednesday where we were ultimately defeated by a combination of Wi-Fi, Skype, TeamViewer and generally bad mojo, it's occured to me that we really should be looking into remote collaboration more seriously. If you know of more reliable tools for collaboration, please tweet me at @jasongorman.
February 9, 2015
Mock Abuse: How Powerful Mocking Tools Can Make Code Even Harder To ChangeConversation turned today to that perennial question about mock abuse; namely that there are some things mocking frameworks enable us to do that we probably shouldn't ought to.
In particular, as frameworks have become more powerful, they've made it possible for us to substitute the un-substitutable in our tests.
Check out this example:
Because Orders invokes the static database access method getAllOrders(), it's not possible for us to use dependency injection to make it so we can unit test Orders without hitting the database. Boo! Hiss!
Along comes our mocking knight in shining armour, enabling me to stub out that static method to give a test-specific response:
Problem solved. Right?
Well, for now, maybe yes. But the mocking tool has not solved the problem that I still couldn't substitute CustomerData.getAllOrders() in the actual design if I wanted to (say, to use a different kind of back-end data store or a web service). So it's solved the "how do I unit test this?" problem, but not in a way that buys me any flexibility or solves the underlying design problem.
If anything, it's made things a bit worse. Now, if I want to refactor Orders to make the database back end swappable, I've got a bunch of test code that also depends on that static method (and in arguably a bigger way - more code depends on that internal dependency. If you catch my drift.)
I warn very strongly against using tools and techniques like these to get around inherent internal dependency problems, because - when it comes to refactoring (and what's the point in having fast-running unit tests if we can't refactor?) all that extra test code can actually bake in the design problems.
Multiply this one toy example by 1,000 to get the real scale I sometimes see this one in real code bases. This approach can make rigid and brittle designs even more rigid and more brittle. In the long term, it's better to make the code unit-testable by fixing the dependency problem, even if this means living with slow-running (or even - gasp! - manual) tests for a while.
November 25, 2014
Continuous Inspection II - Planning & Executing CInspIn this second blog post about Continuous Inspection (CInsp, for short), I want to look at how we might manage the CInsp process to get the most value from it.
While some develoment teams are now using CInsp tools to analyse their code to get early warnings about code quality problems when they're easier and cheaper to fix, it's fair to say that this area of the develoment discipline has to date evaded the principles that we apply to other kinds of requirements.
Typically, as a kind of work, CInsp is ad hoc, unplanned, untracked and most teams who do it have only a very vague idea of what kind of cost it has and what kind of benefits they're reaping from it.
CInsp is rarely prioritised, leaving the field wide open to waste a lot of time and effort on activities that add little or no value.
Non-functional requirements obey the same laws as functional ones, which is why we need to attack them using the same principles and techniques.
In this post, I want to examine how we plan and execute CInsp on projects starting from scratch. (In a future post, I'll talk about applying CInsp to existing code bases with a build-up of code quality issues.)
Continuous Inspection Requirements.
There are an infinite number of properties we could look for in our code, but some have value in finding and most don't. Rather than waste our time arbitrarily searching our code for "stuff", it's important we have a clear idea of what it is we're looking for and why.
Extreme Programming, for example, has a perfectly usable mechanism for describing the things we want to inspect for, and the benefits of catching those kinds of code quality problems early.
A Code Quality Story is a non-functional user story that briefly summarises a code quality "bug" we wish to avoid and the pay-off we might expect if we can avoid introducing it into our code.
Note first of all that I've chosen here to use a blue index card. This might be in a system where we write functional user stories on green cards, report bugs on red cards, and record other outcomes - "miscellaneous tasks", like setting up the build and implementing code quality gates - on blue cards.
Why do this? Well, I've found it very useful to know roughly how much of a team's time is split between delivering working features, fixing bugs (ideally, zero time), and "shaving yaks" when the yaks being shaved are sufficiently large and not part of the work of delivering specific features.
