| I think you've misunderstood the terms I used. By "problem domain" I mean the things that impose constraints — for a structural engineer, that'd be e.g. building materials, soil, weather, etc. The things that have tolerances. By "solution domain" I mean the space of human ingenuity that we can apply in our designs, in order to make something possible that wouldn't be possible with a naive approach. For a structural engineer, that's things like "suspension-bridge cabling" (more general principle: tensegrity) and, I dunno, "flying buttresses." The problem domain of building software — the parts that impose constraints — are things like what factors lead to robustness (or lack thereof) of a language runtime under production load; programming-language error-rate as a function of language-syntax UX design; evidence-driven software project scope analysis; the trade-offs involved in attempting to scale a process horizontally vs. vertically; the effects of state on ability to scale; etc. Someone who understands these things knows how to engineer software, the same as someone who understands material tolerances knows how to engineer a structure. If they only know that, then they can't draw you a building (that'd be an architect) — but they can take that architect's blueprint, and tell you whether the building described by it will fall over, and whether there's any simple thing you can do to solve that, or whether you need to draw a different building. But note that you learn the solution space naturally, over time, as you're exposed to the solutions people use in the field. A machinist will learn the tools of their trade as they run into them in the shop, and as other machinists demonstrate them, and as books refer to them, and as job-lots demand them. None of this requires academic rigor. It's just learning on your feet. A SWEng might not be aware of the academic result proving some more-optimal data structure exists for something. Just like a machinist might not be aware of a not-yet-commercialized maser CNC lathe. But they don't need to be, either. Very rarely does solving novel problems require novel tools. You can build the part you want to build with the machines you already have in your shop, and maybe one new one you buy off eBay. You can write the code you want to write with your not-so-optimal data structures, and maybe one new one you find in an ecosystem library. Every once in a while, getting things done might require you read a journal paper written by an academic. But let's be very clear: it doesn't take a degree in some field, to be able to read — and make use of! — journal papers from that field. We've got educational vloggers — people who perform on camera for a living — operationalizing stuff they saw in journal papers all the time! If they can do it, a professional in an adjacent field to the academic discipline can certainly do it. |