With “classic” engineering at least you have the immutable laws of physics to judge your work, but with software we have no such luck - software is infinitely pliable in ways equivalent to bending the laws of physics in classical engineering. Your bridge may not be sound at one Earth gravity, or your software might not work reliably with a gigabyte of memory, but it’s like we can place your bridge under half G by giving the software twice as much memory. And we can do all that after building our “bridges”.

I would even suggest software engineering can also be described as “applied poetry”, where we write precise prose designed to elicit specific responses from machines, but I guess that analogy was taken by “prompt engineering”, which feels like “applied sorcery”.

> Remember "imaginary numbers" from algebra? You could do funky stuff with them like take the square root of negative nine. Imaginary numbers are generally useless for the physical world that we experience. However, they turned out to be very useful in the field of electronics. More specifically, they had predictive power in the field of electronics. Using imaginary number math, one can predict behavior of electrons and the accuracy of such predictions in models can be measured.

Like here, I struggle to understand what the point is. Is predicting the behavior of electronics "useless for the physical world we experience"? And that's ignoring the author's apparent ignorance of all the other ways in which complex numbers are useful in the physical world besides taking negative numbers' square roots.

Language is for us, not the other way around. It's common usage changes.

The article seems to make some fairly confusing statements. Why is the bar higher for software engineering, than that of civil engineering otherwise? Statements such as:

> "there is no objective reality inside software"

> "if there are many solutions to the same problem, which one is "better"?"

Is the exact same subjective goal that a objective engineering constraint has in any other engineering field. There are many ways to design and build a bridge, but the engineering aspect of it needs to model reality and account for it in such a way that the bridge to build conforms to said requirements, in a provable way. That's why engineers can be held responsible when mistakes are made.

Software Engineering can be done in the same way. This, however, depends entirely on the culture. My first decade in the field, I was fortunate to only be exposed to en environment and culture that developed software in a provably correct way, or at the very least, aspired to. The latter decade, not so fortunate. With the advent of generative AI, it's become far worse. The challenge is to carve out enough space outside the purview of "management" that wants problems solved with particular tools, regardless of applicability to said problems, and it's becoming insurmountable. Signal to noise disappearing. The idea of buying land and tending to a farm, evermore appealing.

I wonder if the author perhaps has not worked on software that comes with actual engineering constraints. There are plenty of software systems where <if it doesn't work as it should>, people die.

Given all the ways that software can have those same significant impacts, I believe that there should be some sort of level at which software developers are called engineers. However, I believe that designation should come with all the professional oversight that other engineering disciplines have.

Overall though I found this post incredibly hard to read. It's incredibly long and wordy though that's par for the course for these petty semantic arguments.

Luckily now I'm simply a "computer science instructor", though many argue what I teach is "software engineering"... Doh!

I design solutions to computational problems. I also happen to implement them a lot of the time, because code was trivial to implement even before LLMs. What does that make me if not an engineer? I'm open to suggestions.

I'd say software engineering better fits economics these days. Maybe with a Psych major to maximize the dark patterns.

e.g. his 2021 book " Modern Software Engineering"

> Software engineering is the application of an empirical, scientific approach to finding efficient, economic solutions to practical problems in software.

https://www.davefarley.net/?p=352

https://www.goodreads.com/en/book/show/57345270-modern-softw...

https://productdeveloper.net/modern-software-engineering/

Anything that exists in reality and is observable by definition is tightly bound by the laws of physics and chemistry. Software is too.

>Software is a lot like math,

Probably referring to computer science. Computer science is neither about computers nor is it a science. It is a math. Software is like math but applied.

>The only limitation is the imagination of the creator of the virtual world (and perhaps the pesky limitations of computer resources)

computer resources: AKA physical laws. And these "laws" highly limit us in what we can do. We are definetely not operating in some kind of playground where we can be virtual gods, not even close, that's why entire swe teams are involved and paid a lot in software.

Honestly the main difference between "Software Engineering" and "Engineering" is that software is more an "art". We make up a bunch of technical nomenclature for it (like design patterns which sounds technical but is mostly made up and more artsy then say statistical mechanics) but it's mostly similar to sculpture or some artistic creation as we sort of piece everything together by instinct.

The difference between this and engineering is usually engineering involves mathematical modeling and testing heavily in development, while software engineering (usually) does not involve mathematical modeling and software testing is more of a catch-all to find bugs.

Type checking is mathematical modeling, but I wouldn't call it the core of software engineering. I guess this is where the categories get blurry.

