Monthly Archives: November 2022

Technical debt – the truth

.NET, Architecture, DevOps

Rationale

Within software engineering we often talk about Technical Debt. It was defined by elder programmer and agilist Ward Cunningham and likens trade offs made when designing and developing software to credit you can take on, but that has to be repaid eventually. The comparison further correctly implies that you have to service your credit every time you make new changes to the code, and that the interest compounds over time. Some companies literally go bankrupt over unmanageable technical debt – because the cost of development goes up as speed of delivery plummets, and whatever use the software was intended to have is eventually completely covered by a competitor yet unburden by technical debt.

But what do you mean technical debt?

The decision to take a shortcut can be a couple of things. Usually amount of features, or time spent per feature. It could mean postponing required features to meet a deadline/milestone, despite it taking longer to circle back and do the work later in the process. If there is no cost to this scheduling change, it’s just good planning. For it to be defined as technical debt there has to have been a cost associated with the rescheduling.

It is also possible to sacrifice quality to meet a deadline. “Let’s not apply test driven development because it takes longer, we can write tests after the feature code instead”. That would mean that instead of iteratively writing a failing tests first followed by the feature code that makes that test pass, we will get into a state of flow and churn out code as we solve the problem in varying levels of abstraction and retrofit tests as we deem necessary. Feels fast, but the tests get big, incomplete, unwieldy and brittle compared to the plentiful small all-encompassing, and specific tests TDD bring. A debt you are taking on to be paid later.

A third kind of technical debt – which I suspect is the most common – is also the one that fits the comparison to financial debt the least. A common way to cut corners is to not continuously maintain your code as it evolves over time. It’s more akin to the cost of not looking after your house as it is attacked by weather and nature, more dereliction than anything else really.

Let’s say your business had a physical product it would sell back when a certain piece of software was written. Now the product sold is essentially a digital license of some kind, but in the source code you still have inventory, shipping et cetera that has been modified to handle selling a digital product in a way that kind of works, but every time you introduce a new type of digital product you have to write further hacks to make it appear like a physical product as far as the system knows.

The correct way to deal with this would have been to make a more fundamental change the first time digital products were introduced. Maybe copy the physical process at first and cut things out that don’t make sense whilst you determine how digital products work, gradually refactoring the code as you learn.

Interest

What does compound interest mean in the context of technical debt? Let’s say you have created a piece of software, your initial tech debt in this story is you are thin on unit tests but have tried to compensate by making more elaborate integration tests. So let’s say the time comes to add an integration, let’s say a json payload needs to be posted to a third party service over HTTP with a bespoke authentication behaviour.

If you had applied TDD, you would most likely have a fairly solid abstraction over the rest payload, so that an integration test could be simple and small.

But in our hypothetical you have less than ideal test coverage, so you need to write a fairly elaborate integration test that needs to verify parts of the surrounding feature along with the integration itself to truly know the integration works.

Like with some credit cards, you have two options on your hypothetical tech debt statement, either build the elaborate new integration test at a significant cost – a day? Three? Or you avert your eyes and choose the second – smaller- amount and increase your tech debt principal by not writing an automated test at all and vow to test this area of code by hand every time you make a change. The technical debt equivalent of a payday loan.

Critique

So what’s wrong with this perfect description of engineering trade offs? We addressed above how a common type of debt doesn’t fit the debt model very neatly, which is one issue, but I think the bigger problem is – to the business we just sound like cowboy builders.

Would you accept that a builder under-specified a steel beam for an extension you are having built? “It’s cheaper and although it is not up to code, it’ll still take the weight of the side of the house and your kids and a few of their friends. Don’t worry about it.“ No, right? Or an electrician getting creative with the earthing of the power shower as it’s Friday afternoon, and he had promised to be done by now. Heck no, yes?

The difference of course is that within programming there is no equivalent of a GasSafe registry, no NICEIC et cetera. There are no safety regulations for how you write code, yet.

This means some people will offer harmful ways of cutting corners to people that don’t have the context to know the true cost of the technical debt involved.

We will complain that product owners are unwilling to spend budget on necessary technical work, so as to blame product rather than take some responsibility. The business expects us to flag up if there are problems. Refactoring as we go, upgrading third party dependencies as we go should not be something the business has to care about. Just add it to the tickets, cost of doing business.

Sure there are big singular incidents such as a form of authentication being decommissioned or a framework being sunset that will require big coordinated change involving product, but usually those changes aren’t that hard to sell to the business. It is unpleasant but the business can understand this type of work being necessary.

The stuff that is hard to sell is bunched up refactorings you should have done along the way over time, but you didn’t- and now you want to do them because it’s starting to hurt. Tech debt amortisation is very hard to sell, because things are not totally broken now, why do we have to eat the cost of this massive ticket when everything works and is making money? Are you sure you aren’t just trying to gold plate something just out of vanity? The budget is finite and product has other things on their mind to deal with. Leave it for now, we’ll come back to it (when it’s already fallen over).

