Against technical debt

Technical debt is a very useful concept for explaining the consequences of dirty code to management. However, there is a problem that I have with the debt metaphor. The phrase technical debt implies that it's possible to avoid the debt. If I don't write shoddy code today, I wont have to pay for it tomorrow.

This obscures the fact that though dirty code costs more than clean code, every line of code impedes your agility. Sometimes product owners ask for features that compromise a system's architecture or domain model. When I've tried to describe the technical debt that will be incurred by an awkward feature, I've (quite reasonably) been asked how much effort it would take to "do it properly". I'm stumped, because no matter how thoroughly I implement the feature, it will still cause problems down the line.

Sometimes I fall back on depreciation, which I can use to explain anything that reduces the system's ability to meet future needs. Unlike debt, depreciation isn't automatically reversible. I've also considered that fear-driven estimation might produce estimates that more accurately reflect the long-term cost of a story.

I don't want to see the technical debt analogy deprecated, but I do want to encourage people to think critically about how they use it, because all metaphors have their limits.

As a stakeholder

A common template for user stories is "As a user, I want". This forces stakeholders to make the business value of the story explicit and encourages consistency.

However, there are some stories that this doesn't make sense for, including ones that are to the business' advantage and the users' detriment. Stating all stories in terms of users' wants can result in bizarre stories that conceal who has a stake in the their completion:

As a user, I want my DVDs to not work in other regions, so that I have to buy them again if I move countries.

As much as we focus on users, we don't build commercial software for them. It just so happens that satisfying users is a necessary part of achieving our other aims - like making money.

Users are stakeholders, but they aren't the only stakeholders. If we revise the template to "As a stakeholder, I want", then we're able to state anti-user stories much more naturally:

As the sales department, I want to prevent DVDs bought in one region from being played in another, so that I can release and price DVDs in different markets independently.

Thanks to @MrsSarahJones for pointing this out to me.

Tests are facts. Code is theory.

Programmers have turned to science to help resolve the software crisis. But they're doing it wrong.

Science envy

Programmers have science envy. We feel that, unlike much of our code, science works. Scientists have spent hundreds of years honing a methodology that helps them assimilate new knowledge and correct error, while we have spent decades frantically accumulating complexity that we can't handle. Strangely, scientific theories become more accurate over time, whereas software systems often decay.

The software industry has tried to learn from science and engineering's success. We call our programming degrees "Computer Science" and "Software Engineering", though they are neither. "Computer Science" students do almost no experiments. The "Software Engineering" concept of exhaustive up-front design has become so discredited that even those who can't imagine any other way feel obliged to pretend that they "don't do Waterfall".

Of course, science and engineering are just analogies when applied to programming. They are meant to be useful ways of imagining our profession, not to be literally true. But in their naive form, I don't think analogies between programming and science are very useful. If we want to benefit from scientific rigour, we need to be more rigorous in how we appropriate scientific concepts.

Scientific testing

Some software testers have used the scientific method as a way of framing their testing activities. For example, David Saff, Marat Boshernitsan and Michael D. Ernst explicitly cite Karl Popper and the scientific method in their paper on test theories. Test theories are invariant properties possessed by a piece of code which Saff et al attempt to falsify over a wide range of data points with an extension to the JUnit testing framework.

I find the reciprocal of this approach useful when debugging. I start with a defect, form a theory as to its cause, then design a test to try and falsify that theory. If I suspect that the issue is caused by rogue javascript, I'll disable javascript and attempt to reproduce the issue. If I can, I've disproved my theory and I need to find another explanation. This helps me to eliminate false causes and gradually home in on the bug.

The problem with analogies that treat tests as theories and code as a phenomena is that they tell us nothing about how to write code. The software under test is like gravity, a chemical reaction or the weather. It may or may not have an underlying structure and beauty, but any insights we gain during testing are inevitably after-the-fact.

