In my last last post, I argued that property-based testing and fuzzing are essentially the same practice, or at least share a lot of commonality. In this followup post, I want to explore that idea a bit more: I’ll first detour into some of my frustrations and hesitations around typical property-based testing tools, and then propose a hypothetical UX to resolve these concerns, which takes heavy inspiration from modern fuzzing tools, specifically the AFL and Google’s OSS-Fuzz.
Preface: Desiderata for a Test Suite
A large software project, in general, may have multiple test suites, which are run at different frequencies or different levels of developer interactivity or involvement, with different goals.
In most projects I’ve worked on, however, the most common test suite pattern is a continuous integration suite that runs on every change (proposed or merged), and a subset of which is run interactively by developers as they develop to validate their work.
Such a test suite is, in many ways, first and foremost a regression suite. Developers may write in a test-first “TDD” style or write tests for compliance with an external spec (implicit or explicit), but once the code is written, the most important function of the test suite is to document (in an executable+verifiable way) the critical properties of the code which must be preserved by future changes. In a large codebase under active development, this regression testing gives future developers the ability to make changes with some confidence that they have not broken everything.
What properties do we want from such a test suite? There are a number, but I want to highlight two important ones here. Specifically, a test suite should be fast and reproducible.
Speed of execution is important, because fast tests enable rapid feedback for developers. In a large codebase, adding a feature or fixing a bug is often not hard, but adding a feature or fixing a bug without breaking any existing behavior is the hard part. Since tests provide your first line of feedback for that property, they often become a critical rate-limiter on your development feedback cycle. Slow tests are frustrating to developers, and force them to resort to manual or ad-hoc testing during development, instead of relying on the test suite.
Test results must also be reproducible in order to reliably provide the desired confidence for developers. By this, I mean that once a test succeeds, it should reliably continue succeeding until something meaningful changes, and if a test fails, it should be easy to reliably reproduce the failure.
Both properties are essential for a CI regression-testing suite to be viable. Once tests pass and are merged, they must not fail randomly; otherwise, the compounding effects of random failures makes it impossible to ever keep the build “green”, ruining the feedback value of CI. On the flip side, if a test fails during development or in a pre-merge push, it must be easy to reproduce that failure to allow a developer to debug and fix the issue quickly and with confidence that the issue has been resolved.
Where Property-Based Fuzzing Falls Short
Traditional property-based fuzzing fails at both these criteria, which I suspect is one of the reasons it has gotten less traction than it might have.
Because traditional property-based testing generates random inputs at runtime, you generally need a relatively large number of runs to get decent test coverage (most systems I’ve seen default to something like 100). While it’s pretty easy to execute 100 runs of a toy example within human timescales, large systems have a bad habit of ending up with test cases that have many expensive dependencies, I/O requirements, or other slowdowns, and a 10x slowdown vs “executing on 10 hand-chosen examples” is a frustrating price to pay.
Because property-based testing selects test cases at random, it is prone (by design!) to nondeterminism, which spoils the above-described reproducibility properties. Authors and adherents of property-based test suites often describe this as a feature, rightly pointing out that it gives every test run the opportunity to discover novel bugs, continually improving your coverage. However, this nondeterminism is untenable for a CI suite that must run on every commit for every developer; CI’s job is to prove the absence of (certain) regressions, not absolute correctness.
Many property-based test suites attempt to (optionally) recover determinism by supporting a deterministic random-number generator. This approach helps a lot, but can still leave you with a brittle test suite, where small changes to your generation strategy or test cases cause cascading changes to the set of considered test cases. A developer who adds a branch to a data-generation strategy to support their new feature may find bizarre cascading failures if doing so changes the examples seen by other tests.
The Workflow I Want
With that motivation as background, I want to lay out the workflow I believe I want out of property-based testing. Based on my judgment and experience, I think a design of this shape would effectively bridge the gap between many of the present tools, and the needs of a large-scale regression suite. This description is inspired by a combination of attempting to address the above concerns, and by techniques already widespread in the fuzzing space, notably the afl and oss-fuzz tools.
I want to write tests in a property-based style, by writing functions that must pass for all inputs of some type. However, I also want to commit a list of test examples to my repository, ideally in a textual, human-readable format (e.g. as source code literals or JSON) for ease of review and merging.
