This is indeed a great technique. The only way it could be improved is to expand on step 3 by keeping a list of the random mutation functions called and the order in which they were called, then if the test passes you throw that list away and generate a new list with the next seed. But if the test fails then you go through the following procedure to "shrink" the list of mutations down to a minimal (or nearly minimal) repro:

1. Drop the first item in the list of mutations and re-run the test. 2. If the test still fails and the list of mutations is not empty, goto step 1. 3. If the test passes when you dropped the first item in the mutation list, then that was a key part of the minimal repro. Add it to a list of "required for repro" items, then repeat this whole process with the second (and subsequent) items on the list.

In other words, go through that list of random mutations and, one at a time, check whether that particular mutation is part of the scenario that makes the test fail. This is not guaranteed to reach the smallest possible minimal repro, but it's very likely to reach a smallish repro. Then in addition to printing the failing seed number (which can be used to reproduce the failure by going through that shrinking process again), you can print the final, shrunk list of mutations needed to cause the failure.

Printing the list of mutations is useful because then it's pretty simple (most of the time) to turn that into a non-RNG test case. Which is useful to keep around as a regression test, to make sure that the bug you're about to fix stays fixed in the future.