The importance of the effort split becomes apparent as time goes by and the software evolves. A healthy project is one where the proportion of effort devoted to delivering working features remains relatively constant. What typically happens on teams who set out at an unsustainable pace is that they begin development with their time devoted mostly to the green cards, and after a few months most of their time is spent tackling red cards and making a lot less progress on new features. This is a good indicator of the rising cost of change we're seeking to avoid, so we can sustain the pace of development and deliver value for longer. This information will help us better judge how well-spent the time devoted to things like CInsp is.
So we have a placeholder for our code quality requirement in the form of a blue index card. What next?
Planning Continuous Inspection
This is where I, and a lot of teams, have gone wrong in the past. What we should never, ever do is allow the customer to choose when and whether we tackle non-functional requirements. And in "customer" I include proxy customers like business analysts and project managers. The overwhelmingly common experience of development teams is that purely technical issues, like code quality, get sidelined by non-technical stakeholders.
We must not give them the chance to drop our Feature Envy story in favour of a story about, say, sorting columns in an HTML table if we strongly believe, as professionals, that avoiding Feature Envy is important. If, as the evidence suggests, care taken over code quality helps to maintain productivity and deliver greater value over time, then we risk presenting customers with a confusing false dichotomy between work that enhances quality and work that directly delivers working features.
The analogy I use is to pretend we're running a restaurant using the planning practices of Extreme Programming.
Every job that needs doing gets written on a card, and placed into a backlog of outstanding work. There will be user stories like "Take table 3's order" and "Serve french fries and beer to table 7" and "Get the bill for table 12". These are stories about work that will make the restaurant money.
There will also be stories like "Wash the dishes in the sink" and "Clean out pizza oven" and "Repaint sign over door". These are about tasks that cost money, but don't directly bring in revenue by themselves.
If we allowed our restaurant's shareholders - who themselves have never worked in a restaurant, but they have a stake in it as a business - to prioritise what stories get done at the expense of others in a world where backlogs always outweigh the available time and resources, then there's a very real danger that the kitchen will rarely get cleaned, the sign above the door will fade until nobody can see it, and we'll run out of clean plates halfway through service.
The temptation for teams who are driven solely by the priorities of non-technical stakeholders is that non-functional issues like code quality will only get tackled when a crisis emerges that blocks progress on functional requirements. i.e., we don't wash up until we run out of plates, or we don't clean the kitchen until the inspector shuts us down, or we don't repaint the sign until the customers have stopped coming in.
One thing we've learned about writing software is that it's cheaper and easier to tackle problems proactively and catch them earlier. Sadly, too many teams are left lurching from one urgent crisis to the next, never getting the chance to get ahead of the issues.
For this reason, I strongly advise against involving non-technical stakeholders in planning CInsp. (As well as other technical work.)
Now put yourself in the diner's shoes: you pick up the menu, and every dish lists all of the tasks restaurant staff have to do in order to deliver it. Let's say we charge £11 for fish and chips, with a clean grill, mopping the floors, cashing up that evening, doing the accounts, getting up early to take delivery of fresh fish, and so on.
1. If we hadn't told them, would the diner even care?
2. If we make it the diner's business, are we inviting them to negotiate the price of the fish and chips down by itemising what goes in to running the restaurant? ("I'll have the fish & chips, but I'm not paying for your trainee chef's college course" etc)
The world is full of work that needs doing, but nobody thinks they should pay for. In order for the world to keep turning, for fish & chips to appear on our dining tables, this work has to get done one way or another, and it has to be paid for.
The way a restaurant squares this circle is to build it into the cost of the meal and to not present diners with a choice. Their choice is simple: don't like the price, don't order the dish.
Likewise in software development, there's a universe of tasks that need doing that do not directly end with a working feature being delivered to the customer's table. We must build this work into the price ("feature X will take 3 days to deliver") and avoid presenting the customer with bewildering choices that, in reality, aren't choices at all.
So planning Continuous Inspection is something that happens within the team among technical stakeholders who understand the issues and will be doing the work. This is good advice for any non-functional requirements, be they about build automation, internal training or hiring developers. This is just "stuff that has to happen" so we can deliver working software reliably, economically and sustainably.