When run in a default mode (e.g.
make test or your CI entrypoint),
the tool should only run those examples. By running a fixed,
small, set of examples, we recover the speed and reliably we desired
So far, what I’ve described is just a slightly-heavyweight version of “table-driven testing”, and doesn’t have the generative nature that typically defines property-based testing. And while I’m an enormous proponent of table-driven tests, it seems clear that automatic test generation has something to offer above and beyond such tests.
To bridge the gap, I desire that the aforementioned lists of inputs
should be, in most cases, automatically generated by the
property-based framework, before being commited. After writing a test,
I might run
proptest generate, which will begin a traditional
property-based-testing generation-and-minimization process, with the
difference that any failing tests, and a sample of other tests, will
be automatically written out to the “examples” file for review and
potential committing by the developer.
Coverage-guided generation and feedback
proptest generate decide which cases to save, and how will
it decide when it has enough tests? Here we take inspiration from AFL,
and use coverage-guided exploration to guide test case generation, and
also to determine when to stop. The generator stops generating tests
cases after some combination of
- A configurable timeout
- When coverage stops improving
- When coverage is sufficiently high in an absolute sense
(Upon completion, it can also print statistics about this exploration process and the coverage reached, to inform the quality of the test case and/or generator.)
Once it has stopped (or perhaps concurrently with generation), the
tool will use a coverage-driven minimization process (ala
afl-cmin) to find a near-minimal list of test cases that
exercises approximately the same set of coverage as the total
corpus. In addition, any tests that fail are automatically preserved
(perhaps up to uniqueness, as judged by the execution trace and
The end result of such a process should be a small corpus of tests that execute quickly, while still exercising about as much of the code under test as possible, and explicitly checking test cases that once failed, to prevent regressions of known bugs.
Finally, the tool will also have an “unbounded fuzzing” mode, in which it uses the coverage-guided exploration engine to continually generate and explore new test cases, reporting any failures. As a developer, I can run this periodically in spare cycle on a laptop or server somewhere, or, ideally, in a cloud executor designed for this purpose (ala oss-fuzz).
Importantly, since this search process will run as a separate job, it will discover bugs asynchronously to my main development process, and not block PRs or break trunk. I can then triage and address these bugs on whatever timeline I see fit, making my own judgment of severity or urgency.
Additionally, any failures found in this mode will generate output
that includes a formatted example that I can copy-paste directly into
the committed examples list and commit. This makes local reproduction
easy, since a
make test will now run this test case and demonstrate
the failure for me, and also ensures that this bug is never regressed,
by directly testing this test case on every future CI run, once I do
land a fix.
Given such a system, the tool can also implement a “traditional” property-testing mode, which ignores the hardcoded corpus and runs each test for a fixed duration or number of runs. We can also mark a given test as “never autogenerate input”, and recover classic table-driven testing. This flexibility should allow for workflows that scale from small tests on trivial codebases to very large, complex test setups, all within a uniform framework.
Similarly, even for tests that already have committed examples, if you change the code under test, you should be able to re-run the generator, seeded with the existing examples, to re-start the exploration process on the new code, and produce a new minimized high-coverage corpus. Obviously this process shouldn’t be necessary – if the properties haven’t changed, the old examples should still be valid – but it’s important to allow us to evolve our corpus and test data with an evolving implementation.
I’m fairly confident I’d be much more willing to adopt property testing frameworks in my own code if they behaved more like the (notional) system described above.
I have pretty good confidence in many parts of the above workflow, because I’ve largely just described tools that exist in the fuzzing world, and so this is not completely fabricated from thin air. That said, I think there are still some open questions about how such workflows would work as a primary means of testing code; If you see concerns or have tried something similar, I’d be very curious to hear experience reports.
Finally, I want to once again include a shout-out to Hypothesis; Although it doesn’t implement precisely the workflows I’ve described above, it contains essentially all of the pieces you would need to build them, including a persistent example database of failed examples, the ability to specify examples manually, and coverage-guided exploration.
I don’t write much Python these days, but if I did I’d definitely explore building out a variant of this workflow on top of Hypothesis.
This post is also available in translation into Russian