proptest VS tarpaulin

proptest: Property-based testing with random input generation.

Isn't proptest something that could handle this?

https://github.com/proptest-rs/proptest

Proptest is a Rust crate for property-based testing. Recently I wanted/needed to manually implement a proptest strategy for my own type, and I realized that there is not that much material on how to do it. So I wrote a post where I tried to describe what I learned. It's a bit niche, but I hope that someone at some point will find it useful.

Take a look at proptest.

Yes exactly, you can also add to this fuzzing and property based testing.

proptest is great! It generates random input data according to some rules, and if the input fails it saves random seed into a file so that failing inputs are guaranteed to be tested on the subsequent runs (as well as new random inputs). It also doesn't immediately stop on fail but tries to find a minimal failing input first.

The only other crate I could find is proptest, but it looks a lot more complicated, and I don't know if lets you skip the shrinking step as quickcheck does. I've been reading the book and going through the docs, but a quick answer would be appreciated.

We just released proptest 1.1.0, a property-testing framework for Rust. Proptest has recently found new maintainers, and this marks the first new release of proptest in ~2 years.

Hi, I'm working on a fuzzer, that fuzzes APIs based on OpenAPI specification. I'd like to implement shrinking. It means that when an interesting input (for the API) is found, I'd like to create the smallest possible input that still causes the same behaviour of the API. I'd like to implement a payload generation via proptest, because it already has the shrinking ability. I'm having issues implementing the JSON object as a proptest strategy. Here is what I tried so far. I explained it in a detail in stackoverflow question but it did not reach many people. Thanks for your help!

Third, if you're opting out of a compile-time safety guarantee in the name of performance, test heavily (high-coverage unit tests, property testing, fuzzing, differential fuzzing, etc.) and make use of tools like Loom and Miri's runtime data race detector for unsafe code, which can catch stuff that is beyond the scope of the compiler's guarantees.

I’ll add tarpaulin, a crate I use for code coverage analysis.

Any pros/cons compared to tarpaulin?

What about tarpaulin : https://github.com/xd009642/tarpaulin

Note that the tool cargo-tarpaulin provides code coverage.

There is https://github.com/xd009642/tarpaulin for code coverage, but I'm not sure how widely it's used.

I know giving a link is not a tutorial, but I use https://github.com/xd009642/tarpaulin for this.

Writing software test for [Toql](https://crates.io/crates/toql) and shaking out smaller bugs here and there. [Tarpaulin](https://github.com/xd009642/tarpaulin) is such a fantastic tool!

Code quality also has become quite better than the last year. Almost codes in the project are tested by either unit or integration tests. Code coverage using tarpaulin is above 90%.