The key thing, to avoid teams disappearing up their own backsides with the technical stuff, is to make sure we're all absolutely clear about why we're doing it. Why are we automating the build? Why are we writing a tool that generates code? Why are we sending half the team to the Software Craftsmanship conference? (Some companies send entire teams.) And the answer should always be something of value to the customer, even if that value might not be realised for months or years.
In practice, we have planning meetings - especially in the early stages of a project - that are for technical stakeholders only. Lock the doors. Close the blinds. Don't tell the boss. (I have literally experienced running around offices looking for rooms where the developers can have these discussions in private, chased by the project manager who insists on sitting in. "Don't mind me. I won't interfere." Two seconds later...)
Such meetings give teams a chance to explicitly discuss code quality and to thrash out what they mean by "good code" and "bad code" and establish a shared set of priorities over code quality. It's far better to have these meetings - and all the inevitable disagreements - at the start, when we can take steps to prevent issues, than to have them later when we can only ask "what went wrong?"
Executing Continuous Inspection
On new software, the effort in Continuous Inspection tends to be front-loaded, and with good reason.
As I've mentioned a few times already, it tends to be far cheaper to tackle code quality "bugs" early - the earlier the better. This means that adding new code quality requirements later in development tends to catch problems when they're much more expensive to fix, so it makes sense to set the quality bar as high as we can at the start.
There's good news and there's bad news. First, the bad news: on a new project, from a standing start, it's going to take considerable effort to get automated code inspections in place. It will vary greatly, depending on the technology stack, availability of tools, experience levels in the team, and so on. But it's not going to take an afternoon. So you may be faced with having to hide a big chunk of effort from non-technical stakeholders if you attempt to start development (from their perspective, when they're actively involved) at the same time as putting CInsp in place. (Same goes for builds, CI, and a raft of other stuff that we need to get up and running early on.)
Another very strong recommendation from me: have at least one iteration before you involve the customer. Get the development engine running smoothly before you wind down the window and shout "Where to, guv'nor?" They may be less than impressed to discover that you just need to build the engine before you can set off. Delighting customers is as much about expectations as it is about actual delivery.
Going back to the restaurant analogy, consider why restaurants distinguish between "service" and "preparation". Service may start at 6pm, but the chefs have probably been there since 9am getting things ready for that. If they didn't, then those first orders might take hours to reach the table. Too many development teams attempt the equivalent of starting service as the ingredients are being delivered to the kitchen. We need to do prep, too, before we can start taking orders.
Now, for the good news: the kinds of code quality requirements we might have on one, say, JEE project are likely to be similar on another JEE project. CInsp practitioners tend to find that they can get a lot of reuse out of code quality gates they've already developed for previous projects. So, over months and years, the overall cost of getting CInsp up and running tends to decrease quite significantly. If your technology stack remains fairly stable over the years, you may well find that getting things up and running can eventually become an almost push-button process. It takes a lot of investment to get there, though.
Code Quality stories work the same way as user stories in their execution. We plan what stories we're going to tackle in the current timebox in the same way. We tackle them in pairs, if possible. We treat them purely as placeholders to have a conversation with the person asking for each story. And, most importantly, we agree...
Continuous Inspection Acceptance Tests
Going back to our Feature Envy code quality story, what does the developer who write that story mean by "Feature Envy"?
Here's the definition from Martin Fowler's Refactoring book:
"A classic [code] smell is a method that seems more interested in a class other than the one it is in. The most common focus of the envy is the data."
It's all a bit handwavy, as is usually the case with software design wisdom. A human being using their intelligence, experience and judgement might be able to read this, look at some code and point to things that seem to them to fit the description.
Programming a computer to do it, on the other hand...
This is where we can inhabit our customer's world for a little while. When we ask our customer to precisely decribe a business rule, we're putting them on the spot every bit as much as a computable definition of Feature Envy might put me and you on the spot. In cold, hard, computable terms: we don't quite know what we mean.
When the business problem we're solving is about, say, mortgages or video rentals or friend requests, we ask the customer for examples that illustrate the rule. Using examples, we can establish a shared vocabulary - a language for expressing the rule - explore the boundaries, and pin down a precise computable understanding of it (if there is one.)
