These queries have no results on our test cases in the repo, but
`ambiguousSuccessors` has results on any large C++ code base, and
`unexplainedLoop` has results on Windows builds of ChakraCore.
With this change, the `IRDataflowTestCommon.qll` and
`DataflowTestCommon.qll` files use the same definitions of sources and
sinks. Since the IR data flow library is meant to be compatible with the
AST data flow library, this is what we ought to be testing.
Two alerts change but not necessarily for the right reasons.
As we prepare to clarify how conversions are treated, we don't want a
`sink(...)` declaration where it's non-obvious which conversions are
applied to arguments.
The CFG construction code previously contained half of an approximation
of which address expressions are constant. Now this this property is
properly modelled by `Expr.isConstant`, we can remove this code.
This fixes most discrepancies between the QL-based CFG and the
extractor-based CFG on Wireshark.
Before this change, `Expr.isConstant` only was only true for those
constant expressions that could be represented as QL values: numbers,
Booleans, and string literals. It was not true for string literals
converted from arrays to pointers, and it was not true for addresses of
variables with static lifetime.
The concept of a "constant expression" varies between C and C++ and
between versions of the standard, but they all include addresses of data
with static lifetime. These are modelled by the new library
`AddressConstantExpression.qll`, which is based on the code in
`EscapesTree.qll` and modified for its new purpose.
I've tested the change for performance on Wireshark and for correctness
with the included tests. I've also checked on Wireshark that all static
initializers in C files are considered constant, which was not the case
before.
This test was intended to catch regressions in the CFG, but it looks
like it's just catching insignificant extractor changes. The test has
started failing after some recent extractor changes, but I have no way
to pinpoint the failure and understand whether it's a problem or not, so
I think it's better to delete this test.
The remaining tests check whether the QL-based CFG generates the same
graph as the extractor-based CFG. Furthermore, the `successor-tests`
check that the extractor-based CFG works as intended.
This reduces the number of bounds computed, and will simplify use of the
library. The resulting locations in the tests may be slightly strange,
because the example `Instruction` for a `ValueNumber` is the first
appearing in the IR, regardless of source order, and may not be the most
closely related `Instruction` to the bounded value. I think that's worth
doing for the performance and usability benefits.
This implements calculation of the control-flow graph in QL. The new
code is not enabled yet as we'll need more extractor changes first.
The `SyntheticDestructorCalls.qll` file is a temporary solution that can
be removed when the extractor produces this information directly.
The existing unreachable IR removal code only retargeted an infeasible edge to an `Unreached` instruction if the successor of the edge was an unreachable block. This is too conservative, because it doesn't remove an infeasible edge that targets a block that is still reachable via other paths. The trivial example of this is `do { } while (false);`, where the back edge is infeasible, but the body block is still reachable from the loop entry.
This change retargets all infeasible edges to `Unreached` instructions, regardless of the reachability of the successor block.
This change removes any IR instructions that can be statically proven unreachable. To detect unreachable IR, we first run a simple constant value analysis on the IR. Then, any `ConditionalBranch` with a constant condition has the appropriate edge marked as "infeasible". We define a class `ReachableBlock` as any `IRBlock` with a path from the entry block of the function. SSA construction has been modified to operate only on `ReachableBlock` and `ReachableInstruction`, which ensures that only reachable IR gets translated into SSA form. For any infeasible edge where its predecessor block is reachable, we replace the original target of the branch with an `Unreached` instruction, which lets us preserve the invariant that all `ConditionalBranch` instructions have both a true and a false edge, and allows guard inference to still work.
The changes to `SSAConstruction.qll` are not as scary as they look. They are almost entirely a mechanical replacement of `OldIR::IRBlock` with `OldBlock`, which is just an alias for `ReachableBlock`.
Note that the `constant_func.ql` test can determine that the two new test functions always return 0.
Removing unreachable code helps get rid of some common FPs in IR-based dataflow analysis, especially for constructs like `while(true)`.
This change moves the simple constant analysis that was used by the const_func test into a pyrameterized module for use on any stage of the IR. This will be used to detect unreachable code.
This sort of fixes one FP and causes a new FN, but for the wrong reasons. The IR dataflow is tracking the reference itself, rather than the referred-to object. Once we can better model indirections, we can make this work correctly.
This change is still the right thing to do, because it ensures that the dataflow is looking at actual expression being computed by the instruction.
Made `Node::getType()`, `Node::asParameter()`, and `Node::asUninitialized()` operate directly on the IR. This actually fixed several diffs compared to the AST dataflow, because `getType()` wasn't holding for nodes that weren't `Exprs`.
Made `Uninitialized` a `VariableInstruction`. This makes it consistent with `InitializeParameter`.
This fixes a subtle bug in the construction of aliased SSA. `getResultMemoryAccess` was failing to return a `MemoryAccess` for a store to a variable whose address escaped. This is because no `VirtualIRVariable` was being created for such variables. The code was assuming that any access to such a variable would be via `UnknownMemoryAccess`. The result is that accesses to such variables were not being modeled in SSA at all.
Instead, the way to handle this is to have a `VariableMemoryAccess` even when the variable being accessed has escaped, and to have `VariableMemoryAccess::getVirtualVariable()` return the `UnknownVirtualVariable` for escaped variables. In the future, this will also let us be less conservative about inserting `Chi` nodes, because we'll be able to determine that there's an exact overlap between two accesses to the same escaped variable in some cases.
