If you come up with a strategy that seems to "solve programming", then you know for certain there must be a flaw in it, and you need to identify where it is that corners must be cut and how.

Computer science is an introspective discipline because it studies the essential difficulty of problems regardless of the process taken to solve them, and programming itself (i.e. the problem of producing a correct, or correct-enough program) is such a problem that can be, and has been studied. The question of learning whether a program X satisfies some correctness property P is known as the model-checking problem, and we know that answering it with certainty is intractable. For example, some properties that are true for some program would take no less than 10 minutes to verify (regardless of how that verification is done), others will take no less than 10 hours, others no less than 10 months, others no less than 10 years and so on, and we don’t know ahead of time whether the proprty is true, and if it is, where on this spectrum it falls.

So suppose you decide some property must be proven with full certainty, the question becomes, how long do you wait before giving up waiting for the validation and what do you do when you give up? If you then decide that you’re okay with less than 100% confidence, what approach do you take and how much confidence do you actually have? The problem with that is that the answer to that question often requires a deep understanding of the implementation. I.e. if you have two programs, X and Y, that compute the same function, one less-than-perfect approach would give you 99% confidence with one of them, but only 10% confidence with another.