We shouldn't be at all surprised that this technique also works very well for rules about our code. Ask the owner of a code quality story to track down some classic examples of code that breaks the rule, as well as code that doesn't (even if it looks at first glance like it might).
This is where the real skill in CInsp comes into play. To win at Continuous Inspection, development teams need to be skilled as reasoning about code. This is not a bad skill for a developer to have generally. It helps us communicate better, it helps us visualise better, it makes us better at design, at refactoring, at writing tools that work with code. Code is our domain model - the business objects of programming.
Using our code reasoning skills, applied to examples that will form the basis of acceptance tests, we can drive out the design of the simplest tool possible that will sound the alarm when the "bad" examples are considered, while silently allowing the "good" examples to pass through the quality gate.
As with functional user stories, we're not done until we have a working automated quality gate that satisfies our acceptance tests and can be applied to new code straight away.
In the next blog post, we'll be rolling up our sleeves with an example Continuous Inspection quality gate, implementing it using a variety of tools to demonstrate that there's often more than one way to skin the code quality cat.
November 22, 2014
Continuous Inspection I - Why Do We Need It?This is the first of a series of posts about Continuous Inspection. My goals here is to give you something to think about, rather than to present a complete hands-on guide. The range (and maturity) of tools and techniques we can apply to Continuous Inspection (I'll call it CInsp from now on to save a few keystrokes) is such that I could write 1,000 blog posts and still not cover it all. So here I'll just focus on general CInsp principles and illustrate with cherrypicked examples.
In this first post, I want to summarise what I mean by "Continuous Inspection" and argue that there's a real need for it on most software development teams.
Contininuous Inspection is the practice of - and stop me if I'm getting too technical here - continuously inspecting your code to detect non-functional issues in the software.
CInsp is just another kind of Continuous Testing, which is a cornerstone of Continuous Delivery. To have our software always in a shippable state, we must take steps to assure ourselves that the software is always working.
If we follow the thinking behind continuous testing (and re-testing) of our software to check that it still works, the benefit is that we never stray more than a few minutes from having something we could ship if the business wanted us to.
To date, the only practical way we've found to achieve Continuous Testing is to automate those tests as much as possible, so they can be run quickly and economically. If it takes you 2 weeks to re-test your software, then after each change you make to the code, you are at least 2 weeks away from knowing if the software still works. Manual testing makes Continuous Delivery impractical.
In recent years, automated testing - and especially automated unit testing - has grown in popularity, and the effects can be seen in teams delivering more reliably and more sustainably as a result.
But only to a point.
What I've observed across hundreds of teams over the last decade or more is that, even with high levels of automated testing, the pace of delivery still slows to unacceptable levels.
In order to sustain the pace of change, the code itself needs to remain open to change. Being able to quickly regression test our software is a boon in this respect, no doubt. But it doesn't address the whole picture.
There are other things that can hamper change in our code. If the code's complicated, for example, it will be more likely to break when we change it. If there's duplication in our code - if we've been a bit trigger-happy with Copy+Paste - then that can multiply the cost of making a change. If we've not paid attention to the dependencies in our code, small changes can cause big ripples through the code and amplify the cost.
As we make progress in delivering functionality we tend also to make a mess inside the software, and that mess can get in our way and impede future progress. To maintain the pace of innovation over months and years and get the most out of our investment over the lifetime of a software product, we need to keep our code clean.
Experienced developers view design issues that impede progress in their code as bugs, and they can be every bit as serious as bugs in the functionality of the software.
And, just like functional bugs, these code quality bugs (often referred to as "code smells", because they're indicatice of your code "rotting" as it grows) have a tendency to get harder and more expensive to fix the longer we leave them.
Duplication has a tendency to grow, as does complexity. We build more dependencies on top of our dependencies. Switch statements get longer. Long parameter lists get longer. Big classes get bigger. And so on.
Here's what I've discovered form examining hundreds of code bases over the years: code smells that get committed into the code are very likely to remain for the lifetime of the software.
