> Give time to researchers to gather enough mappings between source code and machine code, get used to training large predictive models, and you shall see top n

> Give time to researchers to gather enough mappings between source code and machine code, get used to training large predictive models, and you shall see top notch decompilers that beat all engineered methods. Decompilation is about dependencies which makes it a graph problem. One such problem is boolean satisfiability and this particular kind of problem is extremely important. It also very easy to gather mappings between CNF and solutions. Actually, randomization of standard benchmarks is now part of SAT competitions, AFAIK. Have you seen any advances there using large predictive models? Proper decompilation is even harder, it is much like halting problem than SAT. Imagine that there is a function that gets inlined and, therefore specialized. One definitely wants source for the original function and calls to it, not a listing of all specializations. This moves us to the space of "inverse guaranteed optimization" and as such it requires approximation of the solution of halting problem.