Related: one of my colleagues created [wasm-smith](https://github.com/bytecodealliance/wasm-tools/tree/main/cra...) for fuzzing wasmparser and Wasmtime.
I don’t think I’ve ever seen standards work be so high quality.
I look forward to reading more about how they do the targeting and tuning.
For example, to get a nice spread of function sizes, Wasmlike limits the AST depth of new functions based on how many were generated so far. If the maximum depth isn’t limited, program size and generation time explodes. If the depth is just a simple continuation of where the function was first called from, the resulting program will have a ridiculous number of one liner functions without any medium sized ones.
There are also a few ‘feature’ flags to enable/disable things like floating point operations in the case that it would affect results, but that never actually came up in testing, and the tests runs we did used roughly the same configurations.
The idea is to enable feedback directed fuzzing for a senantically valid random program generator. I believe that adding this to the Wasm fuzzer is the ‘next step’ in the ongoing research.
So not quite swarm testing, but a bit closer in terms of focused fuzzing instead of a shotgun approach.