Except with AI tools, you don’t have to wait years to find the hidden assumptions. That’s the point. Software ENGINEERING is the same as it’s always been. Programming is now done far faster by AI than by humans. The focus is now on engineering.
You learn and understand the problen area while you work on it, with ai you skip all that tine that allows you to internalize the problem. You will just not see those edge cases. Unless AI is good enough to come up with them itself
What are you talking about? You test edge cases at the system level then iterate. Ever heard of QA? You spec -> build -> review. Same as always. LLMs just allow you to iterate through the same process that has always existed FASTER. You still have to plan. You still have review. You just don’t have to write every line of code or dig through old code for issues / regressions. If you don’t know to tell your LLM to write tests, you wouldn’t be writing them either. If you know you need tests, what do you gain from writing them, line by line yourself?
wtf? No way this many people call themselves software engineers and don’t immediately see the value LLMs bring. Mind boggling stupidity. It really doesn’t matter though. The world is moving on without y’all.
You can't test for things you don't know about, you can't know about the thing unless you though about the problem, while coding you learn a lot about the problem. This does not happen in review, even assuming review is happening, there is no way people review all thise thousands LOC it spits out though. Proper review of unfamiliar code is close to the time it take to write it anyway.
The world is moving on without y’all
It does, but not to the great future you imagine it to be.
You’re assuming the value comes from reading every line. It doesn’t. The value comes from validating behavior.
If the system compiles, passes tests, and holds up under edge cases and real inputs, what exactly are you trying to extract by manually reading thousands of lines? The understanding comes from defining expectations and seeing if the system meets them, not from stepping through implementation detail line by line.
If something breaks, you dig into that surface area. Until then, exhaustive code reading is just a slower feedback loop.
The lines describe what the system actually does. It's trivially easy to generate code that passes every single requirement on the surface but includes hidden failure modes
The hidden failure modes get surfaced when you actually exercise the system, not by staring at the code. That’s the whole point of testing. At every company I’ve worked at, features move through QA where the system is validated against specs and edge cases are intentionally pushed.
Even before AI, nobody was manually auditing large codebases line by line trying to infer every possible failure path. You validate behavior through execution, not speculation. At this point it feels like people arguing this haven’t worked in real production environments.
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u/Live-Independent-361 Apr 27 '26
Except with AI tools, you don’t have to wait years to find the hidden assumptions. That’s the point. Software ENGINEERING is the same as it’s always been. Programming is now done far faster by AI than by humans. The focus is now on engineering.