Hypothesis is easy to learn and powerful implementation of property based testing, and generally an invaluable tool. At Lyst we've used it in a wide variety of situations, from testing APIs to machine learning algorithms and in all cases it's given us a great deal more confidence in that code.
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.
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.
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.
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!
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.
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.
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