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How is PyPy Tested?

How is PyPy Tested?

In this post I want to give an overview of how the PyPy project does and thinks about testing. PyPy takes testing quite seriously and has done some from the start of the project. Here I want to present the different styles of tests that PyPy has, when we use them and how I think about them.


To make the blog post self-contained, I am going to start with a small overview about PyPy's architecture. If you already know what PyPy is and how it works, you can skip this section.

PyPy means "Python in Python". It is an alternative implementation of the Python language. Usually, when we speak of "Python", we can mean two different things. On the one hand it means "Python as an abstract programming language". On the other hand, the main implementation of that language is also often called "Python". To more clearly distinguish the two, the implementation is often also called "CPython", because it is an interpreter implemented in C code.

Now we can make the statement "PyPy is Python in Python" more precise: PyPy is an interpreter for Python 3.9, implemented in RPython. RPython ("Restricted Python") is a subset of Python 2, which is statically typed (using type inference, not type annotations) and can be compiled to C code. That means we can take our Python 3.9 interpreter, and compile it into a C binary that can run Python 3.9 code. The final binary behaves pretty similarly to CPython.

The main thing that makes PyPy interesting is that during the translation of our interpreter to C, a number of components are automatically inserted into the final binary. One component is a reasonably good garbage collector.

The more exciting component that is inserted into the binary is a just-in-time compiler. The insertion of this component is not fully automatic, instead it is guided by a small number of annotations in the source code of the interpreter. The effect of inserting this JIT compiler into the binary is that the resulting binary can run Python code significantly faster than CPython, in many cases. How this works is not important for the rest of the post, if you want to see an example of concretely doing that to a small interpreter you can look at this video.

PyPy Testing History

A few historical notes on the PyPy project and its relationship to testing: The PyPy project was started in 2004. At the time when the project was started, Extreme Programming and Agile Software Development were up and coming. On the methodology side, PyPy was heavily influenced by these, and started using Test-Driven Development and pair programming right from the start.

Also technologically, PyPy has been influential on testing in the Python world. Originally, PyPy had used the unittest testing framework, but pretty soon the developers got frustrated with it. Holger Krekel, one of the original developers who started PyPy, started the pytest testing framework soon afterwards.

Interpreter-Level Tests

So, how are tests for PyPy written, concretely? The tests for the interpreter are split into two different kinds, which we call "interpreter level tests" and "application level tests". The former are tests that can be used to test the objects and functions that are used in the implementation of the Python interpreter. Since the interpreter is written in Python 2, those tests are also written in Python 2, using pytest. They tend to be more on the unit test side of things. They are in files with the pattern test_*.py.

Here is an example that tests the implementation of integers (very slightly simplified):

class TestW_IntObject:

    def test_hash(self):
        w_x = W_IntObject(42)
        w_result = w_x.descr_hash(
        assert isinstance(w_result, W_IntObject)
        assert w_result.intval == 42

This test checks that if you take an object that represents integers in the Python language (using the class W_IntObject, a "wrapped integer object") with the value 42, computing the hash of that object returns another instance of the same class, also with the value 42.

These tests can be run on top of any Python 2 implementation, either CPython or PyPy. We can then test and debug the internals of the PyPy interpreter using familiar tools like indeed pytest and the Python debuggers. They can be run, because all the involved code like the tests and the class W_IntObject are just completely regular Python 2 classes that behave in the regular way when run on top of a Python interpreter.

In CPython, these tests don't really have an equivalent. They would correspond to tests that are written in C and that can test the logic of all the C functions of CPython that execute certain functionality, accessing the internals of C structs in the process. ¹

Application-Level Tests

There is also a second class of tests for the interpreter. Those are tests that don't run on the level of the implementation. Instead, they are executed by the PyPy Python interpreter, thus running on the level of the applications run by PyPy. Since the interpreter is running Python 3, the tests are also written in Python 3. They are stored in files with the pattern apptest_*.py and look like "regular" Python 3 tests. ²

Here's an example of how you could write a test equivalent to the one above:

def test_hash():
    assert hash(42) == 42

This style of test looks more "natural" and is the preferred one in cases where the test does not need to access the internals of the logic or the objects of the interpreter.

Application level tests can be run in two different ways. On the one hand, we can simply run them on CPython 3. This is very useful! Since we want PyPy to behave like CPython, running the tests that we write on CPython is useful to make sure that the tests themselves aren't wrong.

On the other hand, the main way to run these tests is on top of PyPy, itself running on top of a Python 2 implementation. This makes it possible to run the test without first bootstrapping PyPy to C. Since bootstrapping to C is a relatively slow operation (can take up to an hour) it is crucially important to be able to run tests without bootstrapping first. It also again makes it possible to debug crashes in the interpreter using the regular Python 2 debugger. Of course running tests in this way is unfortunately itself not super fast, given that they run on a stack of two different interpreters.

