I’m in the process of trying to turn my work on Hypothesis into a PhD and I realised that I don’t have a good self-contained summary as to why researchers should care about it.
So this is that piece. I’ll try to give a from scratch introduction to the why and what of Hypothesis. It’s primarily intended for potential PhD supervisors, but should be of general interest as well (especially if you work in this field).
Why should I care about Hypothesis from a research point of view?
The short version:
Hypothesis takes an existing effective style of testing (property-based testing) which has proven highly effective in practice and makes it accessible to a much larger audience. It does so by taking several previously unconnected ideas from the existing research literature on testing and verification, and combining them to produce a novel implementation that has proven very effective in practice.
The long version is the rest of this article.
The remainder is divided into several sections:
- What is Hypothesis? is a from-scratch introduction to Hypothesis. If you are already familiar with property-based testing (e.g. from QuickCheck) you can probably skip this.
- How is Hypothesis innovative? is about the current state of the art of Hypothesis and why it’s interesting. If you’ve already read How Hypothesis Works this is unlikely to teach you anything new and you can skip it.
- What prior art is it based on? is a short set of references for some of the inspirations for Hypothesis. You probably shouldn’t skip this, because it’s short and the linked material is all interesting.
- What are some interesting research directions? explores possible directions I’m looking into for the future of Hypothesis, some of which I would hope to include in any PhD related to it that I worked on. You probably shouldn’t skip this if you care about this document at all.
- What should you do with this information? simply closes off the article and winds things down.
So, without further ado, the actual content.
What is Hypothesis?
Hypothesis is an implementation of property-based testing, an idea that originated with a Haskell library called QuickCheck.
Property-based testing is a way to augment your unit tests with a source of structured random data that allows a tool to explore the edge cases of your tests and attempt to find errors automatically. I’ve made a longer and more formal discussion of this definition in the past.
An example of a property-based test using Hypothesis:
from hypothesis import given from hypothesis import strategies as st @given(st.lists(st.integers())) def test_sort_is_idempotent(ls): sort1 = sorted(ls) assert sorted(sort1) == sort1
This exposes a normal function which can be picked up by a standard runner such as py.test. You can also just call it directly:
if __name__ == '__main__': test_sort_is_idempotent()
When the test is run, Hypothesis will generate random lists of integers and pass them to the test. The test sorts the integers, then sorts them again, and asserts that the two results are the same.
As long as the test passes for every input Hypothesis feeds it this will appear to be a normal test. If it fails however, Hypothesis will then repeatedly rerun it with progressively simpler examples to try and find a minimal input that causes the failure.
To see this, suppose we implemented the following rather broken implementation of sorted:
def sorted(ls): return list(reversed(ls))
Then on running we would see the following output:
@given(st.lists(st.integers())) def test_sort_is_idempotent(ls): sort1 = sorted(ls) > assert sorted(sort1) == sort1 E assert [0, 1] == [1, 0] E At index 0 diff: 0 != 1 E Use -v to get the full diff sorting.py:12: AssertionError ---- Hypothesis ---- Falsifying example: test_sort_is_idempotent(ls=[0, 1])
Hypothesis probably started with a much more complicated example (the test fails for essentially any list with more than one element) and then successfully reduced it to the simplest possible example: A list with two distinct elements.
Importantly, when the test is rerun, Hypothesis will start from the falsifying example it found last time rather than trying to generate and shrink a new one from scratch. In this particular case that doesn’t matter very much - the example is found very quickly and it always finds the same one - but for more complex and slower tests this is an vital part of the development work flow: It means that tests run much faster and don’t stop failing until the bug is actually fixed.
Tests can also draw more data as they execute:
@given(st.lists(st.integers(), min_size=1), st.data()) def test_sort_is_idempotent(ls, data): ls.sort() i = data.draw(st.integers(0, len(ls) - 1)) assert ls[i - 1] <= ls[i]
This fails because we’ve forgotten than
i may be zero, and also about Python’s negative indexing of lists:
@given(st.lists(st.integers(), min_size=1), st.data()) def test_sort_is_idempotent(ls, data): ls.sort() i = data.draw(st.integers(0, len(ls) - 1)) > assert ls[i - 1] <= ls[i] E assert 1 <= 0 sorting.py:15: AssertionError ---- Hypothesis ---- Falsifying example: test_sort_is_idempotent(ls=[0, 1], data=data(...)) Draw 1: 0
Simplification and example saving work as normal for data drawn in this way.
