This is a page for listing people who are using Hypothesis and how excited they are about that. If that’s you and your name is not on the list, this file is in Git and I’d love it if you sent me a pull request to fix that.
At Stripe we use Hypothesis to test every piece of our machine learning model training pipeline (powered by scikit). Before we migrated, our tests were filled with hand-crafted pandas Dataframes that weren’t representative at all of our actual very complex data. Because we needed to craft examples for each test, we took the easy way out and lived with extremely low test coverage.
Hypothesis changed all that. Once we had our strategies for generating Dataframes of features it became trivial to slightly customize each strategy for new tests. Our coverage is now close to 90%.
Full-stop, property-based testing is profoundly more powerful - and has caught or prevented far more bugs - than our old style of example-based testing.
Kristian Glass - Director of Technology at LaterPay GmbH¶
Hypothesis has been brilliant for expanding the coverage of our test cases, and also for making them much easier to read and understand, so we’re sure we’re testing the things we want in the way we want.
When I first heard about Hypothesis, I knew I had to include it in my two open-source Python libraries, natsort and fastnumbers . Quite frankly, I was a little appalled at the number of bugs and “holes” I found in the code. I can now say with confidence that my libraries are more robust to “the wild.” In addition, Hypothesis gave me the confidence to expand these libraries to fully support Unicode input, which I never would have had the stomach for without such thorough testing capabilities. Thanks!
At Sixty North we use Hypothesis for testing Segpy an open source Python library for shifting data between Python data structures and SEG Y files which contain geophysical data from the seismic reflection surveys used in oil and gas exploration.
This is our first experience of property-based testing – as opposed to example-based testing. Not only are our tests more powerful, they are also much better explanations of what we expect of the production code. In fact, the tests are much closer to being specifications. Hypothesis has located real defects in our code which went undetected by traditional test cases, simply because Hypothesis is more relentlessly devious about test case generation than us mere humans! We found Hypothesis particularly beneficial for Segpy because SEG Y is an antiquated format that uses legacy text encodings (EBCDIC) and even a legacy floating point format we implemented from scratch in Python.
Hypothesis is sure to find a place in most of our future Python codebases and many existing ones too.
Just found out about this excellent QuickCheck for Python implementation and ran up a few tests for my bytesize package last night. Refuted a few hypotheses in the process.
Looking forward to using it with a bunch of other projects as well.
I have written a small library to serialize
dicts to MariaDB’s dynamic
columns binary format,
mariadb-dyncol. When I first
developed it, I thought I had tested it really well - there were hundreds of
test cases, some of them even taken from MariaDB’s test suite itself. I was
ready to release.
Lucky for me, I tried Hypothesis with David at the PyCon UK sprints. Wow! It found bug after bug after bug. Even after a first release, I thought of a way to make the tests do more validation, which revealed a further round of bugs! Most impressively, Hypothesis found a complicated off-by-one error in a condition with 4095 versus 4096 bytes of data - something that I would never have found.
Long live Hypothesis! (Or at least, property-based testing).
Adopting Hypothesis improved bidict’s test coverage and significantly increased our ability to make changes to the code with confidence that correct behavior would be preserved. Thank you, David, for the great testing tool.
Hypothesis is the single most powerful tool in my toolbox for working with algorithmic code, or any software that produces predictable output from a wide range of sources. When using it with Priority, Hypothesis consistently found errors in my assumptions and extremely subtle bugs that would have taken months of real-world use to locate. In some cases, Hypothesis found subtle deviations from the correct output of the algorithm that may never have been noticed at all.
When it comes to validating the correctness of your tools, nothing comes close to the thoroughness and power of Hypothesis.
One extremely satisfied user here. Hypothesis is a really solid implementation of property-based testing, adapted well to Python, and with good features such as failure-case shrinkers. I first used it on a project where we needed to verify that a vendor’s Python and non-Python implementations of an algorithm matched, and it found about a dozen cases that previous example-based testing and code inspections had not. Since then I’ve been evangelizing for it at our firm.
I am using Hypothesis as an integral part of my Python workshops. Testing is an integral part of Python programming and whilst unittest and, better, pytest can handle example-based testing, property-based testing is increasingly far more important than example-base testing, and Hypothesis fits the bill.
We’ve been using Hypothesis in a variety of client projects, from testing Django-related functionality to domain-specific calculations. It both speeds up and simplifies the testing process since there’s so much less tedious and error-prone work to do in identifying edge cases. Test coverage is nice but test depth is even nicer, and it’s much easier to get meaningful test depth using Hypothesis.
Hypothesis is being used as the engine for random object generation with my open source function fuzzer battle_tested which maps all behaviors of a function allowing you to minimize the chance of unexpected crashes when running code in production.
With how efficient Hypothesis is at generating the edge cases that cause unexpected behavior occur, battle_tested is able to map out the entire behavior of most functions in less than a few seconds.
Hypothesis truly is a masterpiece. I can’t thank you enough for building it.
Just minutes after our first use of hypothesis we uncovered a subtle bug in one of our most used library. Since then, we have increasingly used hypothesis to improve the quality of our testing in libraries and applications as well.
Thank you very much for creating the (probably) most powerful property-based testing framework.
With a micro-service architecture, testing between services is made easy using Hypothesis in integration testing. Ensuring everything is running smoothly is vital to help maintain a secure network of Virtual Power Plants.
It allows us to find potential bugs and edge cases with relative ease and minimal overhead. As our architecture relies on services communicating effectively, Hypothesis allows us to strictly test for the kind of data which moves around our services, particularly our backend Python applications.
I know there are many more, because I keep finding out about new people I’d never even heard of using Hypothesis. If you’re looking to way to give back to a tool you love, adding your name here only takes a moment and would really help a lot. As per instructions at the top, just send me a pull request and I’ll add you to the list.