We measure a lot of things in software engineering these days. Test coverage, time-to-deploy, bugs per line - they’re all good things to keep an eye on. They’re all proxies for risk of failure, either from moving too slowly, or from bugs and downtime destroying the business.

Something that’s not often explicitly controlled, however, is Novelty. One of the dirty secrets of programming is that almost every production codebase contains some dependency that the developers have never used before. Perhaps they’ve written a trivial project to play with it, but mostly they’re relying on community feedback, or if you want to be dismissive, fashion. Civil engineers have solid rules for the way a bridge ought to be constructed: we switch web application frameworks on an annual basis.

Why are we indulging in so much novelty anyway?

There are reasons for this churn. The widespread availability of libraries and frameworks is one of the reasons creating a startup is cheaper and easier than ever before. Your app sits on top of a vast pyramid of code you’ve never seen, and if you want to keep up, you can’t just ignore better options when they come along: your company will suffer, and so will your career (unless you enjoying maintaining pre-millennial line-of-business COBOL apps).

Avoiding the Scylla of endless novelty and the Charybdis of stasis

My technique is to use something called a “novelty budget”. It’s a rough, informal thing: in essence, you decide how much tolerance you have for library code you’ve never deployed on a new project before, and apportion it out in the ways that you think will give you the biggest bang-for-buck. This means that you accept that you’ll pay a cost in missing documentation, unpolished tooling and ungooglable errors for that part of the project, and you make up for it by using the most solid, boring, dependable pieces you can for the rest of it. The name of the game is reducing risk, not optimising for the best possible case you can imagine.

The essence of this approach is that you are trying to balance average and worst case time-to-completion. Any novel component is going to increase your worst case time: the hope is that it improves the average, both now and over the evolution of your app in the coming months and years.

The default tech stack

Let’s assume you’ve decided where you’re spending the biggest chunk of your budget and put that piece aside: I can’t comment on why that’s a good expenditure for you. Whatever it is, every other piece of the project has to tighten its belt to compensate. (Obviously, if one of these is your primary expenditure, feel free to ignore me - this is just a set of defaults.)

Suggestions

choose something boring like Ubuntu or Debian, rather than a fancy hardened BSD variant or exotic unikernel.

Don’t adopt a graph database or fancy distributed database until you’ve satisfied yourself that PostgreSQL or SQLite are really not sufficient. (My defunct startup MeanPath scraped 160 million websites a day using SQLite. The limits are probably higher than you think.)

Try a serverside-rendered site before adopting a SPA framework - you might find that a limited amount of scripting is all you need.

Devops automation is a real timesaver, but don’t feel that you have to go full Docker/Kubernetes right off the bat. While you definitely need a process to build a deployable artifact, it’s entirely possible that artifact is something more akin to a git repo that gets pushed to heroku endpoint than full-on enterprise-grade automation.

Process

When I can, I run my development processes on GitHub via pull reviews, issues, and the rest of that stack: possibly there is a better solution for parts of it, but the more you can stay in the boring zone, the lower your chances of a catastrophic failure. Writing your own process management software is unlikely to be a good idea unless that’s your company’s reason to exist.

Startups are different though, right?

There’s a seductive argument that since the chances of any given startup’s success are small anyway, you might as well crank all the knobs to 11 just to increase the variance. I think this is a bad idea: contrary to the founder legend, most startups are not solving difficult technical problems. The job of the founding engineers is to get something out there that works well-enough, and to do so quickly enough to test the hypothesis on which the startup was founded. Once you get traction, you might hit scaling problems: at that point, you’ll have either revenue or funding to fix them.

Haskell case study

If you don’t care about Haskell, skip this section.

Concretely, my biggest novelty budget expenditure for a project is frequently Haskell. Haskell has some obvious benefits for me: I know- it well, and I can bake out a lot of potential flaws just by leaning hard on the type system. It has a cost, though: IDE tooling is not as seamless as something like Java or Ruby, sometimes libraries are missing, sometimes you hit baffling type errors (especially with more advanced libraries).

The principle of a novelty budget applies within Haskell too, both at a language level and in your choice of libraries.

Language

Haskell gives you a lot of rope to hang yourself, if you’re so inclined: there’s a whole zoo of extensions, and the emphasis in community blog posts on sexy types means that you can easily get the impression that if your app doesn’t have a type-indexed generic free monad at its core, you might as well be writing Perl. It just isn’t true. Sum types, parametric polymorphism and typeclasses already put the language way ahead of almost everything out there, and you can build very solid apps without using any extensions. Treat it as an a la carte menu, not a buffet. (I don’t include things like OverloadedStrings or LambdaCase here - they are minor syntactic conveniences that don’t add significantly to cognitive load.)

Libraries

I tend to use the Yesod/Persistent+Esqueleto stack. It has problems but almost any reasonable thing someone might do with a website has been attempted in Yesod, and there’s usually a solution. There are fascinating database experiments out there with Ferry and Opaleye, but they don’t have the volume of use. Similarly, Servant is a brilliant piece of work, but in my test projects, I inevitably hit some small but critical thing that will not get fixed for days, or weeks, or months, and I can’t afford that time on a commercial project. (Yes, one option is to fix it yourself or sponsor the author to do it: for whatever reason, I’ve found that it’s usually far harder to get payments authorised than it is to spend the equivalent amount of dev time. That’s a company management problem I have no idea how to solve.)

Another approach I’ve seen people have success with is to use WAI directly or very minimal frameworks like Scotty. While they don’t have all the bells and whistles of Yesod, you are very unlikely to end up hitting a hard stop.

Conclusion

I hope I’ve convinced you that this is at least a concept worth thinking about. I should mention that I’ve mostly worked at smallish startups, and it’s entirely possible it doesn’t generalise to AmaGooBookSoft - would love to hear from anyone in that environment.

Feel free to tell me I’m completely off base.

PS. I’m not claiming to have invented this concept - it’s been a term of art in my development conversations for years. I was frankly surprised to find nothing about it written down and would welcome hearing about anything I’ve missed.

References and apologies

Similar

A contrary view from the enterprise

Thanks to Matt Olson, David Maciver and Alec Heller for thoughtful comments on a draft.

Thanks also to my other reviewers who I currently can’t find because Twitter search is terrible, and apologies that this took almost a year to actually find time to polish and publish.