I respect all three authors deeply but this is my first time reading Tellman's take. His idea that "simplicity is not intrinsic ... Simplicity is a fitness between software and our expectations" has a very nice ring to it.

Contrast that with Hickey's notion of simplicity as an objective count of interleaved concerns or "folds". The quibble with this is, how do you delineate the concerns? Depending on your style and knowledge of the system, you'll get a different number. So it's hard to call it objective.

Tellman's definition is nice because it acknowledges the subjectivity, and puts it front and center. IOW the "style and knowledge of the system" form a mental model of the software system. What's important is not the cardinality, OR the interleaving. It's the ability for that model to make good predictions about the software's behavior. Accurate model held in the minds of humans that operate it == simple software.

I read a research article long ago that said that most of the complexity measures investigated at the time were highly correlated with loc.

For example, it's nice to measure McCabe cyclomatic complexity, and it generally rings true, but if you can't be bothered - just look at LOC and you'll get pretty close

I like Ousterhout's the best because I think it's the most concrete out of the three. The other two are more subjective imo, of course still very useful and important.

Although I think "dependency" is an overloaded term. There are dependencies as in libraries or shared code, and then there are "dependencies" which are like assumptions about other parts of the system. Hickey also mentions this when he talks about interfaces. If you break a class into two classes, but they both make assumptions about the other, in effect they are still the same class.

Perhaps another way to think about complexity is the number of assumptions baked into a system?

When you see the words "complex", "simple", and "easy", keep this in mind: these are words that exist to help humans communicate their difficulty in interacting with the world.

This whole post is a discussion of philosophy. There are three different ways you can think about software:

  - philosophy
  - science
  - function

The philosophy of software involves people trying to understand what software is. Humans have a desire for knowledge and understanding, and like to explain things they come into contact with and think about. A lot of the time there's no "objective" philosophical explanation, try as we might to find one. Despite that, many explanations in this realm end up being useful.

The science of software is computer science, which is more about math than anything else. This is the only place you're gonna find objectivity or consensus, because you can't really argue with a number. Discussions around CS are less poetic and more boring, and require a lot more knowledge, so you won't typically find these on HN.

The function of software is the day-to-day practice of writing and using software. This is the grunt work, the meat and potatoes. No need for philosophy or science. Just put the legos together and kick the tires. This is 99% of what 99% of software developers deal with. Most people don't think about the science or philosophy of the coffee they're ordering or the bike they're riding; they're just ordering it, riding it.

Enjoyed this, but looking at these from manager decision scenario is of course going to bias towards the more subjective.

For a different scenario, imagine you operate a large distributed system and have been paged at 3 am because there is a problem. Besides taking steps to contain the fallout, you want to quickly locate the error and take steps to prevent this stuff from ever happening again.

Unlike a hiring decision, you want to propose measures such that a large number of people get to agree that the state of affairs will improve. It is a truism that we don't want software to fail, so considering expectations may not necessarily help here, and rationality would suggest to strive to achieve an "objective" improvement - and one that does not make the system harder to understand or invite new, different failures.

So all in all, I enjoyed this advocacy of Tellman's perspective, but it does not always seem appropriate to apply it and more meta than the others.

> I reached out to Tellman before writing this post for some clarifications.

Ironic!

I like to think of software complexity in terms of the rocket equation [1].

More generally, I imagine complexity as a dynamic multi-dimensional phenomenon, operating across time and space. The more complex a phenomenon is, the more it resists explanation. Static systems made of many many parts (fractals, amorphous materials etc...) can be simpler than dynamic systems made of very few parts (three body problem).

Behaviour makes all the difference [2].

[1] https://www.evalapply.org/posts/software-debt/index.html#sof....

Quoting oneself...

> Complexity is to software what mass is to a rocket; the hard limiting factor of growth in any dimension you choose to measure (shipping velocity, headcount, revenue, cash flow, account expansion; anything). This is the sort of thing that demands tree-recursive, networked thinking we are not good at doing explicitly and deliberately. Something that our education actively disables by drilling us to think in simplistic linear terms in which correlation amounts to causation.

[2] In this sense, I'd argue that the biggest baddest LLM contains far less complexity than is contained in a living planarium.

Though an LLM is a black box demonstrating emergent-looking behaviour, it is a pure function, once calculated. The apparent emergence is a function of us not knowing what bit-twiddles of the input space will lead to what bit responses out the other end. The probability distribution being fixed, LLM input-output relationships are countable (a finite game), though the number is too big for us to count.

(P.S. I'm certain of all of this because of my quadruple Phds in Biology, AI, Physics, and Maths. Totally not an Internet Rando. I swear.)

Surprised it didn't mention orthogonality.