There seems to be a line that once we've crossed it, our mistakes are likely to live forever (and impede us forever). From observation, I've found that this line is moving on.
In the Test-driven Development cycle, for example, I've seen that when developers move on to the next failing test, any code smells they leave behind will likely not get addressed later. In programming, "later" is a distant and alien land where all our little TO-DO's never get done. "Later" might as well be "Narnia".
Even more so, when developers commit their code to a shared repository, at that point code smells "petrify", and remain forever trapped in the amber of all the other code that surrounds them. 90% of code smells introduced in committed code never get fixed.
This is partly because most teams have no processes for identifying and addressing code quality problems. But even the ones who do tend to find that their approach, while better than nothing, is not up to the task of keeping the code as clean as it needs to be to maintain the pace of change the customer needs.
Why? Well, let's look at the kinds of techniques teams these days use:
1. Code Reviews
There's a joke that goes something like this: "Ask a developer what's wrong with a line of code, and she'll give you a list. Ask her what's wrong with 500 lines of code, and she'll tell you it's fine."
Code reviews have a tendency to store up large amounts of code - potentially containing large numbers of issues - for consideration. The problem here is seeing the wood for the trees. A lot of issues get overlooked in the confusion.
But even if code reviews identified all of the code quality issues, the economics of fixing those issues is working against us. Fixing bugs - functional or non-functional - tends to get exponentially more expensive the longer we leave them in the code, and for precisely the same reasons (longer feedback cycles).
In practice, while rigorous code reviews would be a step forward for many teams who don't do them at all, they are still very much shutting the stable door after the horse has bolted.
2. Pair Programming
In theory, pair programming is a continuous code review where the "navigator" is being especially vigilent to code quality issues and points them out as soon as they spot them. In some cases, this is pretty much how it works. But, sad to say, in the majority of pairs, code quality issues are not high on anyone's agenda.
This is for two good reasons: firstly, most developers are not all that aware of code smells. They don't figure high in our list of priorities. Code quality isn't sexy, and doesn't get you hired at IronicBeards.com.
Secondly, with the best will in the world, people have limitations. When Codemanship does pairing to assess a developer's skill level in certain practices, the level of focus required on what the other person's doing is really quite intense. You don't take your eye off the screen in case you miss something. But there are dozens of code smells we need to be vigilant for, and even with all my experience and know-how, I can't catch them all. My mind will have to skip between lots of competing concerns, and when my remaining brain cells are tied up trying to remember how to do something with Swing, I'm likely to take my eye off the code quality ball. It's also very difficult to maintain that level of focus hour after hour, day-in and day-out. It hurts my brain.
Pair programming, as an approach to guarding against code smells, is good when it's done well. But it's not that good that we can be assured code written in this way will be maintainable enough.
3. Design Authorities
By far the least effective route to ensuring code quality is to make it someone else's job.
Hiring architects or "technical design authorities" suffers from all the shortcomings of code reviews and pair programming, and then adds a big bunch of new shortcomings.
Putting aside the fact that almost every architect or TDA I've ever met has been mostly focused on "the big picture", and that I've seen 1,000-line switch statements waved through the quality gate by people obsessing over whether classes implement certain interfaces they've prescribed, turning design authorities into design quality testers never seems to end well. Who wants to spend their day scouring other people's code for examples of Feature Envy?
I'll say no more, except to summarise by observing that the code I've seen produced by teams with dedicated design authorities counts amongs the worst for code quality.
4. Coding Standards
In theory, a team's coding standards are a codification of what we all agree we mean by "good code".
Typically, these are written down in documents that nobody ever reads, and suffer from the same practical drawbacks as architecture documents and company mission statements. They're aspirational affirmations at best. But, in practice, everybody just ignores them.
Even on those more disciplined teams that try to adhere to coding standards, they still have major drawbacks, all relating back to things we've already discussed.
Firstly, coding standards are a list of "stuff" we need to be thinking about along with all the other "stuff" we have to think about. So they tend to take a lower priority and often get overlooked.
