yayagram VS fuzzcheck-rs

Two crates: - parse-size is criminally underrated. It allows you to parse input like "100 MB", "50 kb", "25b" into an integer of bytes. It parses input so intuitively and it works exactly the way I expected. I use it in https://github.com/r00ster91/splitter. - line_drawing is in my opinion the best line algorithm library there is. Extremely clean and nice to use. Exactly what I need for my project https://github.com/r00ster91/yayagram.

Still working on this puzzle game playable in the terminal with the mouse: https://github.com/r00ster91/yayagram In version 0.5.0 I've added a feature to darken all cells in a specific direction from the pointer but now I've figured out that a much better way is to constantly darken all cells in the four directions of the mouse pointer.

This is the repository with much more information in the README.md: https://github.com/r00ster91/yayagram, including an example at the bottom that might help you understand the game better if you are new to this kind of puzzle game. See also: https://en.wikipedia.org/wiki/Nonogram.

Hello Rustaceans. This is a clone of the puzzle game picross (also known as nonograms, griddlers and some other names) but with some additions and novelty, like a blue grid cell (wasn't used in this video) and an editor. If you want to try it, you can install the game with cargo install yayagram. Here is the repository with much more information in the README.md: https://github.com/r00ster91/yayagram, including an example that might help you understand the game better if you are new to this puzzle game.

Today I released the puzzle game yayagram which is picross/nonograms but for the terminal. I've been working on it for what feels like months now and it has really come a long way.

Fuzzcheck is a structure-aware fuzzer for rust. "Fuzzing" means feeding large amounts of data into a program and checking for crashes (Fuzzcheck also checks to make sure that all the properties your program should uphold – e.g. a sorting algorithm applied to a list of n items should always return a list of n items – are upheld). Fuzzcheck is an "evolutionary" fuzzer – this means that it generates a set of random inputs, sees what percentage of the program is executed for each input, and keeps inputs which have high levels of percentage of program executed. It then "mutates" these inputs – whereas fuzzers such as AFL/Hongfuzz/etc mutate raw bytes in place (e.g. they swap bytes at different positions, or insert a random byte at a given position to generate inputs similar to the chosen "high coverage" inputs), Fuzzcheck works directly on the Rust types (so it might swap the order of two items in a vec, or randomly insert a new item). It's a really powerful tool for finding lots of bugs.

If you want help with Win support (issues/8) maybe post it here to get it added to TWIR.

I am working on a code coverage viewer for my fuzzer (fuzzcheck). I described what I've done so far in this issue and I am hoping to release the first version within two weeks.

The implications for my fuzzer, fuzzcheck, are huge! Compiling fuzz tests is a lot easier. There should be no more need to create a separate fuzz folder, fuzz tests can be regular #[test] functions, private implementation details can be fuzz-tested as well, rust-analyser works as expected, documentation can be easily generated, etc. I can also attach a human-readable coverage report to every test case :)

Since I graduated, I have had a lot more time to work on fuzzcheck. I am trying to flesh it out, test it, and document it for a new release. It has always felt a bit rushed/experimental and now I am hoping to make it into something solid. I have also played with an egui interface for it, to visualise the tested code coverage, understand how the fuzzer’s decisions are made, and also to interactively tweak the fuzzer’s behaviour. It's a lot of work but it's slowly all coming together! :)

fuzzcheck-rs is really cool. It combines property-testing with fuzzing, getting the nice, structured nature of the former, and the coverage-driven search of the latter, but it works by mutating the structure directly instead of going through a bit string. So if you have a binary tree, going from A(B, C) to A(C, B) can be a single mutation away if that makes sense in your use case, instead of being arbitrarily far away in the bitstring approach.