Application-level tests correspond quite closely to CPython's tests suite (which is using the unittest framework). Of course in CPython it is not possible to run the test suite without building the CPython binary using a C compiler. ³

So when do we write application-level tests, and when interpreter-level tests? Interpreter-level tests are necessary to test internal data structures that touch data and logic that is not directly exposed to the Python language. If that is not necessary, we try to write application-level tests. App-level tests are however by their nature always more on the integration test side of things. To be able to run the test_hash function above, many parts of PyPy need to work correctly, the parser, the bytecode compiler, the bytecode interpreter, the hash builtin, calling the __hash__ special method, etc, etc.

This observation is also true for CPython! One could argue that CPython has no unit tests at all, because in order to be able to even run the tests, most of Python needs to be in working order already, so all the tests are really implicitly integration tests.

The CPython Test Suite

We also use the CPython Test suite as a final check to see whether our interpreter correctly implements all the features of the Python language. In that sense it acts as some kind of compliance test suite that checks whether we implement the language correctly. The test suite is not perfect for this. Since it is written for CPython's purposes during its development, a lot of the tests check really specific CPython implementation details. Examples for these are tests that check that __del__ is called immediately after objects go out of scope (which only happens if you use reference counting as a garbage collection strategy, PyPy uses a different approach to garbage collection). Other examples are checking for exception error messages very explicitly. However, the CPython test suite has gotten a lot better in these regards over time, by adding support.gc_collect() calls to fix the former problem, and by marking some very specific tests with the @impl_detail decorator. Thanks to all the CPython developers who have worked on this!

In the process of re-implementing CPython's functionality and running CPython's tests suite, PyPy can often also be a good way to find bugs in CPython. While we think about the corner cases of some Python feature we occasionally find situations where CPython didn't get everything completely correct either, which we then report back.

Testing for Performance Regressions

All the tests we described so far are checking behaviour. But one of PyPy's important goals is to be a fast implementation not "just" a correct one. Some aspects of performance can be tested by regular unit tests, either application- or interpreter-level. In order to check whether some performance shortcut is taken in the interpreter, we sometimes can write tests that monkeypatch the slow default implementation to always error. Then, if the fast path is taken properly, that slow default implementation is never reached.

But we also have additional tests that test the correct interaction with the JIT explicitly. For that, we have a special style of test that checks that the JIT will produce the correct machine code for a small snippet of Python code. To make this kind of test somewhat more robust, we don't check the machine code directly, but instead the architecture independent intermediate representation that the JIT uses to produce machine code from.

As an example, here is a small test that loading the attribute of a constant global instance can be completely constant folded away:

def test_load_attr(self):
    src = '''
        class A(object):
        a = A()
        a.x = 1
        def main(n):
            i = 0
            while i < n:
                i = i + a.x
            return i
    log =, [1000])
    assert log.result == 1000
    loop, = log.loops_by_filename(self.filepath)
    assert loop.match("""
        i9 = int_lt(i5, i6)
        guard_true(i9, descr=...)
        i10 = int_add(i5, 1)
        jump(..., descr=...)

The string passed to the loop.match function is a string representation of the intermediate representation code that is generated for the while loop in the main function given in the source. The important part of that intermediate representation is that the i = i + a.x addition is optimized into an int_add(x, 1) operation. The second argument for the addition is the constant 1, because the JIT noted that the global a is a constant, and the attribute x of that instance is always 1. The test thus checks that this optimization still works.

Those tests are again more on the unit test side of things (and can thus unfortunately be a bit brittle sometimes and break). The integration test equivalent for performance is the PyPy Speed Center which tracks the performance of micro- and macro-benchmarks over time and lets us see when big performance regressions are happening. The speed center is not really an automatic test and does not produce pass/fail outcomes. Instead, it requires human judgement and intervention in order to interpret the performance changes. Having a real pass/fail mechanism is something that would be great to have but is probably quite tricky in practice.


This concludes my overview of some of the different styles of tests that we use to develop the PyPy Python interpreter.

There is a whole other set of tests for the development of the RPython language, the garbage collectors it provides as well as the code that does the automatic JIT insertion, maybe I'll cover these in a future post.


¹ CPython has the _testcapimodule.c and related modules, that are used to unit-test the C-API. However, these are still driven from Python tests using the unittest framework and wouldn't run without the Python interpreter already working.

² There is also a deprecated different way to write these tests, by putting them in the test_*.py files that interpreter level tests are using and then having a test class with the pattern class AppTest*. We haven't converted all of them to the new style yet, even though the old style is quite weird: since the test_*.py files are themselves parsed by Python 2, the tests methods in AppTest* classes need to be written in the subset of Python 3 syntax that is also valid Python 2 syntax, leading to a lot of confusion.

³ Nit-picky side-note: C interpreters are a thing! But not that widely used in practice, or only in very specific situations.