1. This example [1] compares a SIMD-accelerated implementation of an algorithm vs the naive implementation. This is usually referred to as an "oracle".
2. This example [2] tests an XML document object model library. The tests construct a sequence of operations to apply to a document ("add an element", "delete an element", "move an element" etc) and then assert properties that you expect to be true for a DOM tree (a parent and child are always cross-linked, for example)
[1]: https://github.com/shepmaster/jetscii/blob/8d7e44ad7da990ef1...
[2]: https://github.com/shepmaster/sxd/pull/21/files#diff-fc21cbf...
The difficult bit is finding invariants or approximations, but doing so is half of what makes the code more robust anyway!
The operational problem is that the tests sometimes take a little longer to run, so sometimes they have to be in a low-prio suite.
Fuzzing can be thought of as property based testing.
The key to PBT is that it allows tests to be generated and minimized automatically, enabling enormously larger volumes of tests than if the tests have to be generated manually.
I remember more circa 2010 attending a presentation by the guys from QuviQ who mentioned that their solution was used by the automotive industry in Sweden to verify embedded systems.