Hypothesis also has a form of model based testing, in which you specify a set of valid operations on your API and it attempts to generate whole programs using those operations and find a simple one that breaks.
How is Hypothesis innovative?
From an end user point of view, Hypothesis adds several important things:
- It exists at all and people use it. Historically this sort of testing has been found mostly within the functional programming community, and attempts to make it work in other languages have not seen much success or widespread adoption. Some of this is due to novel implementation details in Hypothesis, and some is due to design decisions making it “feel” like normal testing instead of formal methods.
- Specifying data generators is much easier than in traditional QuickCheck methods, and you get a great deal more functionality “for free” when you do. This is similar to test.check for Clojure, or indeed to the Erlang version of QuickCheck, but some of the design decisions of Hypothesis make it significantly more flexible here.
- The fact that arbitrary examples can be saved and replayed significantly improves the development work-flow. Other implementations of property-based testing either don’t do this at all, only save the seed, or rely on being able to serialize the generated objects (which can break invariants when reading them back in).
- The fact that you can generate additional data within the test is often extremely useful, and seems to be unique to Hypothesis in this category of testing tool.
These have worked together well to fairly effectively bring property based testing “to the masses”, and Hypothesis has started to see increasingly widespread use within the Python community, and is being actively used in the development of tools and libraries, as well as in the development of both CPython and pypy, the two major implementations of Python.
Much of this was made possible by Hypothesis’s novel implementation.
From an implementation point of view, the novel feature of Hypothesis is this: Unlike other implementations of property-based testing, it does not need to understand the structure of the data it is generating at all (it sometimes has to make guesses about it, but its correctness is not dependent on the accuracy of those guesses).
Hypothesis is divided into three logically distinct parts:
- A core engine called Conjecture, which can be thought of as an interactive fuzzer for lightly structured byte streams.
- A strategy library, which is designed to take Conjecture’s output and turn it into arbitrary values representable in the programming language.
- An interface to external test runners that takes tests built on top of the strategy library and runs them using Conjecture (in Python this mostly just consists of exposing a function that the test runners can pick up, but in the Java Prototype this is more involved and ends up having to interact with some interesting JUnit specific features.
Conjecture is essentially the interesting part of Hypothesis’s implementation and is what supports most of its functionality: Generation, shrinking, and serialization are all built into the core engine, so implementations of strategies do not require any awareness of these features to be correct. They simply repeatedly ask the Conjecture engine for blocks of bytes, which it duly provides, and they return the desired result.
If you want to know more about this, I have previously written How Hypothesis Works, which provides a bit more detail about Conjecture and how Hypothesis is built on top of it.
What prior art is it based on?
I’ve done a fair bit of general reading of the literature in the course of working on Hypothesis.
The two main papers on which Hypothesis is based are:
- QuickCheck: a lightweight tool for random testing of Haskell programs essentially started the entire field of property-based testing. Hypothesis began life as a QuickCheck implementation, and its user facing API continues to be heavily based on QuickCheck, even though the implementation has diverged very heavily from it.
- EXPLODE: a lightweight, general system for finding serious storage system errors provided the key idea on which the Conjecture engine is based - instead of doing static data generation separate from the tests, provide tests with an interactive primitive from which they can draw data.
Additionally, the following are major design inspirations in the Conjecture engine, although their designs are not currently used directly:
- American Fuzzy Lop is an excellent security-oriented fuzzer, although one without much academic connections. I’ve learned a fair bit about the design of fuzzers from it. For a variety of pragmatic reasons I don’t currently use its most important innovation (branch coverage metrics as a tool for corpus discovery), but I’ve successfully prototyped implementations of that on top of Hypothesis which work pretty well.
- Swarm Testing drove a lot of the early designs of Hypothesis’s data generation. It is currently not explicitly present in the Conjecture implementation, but some of what Conjecture does to induce deliberate correlations in data is inspired by it.
What are some interesting research directions?
I have a large number of possible directions that my work on Hypothesis could be taken.
None of these are necessarily a thing that would be the focus of a PhD - in doing a PhD I would almost certainly focus on a more specific research question that might include some or all of them. These are just areas that I am interested in exploring which I think might form an interesting starting point, and whatever focus I actually end up with will likely be more carefully tailored in discussion with my potential supervisors.
One thing that’s also worth considering: Most of these research directions are ones that would result in improvements to Hypothesis without changing its public interface. This results in a great practical advantage to performing the research because of the relatively large (and ever-growing) corpus of open source projects which are already using Hypothesis - many of these changes could at least partly be validated by just running peoples’ existing tests and seeing if any new and interesting bugs are found!