Secondly, as someone who's studied a lot of coding standards documents (and what joy they bring!), they have a tendency to be both arbitrary and by no means universally agreed upon. Often they've been written by some kind of design or development authority, usually with little or no input from the team they're being imposed on. It's rare for issues that affect maintainability to be addressed in a coding standards document. Programmers are a funny bunch: we care deeply about some weird stuff while Elephants In The Room creep in without being questioned and sit on us. Naming conventions, therefore, have little relation to how easy the code will be to read and understand. And it's rare to see duplication, dependencies, complexity and so on even being hinted at. As long as all your instances have names beginning with obj and all your private member variables beging with "m_", the gods of code goodness will be appeased.
And then there's the question of how and when we enforce coding standards. And we're back to the hard physics of software development - time, money and cost. Knowing what we should be looking for is only the tip of the code quality iceberg.
What's needed is the ability to do code reviews so freqently, and do them in a way that's so effective, that we never stray more than a few minutes from clean code. For this, we need code reviewers who miss very little, who are constantly looking at the code, and who never get tired or distracted.
For that, thankfully, we have computers.
Program code is like any other domain model; we can write programs to reason about the design of other programs, expressed in terms of the structure of code itself.
Code quality rules are just like any other computable business rules. If the rule is that a block of code in one class should not make copious references to features in another class ("Feature Envy"), it's possible to write an automated test that reads code and looks at those references to determine if that block of code is in the right place.
Let's illustrate with a technology example. Imagine we're working in Java in, say, Eclipse. We could write code for a plug-in that, whenever we make a change to the code document we're working on, reads the code's Abstract Syntax Tree (basically, a code DOM) and does a calculation for the ratio of internal and external dependencies in that Java method we just changed. If the ratio is too low, it could flag it up as a warning while we're writing the code.
The computational power of computers is such today that this sort of continuous background code reviewing is practically possible, and there have already been some early attempts to create just such plug-ins.
In the article I wrote a few years ago for Visual Studio Journal, Ever-decreasing Cycles, I speculate about the impact such short code quality feedback loops might have on the economics of development.
It's my belief that, just as continuous automated unit testing has had a profound effect on the "bottom line" of software development for many teams and businesses, so too would Continuous Inspection.
In the next blog post, I'll talk about the CInsp process and look at practical ways of managing CInsp requirements, test automation and how we action the code quality problems it can throw up.
November 19, 2014
In 2015, I Are Be Mostly Talking About... Continuous InspectionJust a quick FYI, for event organisers: after focusing this year on software apprenticeships, in 2015 I'll be focusing on Continuous Inspection.
A critically overlooked aspect of Continuous Delivery is the need to maintain the internal quality of our software to enable us to sustain the pace of innovation. Experience teaches us that Continuous Delivery is not sustainable without Clean Code.
Traditional and Agile approaches to maintaining code quality, like code reviews and Pair Programming, have shown themselves to fall short of the level of rigour teams need to apply. While we place great emphasis on automated testing to ensure functional quality, we fall back on ad hoc and highly subjective approaches for non-functional quality, with predictable results.
Just as with functional bugs, code quality "bugs" are best caught early, and for this we find we need some kind of Continuous Testing approach to raise the alarm as soon after code smells are introduced as possible.
Continuous Inspection is the missing discipline in Continuous Delivery. It is essentially continuous non-functional testing of our code to ensure that we will be able to change it later.
In my conference tutorials, participants will learn how to implement Continuous Inspection using readily available off-the-shelf tools like Checkstyle, Simian, Emma, Java/NDepend and Sonar, as well as rigging up our own bespoke code quality tests using more advanced techniques with reflection and parser generators like ANTLR.
They will also learn about key Continuous Inspection practices that can be used to more effectively manage the process and deliver more valuable results, like Non-functional Stories, Clean Code Check-ins, Build Inspections and Rising Tides (a practice that can be applied to incrementally improving the maintainability of legacy code.)
If you think your audience might find this interesting, drop me a line. I think this is an important and undervalued practice, and want to reach as many developers as possible in 2015.
September 17, 2014
The 4 C's of Continuous DeliveryContinuous 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.