Worse, they are static models. When software changes over time, the knowledge gathered through "scientific testing" may no longer apply. The scope of scientific testing is confined to a specific version of the software. For example, a tested and verified "theory" about the memory profile of an application may become invalid when a programmer makes a small change to a caching policy.

Tests are facts. Code is theory.

Science's strength is its ability to assimilate new discoveries. If we want to share in its success, a scientific model of software development needs to preserve science's adaptability.

We can go some way to achieving this by reversing the roles of testing and coding in the scientific testing model. Tests are facts. Code's role is as a theory that explains those facts as gracefully and simply as possible.

New requirements mean new tests. New tests are newly discovered facts that must be incorporated into the code's model of reality. Software can be seen as a specialised theory that attempts to embody what the stakeholders want the application to do.

How does that help us?

Once we accept that code as a theory, we are then in a position to justify employing the most powerful weapon in science's armoury - Occam's razor. Our role is to write the simplest possible code that is consistent with the facts/tests/requirements. Whenever we have the opportunity to eliminate concepts from our code, we should.

Simple code isn't just cheaper. It's more valuable too, because it's easier to change and extend. We can justify this with reference to scientists' experience that the simplest theory is the most likely to survive subsequent discoveries.

As new requirements arrive and our understanding of the domain deepens, we have the opportunity to refactor. Refactoring isn't rework or throwing away effort. Refactoring is enhancing code's value by incorporating new knowledge on what we want our software to do. This could be by adding functionality, or in reducing complexity. Either makes the software as a whole more valuable.

Science celebrates refactoring. Each new piece of evidence clarifies scientists' understanding of phenomena and helps yield more useful theories. Often these refinements are small, but occasionally Einstein will have an insight that supercedes Newton's laws of motion. Domain driven design founder Eric Evans describes such pivotal moments on software projects as "breakthroughs".

Non-developers often assume an application is invariably more valuable with a feature than without it. Yet the example of special relativity allows us to explain otherwise. Newton's laws of motion are perfectly adequate for ordinary use. Unless we are interested in bodies moving close to the speed of light, it's not worth bothering with the additional complexity Einstein's theories bring.

If stakeholders are willing to accept that the application targets the common case and excludes troublesome edge cases, they will enjoy software that is simpler and therefore cheaper and more valuable. Sometimes, there is value in absent features. Always, there is value in simpler code.

Crave simplicity. Celebrate deletion. If science responded to new information by adding special cases then science would be in as big a mess as the software industry. As you incorporate new requirements, attempt to refine your code so that it remains flexible enough to accomodate tomorrow's requirements. Otherwise, your code will become less and less fit for its purpose, which is to provide business value.

Conclusion

When John Maynard Keynes was attacked for repeatedly revising his economic theories, he said, "When the facts change, I change my mind – what do you do, sir?" Take the same attitude with your code, but treat requirements and tests as your facts. And remember, your code is just your best approximation of what your stakeholders want it to be.

My agile canon

As much as I enjoy reading blog posts, they cannot match the sustained argument from a well-written book. Fortunately, the agile movement has many articulate advocates who have been busy committing their thoughts to paper (and eReader) over the past few years.

Here are some agile books that I recommend:

• Extreme Programming Explained by Kent Beck is the original XP manifesto.
  
• The Art of Agile Development by James Shore is a comprehensive reference for agile/XP.
  
• Clean Code by Uncle Bob is a detailed manual for producing high quality code, right down to variable naming.
  
• The Art of Unit Testing (with examples in .NET) by Roy Osherove is the only good book I've found specifically on unit testing.
• Working Effectively with Legacy Code by Michael Feathers is an interesting guide to bringing legacy code under test and under control.
• I'm currently reading Agile Estimating and Planning by Mike Cohn, which covers story points, sprint length, agile planning and a lot of other topics that are important for management to understand as well as developers.
• Two very reputable books on use cases and user stories are Writing Effective Use Cases by Alistair Cockburn and User Stories Applied by Mike Cohn, but I've read neither (yet).

