morehouse added a subscriber: metzman.
morehouse added a comment.
Thanks for sharing your data. Took a quick look and seems promising.
I would like to try this on FuzzBench before accepting the patch though.
FuzzBench has a very nice experimental framework for evaluating changes like
this.
> It seems that FuzzBench does not accept this parallel mode evaluation.
I talked to @metzman who manages FuzzBench. Sounds like you're correct,
FuzzBench uses only one worker process in fork mode. @metzman said we could
probably run a special experiment with more workers to evaluate this patch.
Another approach that might be worth doing, is to make the patch effective even
for a single worker. For example, maybe we randomly pick from a subset of the
corpus for that single worker.
Also, I'm curious how the number of fork-mode workers affects efficacy. I can
imagine with lots of workers that this patch could perform much worse.
Specifically if we have a small number of corpus elements per worker, the
crossover mutation becomes quite limited.
================
Comment at: compiler-rt/lib/fuzzer/FuzzerFork.cpp:143
for (size_t i = 0; i < CorpusSubsetSize; i++) {
- auto &SF = Files[Rand->SkewTowardsLast(Files.size())];
- Seeds += (Seeds.empty() ? "" : ",") + SF;
- CollectDFT(SF);
+ size_t j = Rand->SkewTowardsLast(AverageSize);
+ size_t m = j + StartIndex;
----------------
Does keeping this skew within each subset of the corpus help? Would a
uniform-random approach change efficacy?
================
Comment at: compiler-rt/lib/fuzzer/FuzzerFork.cpp:150
+ }
+ else {
+ auto &SF = Files[Rand->SkewTowardsLast(Files.size())];
----------------
So we would still use the current strategy on the rare occasion when we get an
`m` index past the end of `Files`. I imagine there's always some cases where
the original strategy will be beneficial at least for the crossover mutation.
Maybe we could make the probability of using the current strategy more
explicit. Something like
```
if (RandInt(100) < 90)
// Use new strategy
else
// Use old strategy
```
Repository:
rG LLVM Github Monorepo
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https://reviews.llvm.org/D100161/new/
https://reviews.llvm.org/D100161
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