The business expects you to write code that is reliable, performant and maintainable. Even if you warn them you are offering to cut corners at the expense of future speed of execution, a non-developer may have no idea of the scale of the implications of what you are offering .

If they spent a big chunk out of their budget one year – the equivalent of a new house in a good neighbourhood – so that a bunch of people could build a piece of software with the hope that this brand new widget in a website or new line-of-business app will bring increased profits over the coming years, they don’t want to hear roughly the same group of people refer to it as “legacy code” already at the end of the following financial year.

Alternative

Think of your practices as regulations that you simply cannot violate. Stop offering solutions that involve sacrificing quality! Please even.

We are told that making an elaborate big-design-upfront is waterfall and bad – but how about some-design-upfront? Just enough thinking ahead to decide where to extend existing functionality and where to instead put a fork in the road and begin a more separate flow in the code, that you then develop iteratively.

If you have to make the bottom line more appealing to the stakeholders for them to dare invest in making new product through you and not through dubious shadow-IT, try and figure out a way to start smaller and deliver value sooner rather than tricking yourself into accepting work that you cannot possibly deliver safely and responsibly.

Size matters

Architecture

When I started out, I had no idea about lean, agile or TDD. I did read a lot about Object Oriented Programming. I even thought I was practicing it. My first decade of programming was not what would pass for professional grade by modern standards, but even my poor standard back then was – and still would be – not the worst. Sadly.

Eventually I read some pamphlets about TDD, tried some katas, read books about lean software development and the book the Phoenix Project and was subjected to Fred George‘s Object Bootcamp with my co-workers a couple of employers ago.

Then I understood. All of it. Object oriented programming, TDD, lean. All of it. Because of how steeped in my old bad habits I am it still takes conscious effort to practice all these things, and I fail to enforce it sometimes, but at least at this point I know what good looks like. If I had stronger discipline I could refactor towards it as often as I like.

Like, if you make small enough focused classes, object orientation makes sense and you find yourself binning and replacing classes that are no longer fit-for-purpose rather than rewriting them. Open-Closed principle. There are all of a sudden more value objects and composable domain classes that cleanly describe domain behaviour and you come away from cascading changes that you thought were inherent in OO. But is it easy? No. It requires constant effort to maintain.

The most important thing is size. Make the smallest possible change to make the test go green. People – me, you – we? – overcomplicate things and fail to comprehend just how small small should be and in the refactor step, we rarely refactor enough and leave classes too big. Now I find this really hard, the commit-by-commit design step where you are supposed to refactor code into proper shape, reduce classes down to their most composable form, their bare essence. On the other hand, “It’s your only job, Rachel!” like Jimmy Carr says on Eight out of Ten Cats Does Countdown when she can’t solve a numbers game right away. We are paid not only to sit in front of our computers and type but also to think. If we do a bit more of the thinking and a little bit less of the typing, nobody is going to get upset.

Allen Holub talks about user stories on Twitter, reminding us that a story isn’t a neologism for “requirement” and that a user story literally means that, a software user telling us a story about a domain problem they are having. Not “as a user I want to authenticate so that I can have access to the system” but like actual meat on the bone. “As an underwriter I want to bind a quote as I have agreed terms with the broker”. Requirements for authentication will come as part of some story, but start stupid simple with a .htaccess file or similar. For some subsystems that requirement will never come, or can be trivially covered with infrastructure, and you just saved yourself a bunch of maintenance. Code is not an asset, it’s a liability.

Allen Holub also uses TDD when designing systems, as in uses Java and jUnit to TDD integrations between microservices like a less-trendy jupyter notebook proving his overall system design, even ending up with some code that could describe integration points. Start smaller. No, even smaller than that.

Then we have the No Estimates crowd. Seems insane to people used to the widespread non-story user stories with large blocks of detailed requirements that necessitates complex up-front engineering. How could teams take on these big stories without estimating how much they will take to build – what if we spend three months and we don’t even achieve what we want?!

That’s right. But – do you remember Agile? Yea, see the point was to build the smallest possible implementation that can prove viability. If your stories are sized consistently and correctly – i.e. smaller than you think possible – the consistent implementation work will give you the foresight you need to coordinate and plan as necessary.

We can then add features incrementally as users examine the existing product and realise the potential and get ideas based on their experience as domain experts for what they could do to meet their customers’ needs, or what experiments could be run to determine customer desires. In small increments still.

Even if you insist on keeping creating your estimates, you will find that making stories smaller will improve delivery quality and adherence to timescales. As soon as stories get too big, they are harder to reason about and you may even forget acceptance criteria you thought you had clear in your head. You will never regret making stories smaller.