Okay, had to register just because of the positive comments so far. I know nearly nothing about machine learning and may be a bit biased, but has anybody actual

Okay, had to register just because of the positive comments so far. I know nearly nothing about machine learning and may be a bit biased, but has anybody actually read the paper? As I understand it, the model is trained on what compilers do anyway (the training data consists of compiler output from -O0 and -O3), so it won't come up with the kind of clever tricks that a human might. One of their cherry-picked examples (fig5, prstree_empty) is incorrect: in one branch it is supposed to return true if the value at rdi+0x10 is a null pointer, but in the optimized version it falls through from "sete %al" to the next instruction, which overwrites that register so it will always return true. It would be easy to fix by inserting another return instruction, but this clearly shows the neural net doesn't "understand" how to generate correct code. Besides some peephole optimizations, a lot of what it learned seems to be how to fool automated checks, and in this case even the authors who didn't spot the bug. See also the section about verifier exploits, which are embarassingly simple: a completely empty "if statement" which depends on memory causes a function to pass when it clearly does nothing more than always return false. The 6.2% figure is after "human verification" (again, one of the examples in the paper is broken!), down from 8.3% using only the automated verifier. Not impressed.