The new-dataflow ImportResolution module only used
semmle.python.essa.SsaDefinitions for the 5-line helper predicate
SsaSource::init_module_submodule_defn. Inline it locally and drop the
dependency on legacy SsaDefinitions. This is the only remaining direct
import of semmle.python.essa.* in the new dataflow stack, so dropping
it makes the layering cleaner.
Semantic noop on the current SSA: SsaSourceVariable.getName() and
GlobalVariable.getId() both project the same DB column
(variable(_,_,result)), and the old call's 'init.getEntryNode() = f'
join was just constraining init = package via Scope.getEntryNode()'s
functional uniqueness. RA dump of accesses.ql confirms only the
expected predicate-rename shuffle; all 70 dataflow + ApiGraphs library
tests pass.
This factors out commit 8cab5a20f2 from the larger shared-CFG
migration #21925.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
The internal predicates that identify `@staticmethod`, `@classmethod` and
`@property` decorators previously required the decorator's `NameNode` to
satisfy `isGlobal()` (i.e. no SSA def reaches the decorator's name use).
That filter was correct but unnecessarily indirect: these three names
are builtins, and even when a class body redefines one, the class body
has not started executing at the decorator position, so Python uses the
builtin.
Match the decorator's AST `Name` directly instead, dropping the CFG/SSA
detour. The slight semantic change — `isGlobal()` would have rejected
module-level shadowing of these builtins — is negligible in practice
and explicitly documented in the change note.
`hasContextmanagerDecorator` and `hasOverloadDecorator` keep the
`NameNode.isGlobal()` check because their target names (`contextmanager`,
`overload`) are imported, not builtin, and local shadowing is a real
concern.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
This one is potentially a bit iffy -- it checks for a very powerful
property (that implies many of the other queries), but as the test
results show, it can produce false positives when there is in fact no
problem. We may want to get rid of it entirely, if it becomes too noisy.
This looks for nodes annotated with `t[never]` in the test that are
reachable in the CFG. This should not happen (it messes with various
queries, e.g. the "mixed returns" query), but the test shows that in a
few particular cases (involving the `match` statement where all cases
contain `return`s), we _do_ have reachable nodes that shouldn't be.
This one demonstrates a bug in the current CFG. In a dictionary
comprehension `{k: v for k, v in d.items()}`, we evaluate the value
before the key, which is incorrect. (A fix for this bug has been
implemented in a separate PR.)
These use the annotated, self-verifying test files to check various
consistency requirements.
Some of these may be expressing the same thing in different ways, but
it's fairly cheap to keep them around, so I have not attempted to
produce a minimal set of queries for this.
These tests consist of various Python constructions (hopefully a
somewhat comprehensive set) with specific timestamp annotations
scattered throughout. When the tests are run using the Python 3
interpreter, these annotations are checked and compared to the "current
timestamp" to see that they are in agreement. This is what makes the
tests "self-validating".
There are a few different kinds of annotations: the basic `t[4]` style
(meaning this is executed at timestamp 4), the `t[dead(4)]` variant
(meaning this _would_ happen at timestamp 4, but it is in a dead
branch), and `t[never]` (meaning this is never executed at all).
In addition to this, there is a query, MissingAnnotations, which checks
whether we have applied these annotations maximally. Many expression
nodes are not actually annotatable, so there is a sizeable list of
excluded nodes for that query.
We won't be able to run these tests until Python 3.15 is actually out
(and our CI is using it), so it seemed easiest to just put them in their
own test directory.