Without further ado, here are some of what I think are the most interesting directions to go next.
More structured byte streams
My current immediate research focus on Hypothesis is to replace the core Conjecture primitive with a more structured one that bears a stronger resemblance to its origins in EXPLODE. This is designed to address a number of practical problems that Hypothesis users currently experience (mostly performance related), but it also opens up a number of other novel abstractions that can be built on top of the core engine.
The idea is to pare down the interface so that when calling in to Conjecture you simply draw a single byte, specifying a range of possible valid bytes. This gives Conjecture much more fine-grained information to work with, which opens up a number of additional features and abstractions that can be built on top of it.
From this primitive you can then rebuild arbitrary weighted samplers that shrink correctly (using a variation of the Alias Method), and arbitrary grammars (probably using Boltzmann Samplers or similar).
This will provide a much more thorough basis for high quality data generation than the current rather ad hoc method of specifying byte streams.
This is perhaps more engineering than research, but I think it would at the bare minimum make any paper I wrote about the core approach of Hypothesis significantly more compelling, and it contains a number of interesting applications of the theory.
Glass box testing
Currently Conjecture treats the tests it calls as a black box and does not get much information about what the tests it executes are actually doing.
One obvious thing to do which brings in some more ideas from e.g. American Fuzzy Lop is to use more coverage information, but so far I haven’t had much success with making my prototypes of this idea suitable for real world use. The primary reason for this so far has been that all of the techniques I’ve found have worked well when tests are allowed to run for minutes or hours, but the current design focus of Hypothesis assumes tests have seconds to run at most, which limits the utility of these methods and means they haven’t been a priority so far.
But in principle this should be an extremely profitable line of attack, even with that limitation, and I would like to explore it further.
The main idea would be to add a notion of “tags” to the core Conjecture engine which could be used to guide the search. Coverage would be one source of tags, but others are possible. For example, my previous work on Schroedinteger implements what is essentially a form of lightweight Concolic testing that would be another possibly interesting source of information to use.
Exactly how much of this is original research and how much is just applications of existing research is yet to be determined, but I think it very likely that at the very least figuring out how to make use of this sort of information in sharply bounded time is likely to bear interesting fruit. The opportunity to see how Concolic testing behaves in the wild is also likely to result in a number of additional questions.
Making the Conjecture engine smarter
A thing I’ve looked into in the past is the possible use of grammar inference to improve shrinking and data generation.
At the time the obstacle I ran into was that the algorithm I was using - an optimized variation of L* search - did not get good performance in practice on the problems I tried it on.
Synthesizing Program Input Grammars promises to lift this restriction by providing much better grammar inference in practical scenarios that are quite closely related to this problem domain, so I would like to revisit this and see if it can prove useful.
There are likely a number of other ways that the Conjecture engine can probe the state of the system under test to determine interesting potential behaviours, especially in combination with glass box testing features.
I think there are a lot of potentially interesting research directions in here - especially if this is combined with the glass box testing. Given that I haven’t even been able to make this perform acceptably in the past, the first one would be to see if I can!
This will also require a fair bit of practical experimentation to see what works well at actually finding bugs and what doesn’t. This is one area in particular where a corpus of open source projects tested with Hypothesis will be extremely helpful.
Other testing abstractions
Despite Hypothesis primarily being a library for property based testing, the core Conjecture engine actually has very little to do with property-based testing and is a more powerful low-level testing abstraction. It would be interesting to see how far that could be taken - the existing stateful/model-based testing is one partial step in that direction, but it could also potentially be used more directly for other things. e.g. in tandem with some of the above features it could be used for low-level fuzzing of binaries, or using it to drive thread scheduling.
The nice thing about the Conjecture separation is that because it is so self-contained, it can be used as the core building block on which other tools can be rebuilt and gain a lot of its major features for free.
I don’t currently have any concrete plans in this direction, but it seems likely there are some interesting possibilities here that will emerge after more review of the testing literature.
This is probably just engineering unless some particularly interesting application emerges, but I think the basic potential of the technology would probably give pretty good odds of such an application.
What should you do with this information?
It depends who you are.
- If I’m already talking to you because you’re a potential PhD supervisor, tell me what about this interests you and ask me lots of questions.
- If you’re a potential PhD supervisor who I’m not already talking to but you’d like me to, please let me know!
- If you’re somebody else, it’s rather up to you. Feel free to send me papers, questions, etc.
Whoever you are, if you found this document interesting I’d love to hear from you. Drop me an email at email@example.com.