proptest
fast-check
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proptest | fast-check | |
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15 | 21 | |
1,554 | 4,071 | |
3.2% | - | |
8.3 | 9.8 | |
6 days ago | 4 days ago | |
Rust | TypeScript | |
Apache License 2.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
proptest
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What Are The Rust Crates You Use In Almost Every Project That They Are Practically An Extension of The Standard Library?
proptest: Property-based testing with random input generation.
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Iterating on Testing in Rust
Isn't proptest something that could handle this?
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The birth of a package manager [written in Rust :)]
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.
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Hey Rustaceans! Got a question? Ask here (11/2023)!
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.
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Hey Rustaceans! Got a question? Ask here! (32/2022)!
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!
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Which Mutex to use in this case (independent tasks, partially under contention)
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.
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Is RUST aiming to build an ecosystem on scientific computing?
See also property testing
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Is there a rust way for doing TDD?
It is not hard to do TDD with integration tests either, just write tests that encapsulate the properties you want your library to hold then implement those properties one by one. This is also known as property driven/based testing which then couples well with fuzzers or randomized testing where you randomize the input rather than using large amounts of test assets. This also help you catch a lot more bugs that you never even thought of. proptest is a good library that can help with this.
- Go Fuzzing
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In praise of property-based testing (2019)
I'm quite new to property testing, first introduced recently via a Rust property testing framework proptest[0]. So far i've had the feeling that property testing frameworks need to include a way to rationalize complexity, as their assumptions can easily fall short as you illustrated.
Eg the simplest example might be an application where you input an integer, but a smaller int actually drives up the complexity. This idea gets more complex when we consider a list of ints, where a larger list and larger numbers are simpler. Etcetc.
It would be neat if a proptest framework supported (and maybe they do, again, i'm a novice here) a way to rank complexity. Eg a simple function which gives two input failures and you can choose which is the simpler.
Another really neat way to do that might be to actually compute the path complexity as the program runs based on the count of CPU instructions or something. This wouldn't always be a parallel, but would often be the right default i imagine.
Either way in my limited proptest experience, i've found it a bit difficult to design the tests to test what you actually want, but very helpful once you establish that.
fast-check
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The 5 principles of Unit Testing
Libraries like JSVerify or Fast-Check offer essential tools to facilitate property-based testing.
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How to Survive Your Project's First 100k Lines
Strong agree!
For JavaScript, I suggest folks check out fast-check [0] and this introduction to property-based testing that uses fast-check [1].
This is broadly useful, but one specific place I've found it helpful was to check redux reducers against generated lists of actions to find unchecked edge cases and data assumptions.
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[AskJS] Should I be generating random data for parameters when unit testing?
There's a library for exactly that: FastCheck.
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Integrate Jest and fast-check together
It makes @fast-check/jest, the best option to integrate Jest and fast-check, as it provides an abstraction over both to ease their mutual integration.
- I Created an API to Generate Mock Information
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Generating dummy entities with random data for tests based on types
The closest that I know of (and I have not used this) is zod-fast-check. It generates fast-check “arbitraries” (test data generators) for property-based testing based on zod schemas. Of course, this requires that you use zod to define your types, which has some downsides. Fortunately there is another tool, ts-to-zod, (which I also have not used) which will codegen zod schemas based on TS type definitions. If you thread these four libraries together you should end up with the ability to write random tests on generated data with very little overhead. In theory.
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In praise of property-based testing (2019)
I've been aware of property-based testing for a number of years now, but never had a good opportunity to give it a try. Then the past year I had a piece of serialisation/de-serialisation code, which was the perfect opportunity for a rather simple property-based test. That gave me the hang of it, and found two (minor, but still) bugs.
Then recently I had a fairly larger, more error prone piece of work that lend itself very well to property-based testing, and it's been a godsend. It helped me discover a number of bugs, this time with the risk of causing privilege escalation. And since the proptests started succeeding reliably, I've been very confident that a rather complex piece of code now actually does what it's supposed to.
If you're working in JavaScript, I can recommend fast-check [1].
Another interesting approach, that I haven't yet tried, is Quickstrom [2], basically Puppeteer for proptests. It opens a webpage in a browser, performs some random interactions (pressing buttons, entering data, etc.), and then verifies that properties you specified still hold.
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Fastcheck: Property based testing for JavaScript and TypeScript
The about says:
Property based testing framework for JavaScript (like QuickCheck) written in TypeScript
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Effective Property-Based Testing
For JS and TypeScript, the best property testing library I've encountered so far is fast-check https://github.com/dubzzz/fast-check
What are some alternatives?
quickcheck - Automated property based testing for Rust (with shrinking).
afl.rs - 🐇 Fuzzing Rust code with American Fuzzy Lop
Unexpected - Unexpected - the extensible BDD assertion toolkit
trust - Travis CI and AppVeyor template to test your Rust crate on 5 architectures and publish binary releases of it for Linux, macOS and Windows
jest - Delightful JavaScript Testing.
tarpaulin - A code coverage tool for Rust projects
trevor - 🚦 Your own mini Travis CI to run tests locally
tape - tap-producing test harness for node and browsers
test-each - 🤖 Repeat tests. Repeat tests. Repeat tests.
Clippy - A bunch of lints to catch common mistakes and improve your Rust code. Book: https://doc.rust-lang.org/clippy/
nyc - the Istanbul command line interface
loadtest - Runs a load test on the selected URL. Fast and easy to use. Can be integrated in your own workflow using the API.