If you wrote a correct binary search algorithm and you observed that, under one language implementation, the time complexity scaled linearly with the size of the input instead of logarithmically, you would think the semantics of the program were changed.
If you used an in-place sort algorithm and observed memory requirements that scale super-linearly with the size of the input, you would think the semantics of the program were changed.
In languages with such tail call guarantees, tail recursion _is_ a loop. It semantically encodes constant space complexity.
Programming language semantics as in https://en.wikipedia.org/wiki/Semantics_(programming_languag... is usually decoupled from space complexity. An interpreter or emulator is considered to preserve language semantics even if it changes time or space complexity.
I’m talking about how a programming language specification specifies the semantics of the programming language. It usually does not specify the time and space complexity of its basic operations, be it function calls or arithmetic operators. For example, multiplication could be implemented as O(n) repeated addition instead of in constant time. That would probably be a bad implementation (even on CPUs that only support addition), but it wouldn’t violate the semantics of the programming language.
If you used an in-place sort algorithm and observed memory requirements that scale super-linearly with the size of the input, you would think the semantics of the program were changed.
In languages with such tail call guarantees, tail recursion _is_ a loop. It semantically encodes constant space complexity.