This is definably not engineering.

That said, there's been a lot of changes since this was written that was relevant to the point being made, so I'm not sure I necessarily disagree with the article as written at the time. If we take the metric I used in that blog post and asked "what if we were handed current-gen AI in 2006, how well would it do?", my answer is that it would do noticeably less well. This is even more true if we instead go back to 2006 and use current hardware tech but have to train on what was available at the time.

Source control was fairly popular at that point, although it hadn't penetrated everywhere yet. I don't recall a lot of curmudgeons complaining that Real Programmers Don't Need Source Control at the time, it was pretty obvious that a record of what was happening in the source code base was useful. However, IIRC, in the open source world we'd mostly have been on cvs. svn was well into the process of eating it but still had a ways to go yet. "git" had just barely been born and it would be years before it was a serious force.

Beyond that, a lot of stuff I take for granted in the engineering domain was either immature or non-existent. I was on the cutting edge bothering everyone about the importance of unit testing. There were communities like Perl that had a strong culture of it and a lot of support, but still a lot of people who would use it without that and a lot of language communities without the culture. A lot of projects banged together their own haphazard solutions, but a lot of projects just went without. No devops. Metrics was something I'd never really heard of. Logs probably went to your disk and the idea of unifying them was just at the beginning. QA was much less established. QA was generally not a team that would be using much automation, it would be all hand testing. CI/CD wasn't a term yet because hardly anyone had anything even resembling it. Bug trackers were still pretty bad and not everyone used them.

I'm sure a lot of people will go "yeah, but I was programming in 2006 and I had all those things", because there were certainly teams that did. Go look at Microsoft Window's dev team, for instance, and they'd have most of what we'd consider a modern development stack, albeit with some quirks we'd find odd. It's not that none of this existed, it's that it wasn't considered just the baseline for a project to be minimally competent the way it is now. And rather than the programmers having an abundance of free options and an even larger abundance of paid and hosted options, to the point it's hard to poke through them all, they're using one of a few very expensive vendors or they put it together themselves.

Introducing an AI into a code base of tens or hundreds of thousands of lines, with minimal testing, trained on the code practices of that era... a modern programmer could probably still work with it. You could tell it about unit testing and it would know what you meant, it would probably just need to be prompted to do it. You could put together unified logging with some work. You could solve the problems... but your solutions would basically be putting in those 2026 guard rails. You'd have the same uphill battle convincing people of the time that this wasn't just a nice-to-have but a bare necessity, though it would be a much less uphill battle when you have a tool that can do it with a lot less effort, making the cost/benefit tradeoff a lot more appealing when the cost is so much lower. But a lot of programmers of the time would make even larger messes even more quickly with AIs happy to do so if you didn't give them some guidance on how to use it. They would, of course, figure it out eventually, since "they" are also "us", and we collectively figured this out without AIs helping us along. But boy oh boy would there be some messes made first.

I think some element of the problem is that, yes, programming was once a wild and wooly frontier of cowboys and crazy people doing crazy things. But one step at a time, the field has grown up, and a lot of people haven't noticed and still have this "cowboy programmer" idea in their mind. I don't know exactly when you might want to say the field became a real engineering field, with its own practices based on the local cost/benefit tradeoffs and its own procedures and its own "bare minimum for competence" standards. That partially depends on your own definitions. Some people who want to put the "bare minimum" at something like "uses dependent types and carries proofs for every line of code" might say we're not there yet, but you can always set the bar higher. But I think a lot of people have not looked around and realized that, yeah, actually, we have crossed that bar.

Personally I put it around a decade ago. But I think we're there. We can stop wringing our hands about not being "real engineers" and stop looking over at what the "real engineers" do. We have better solutions for our problems then any amount of copying what they do could give us, just like they can't just lift our practices and apply it to their work because it doesn't fit their world. It's gotten to the point where dropping what we've got in hand now and trying to jam some other "real engineering" discipline's tools in would be a rather substantial regression. We can chill out a bit.

We can also be glad that all the calls to try to systematize the field so it would be "real engineering" didn't go and lock in the 2006 idea of what "good software engineering" is so that we got stuck to it and we couldn't progress to where we are today. Two years ago I would have said maybe such an effort could do something useful, but then AI came along and overturned the apple cart and now, once again, I would be very suspicious of any attempt to lock in July 2026 practices as The One Correct Way To Write Software For The Next 100 Years.