The exciting thing about reading these books is that they are part of an ongoing conversation. We haven't worked out how to build software well yet, but I think that these books bring us a little closer.

Tolstoy on iterative development

But their mother country was too far off, and a man who has six or seven hundred miles to walk before reaching his destination must be able to put his final goal out of his mind and say to himself that he will 'do thirty miles today and then spend the night somewhere'; and during this first stage of the journey that resting-place for the night eclipses the image of his ultimate goal and absorbs all his hopes and desires.

- Leo Tolstoy, War and Peace

Fear-based estimation

I prefer agile development because early feedback mechanisms like TDD, pair-programming and frequent releases reduce my fear of the unknown. I don't like feeling afraid, but when I am I try to take notice, because it's usually an indication that something is not right.

A great agile tradition is acknowledging the human element in software development. In that spirit, I'd like to suggest that developers' fear can be harnessed to improve estimation.

Humans are notoriously bad at temporal reasoning and programmers are even-more-notoriously bad at guessing how long a given piece of work will take. Agile teams work around this by estimating effort in 'story points' that measure the comparative size of tasks. We might not be sure how long designing the new schema will take, but we assign it 2 story points because we know it will take twice as long as another task we gave 1 point.

The rate at which a team completes points is known as its 'velocity' and can be determined by examining the team's progress over time. It's a robust, self-correcting system, but I've observed two drawbacks on projects I've worked:

• The confidence of estimates isn't captured.
• The technical debt incurred by a particular piece of work is ignored altogether.

A story that has a chance of going horribly wrong and that will in any case cripple the system's architecture could receive a low story point score because it (probably) won't take long to implement. Confronted with such a story, developers are terrified - and helpless.

I propose that we estimate in terms of 'fear points' to give product owners a disincentive to prioritise dangerous stories. The question we ask in estimation meetings shouldn't be "how big is this task" but "how afraid does this task make you?".

So long as the developers have an appropriate level of professional cowardice, a story's fear points will reflect the danger the story represents to the project's current and future timelines. And I think that 'danger' is as useful to communicate to management as 'size'.

If product owners want to negotiate a reduction in a story's fear points then they need to reduce uncertainty, remove risky features and compromise on ideas that would incur a lot of technical debt. They could also prioritise other stories that improve the team's confidence in the development process, like improved Continuous Integration.

Whenever I have seen developers afraid of a feature, it turns out badly. It ends up buggy, expensive to change and probably doesn't serve its intended purpose. By gauging developers' fear, agile organisations have an opportunity to avoid trouble before it happens.

If your developers are afraid of a feature - be very afraid.

Safe habits

When I was growing up, I found some of my parents' habits irritatingly conservative. Examples included:

• Always lock doors from the outside using the key
• Always turn on the cold tap before the hot tap

The idea was that following these habits guaranteed you could never burn yourself or lock yourself out. But I found these precautions frustrating because I knew that it was unlikely that I'd forget my keys or put my hand in the scalding water.

However, I was mistaking the most likely outcome for the entire distribution. In Life's Grandeur, Stephen Jay Gould calls this "reification" - fixating on the average case and ignoring variation and atypical outcomes.

On any given occasion, my cavalier attitude would probably suffice. But sooner or later, if I locked doors by setting the snib and pulling them shut, my keys would not be in my pocket.

My parents understood my fallibility better than I did. And they were in a better position to appreciate the myriad of door-locking scenarios that would confront me over my lifetime.

That's why I like to think that if my mother were a programmer (and she'd make a good one) she would advocate TDD, which goes as follows:

1. Write a failing test
2. Write the minimum of code to pass the test
3. Refactor

If you make TDD a permanent habit then you are inevitably at most 5 or 10 minutes away from working, tested code. And you know that every bit of code you write has a corresponding test.

Be mindful of your development habits, and design them for the bad days, not the average days.