While reading Sipser’s book on theory of computation, it relies heavily on the concept of formal language, and machines that merely accept or reject an input.
But most useful programs we deal with do more than merely verify an input. They compute something. We may compute a solution for an equation instead of merely verify it. We may sum a list of numbers, or calculate some function on it.
Maybe “most” is an exaggeration since I can’t prove it. But still, it begs the question. Why not deal with machines than do more than merely verify?
Well what you are describing are the abstractions upon these models. So the ‘basis’ still remains true. Think of it this way:
So AFAIK, Sipser’s does not explain more intricate models like RAM machine or register machine, I recommend spending some time prompting ChatGPT or reading some books on it (don’t know any off hand).
TL;DR: The formal languages described in Sipser’s are the lowest levels of abstraction. Higher levels of theoretical abstractions do exist, which I described several here.
At the end, remember that theory and practice are to be used in tandem, neither will achieve anything alone. Modern languages we use today once resembled a formal, theoretical language, a PDA made of PDAs. Look at the earliest C compilers, for example: https://github.com/mortdeus/legacy-cc/tree/master/prestruct. It’s just a giant state machine.
I highly recommend picking up an HDL (Hardware Description Language), or implementing one if you got the time. This shall give you a clear picture of how theory differs from practice.
PS: Modern systems languages also never give a true picture of what programming is, and what should it be about. I recommend picking up a language closer to functional than imperative. I recommend OCaml, or Haskell if could be. That is the other side of formality.