Summary:
This diff adds a constant to the set of widening thresholds if the
constant is compared to an abstract value in condition expressions.
Each abstract value has its own set of thresholds.
Reviewed By: mbouaziz
Differential Revision: D14147150
fbshipit-source-id: ca0db34d4
Summary: Record where each symbol in a polynomial is coming from: either a loop, function call or a modeled call.
Reviewed By: mbouaziz
Differential Revision: D14047420
fbshipit-source-id: 56d0bd926
Summary: It keeps alias of simple plus/minus arithmetic in order to pruning the value of "++i" expression.
Reviewed By: mbouaziz
Differential Revision: D14080230
fbshipit-source-id: d3af32a32
Summary:
- There is no need to use AI to compute a dot product: let's just fold over all nodes, but still do it in order (using the WTO) to report at the right place
- The previous version was computing a dot product on nodes for each node, which was quadratic, the new version is linear
- Report only once, the first time the threshold is reached (if in a loop, report at the loop head)
Reviewed By: ddino
Differential Revision: D14028171
fbshipit-source-id: b4a840c6e
Summary:
For abstract values representing one concrete value, create only one symbol instead of two.
Still create two symbols (lb, ub) for abstract values representing multiple concrete values (like array cells).
As a consequence, comparisons of symbolic values are more precise (we can even prove equality). I expect to remove a bunch of FPs.
Another consequence is the disappearance of `.lb` and `.ub` in many reports.
Reviewed By: skcho
Differential Revision: D13072084
fbshipit-source-id: 9bc0b9881
Summary:
It materializes symbolic values of function parameters on-demand. The on-demand materialization is triggered when finding a value from an abstract memory and joining/widening abstract memories.
Depends on D13294630
Main idea:
* Symbolic values are on-demand-ly generated by a symbol path and its type
* In order to avoid infinite generation of symbolic values, symbol paths are canonicalized by structure types and field names (which means they are abstracted to the same value). For example, in a linked list, a symbolic value `x->next->next` is canonicalized to `x->next` when the structures (`*x` and `*x->next`) have the same structure type and the same field name (`next`).
Changes from the previous code:
* `Symbol.t` does not include `id` and `pname` for distinguishing symbols. Now, all symbols are compared by `path:SymbolPath.partial` and `bound_end`.
* `SymbolTable` is no longer used, which was used for generating symbolic values with new `id`s.
Reviewed By: mbouaziz
Differential Revision: D13294635
fbshipit-source-id: fa422f084
Summary:
When initialising a variable via semi-exotic means, the frontend loses
the information that the variable was initialised. For instance, it
translates:
```
struct Foo { int i; };
...
Foo s = {42};
```
as:
```
s.i := 42
```
This can be confusing for backends that need to know that `s` actually
got initialised, eg pulse.
The solution implemented here is to insert of dummy call to
`__variable_initiazition`:
```
__variable_initialization(&s);
s.i := 42;
```
Then checkers can recognise that this builtin function does what its
name says.
Reviewed By: mbouaziz
Differential Revision: D12887122
fbshipit-source-id: 6e7214438
Summary: This diff changes pp of binary operation condition in order to avoid a `make test` failure. For the same `uint64_t` type, it is translated to `unsigned long long` in 64bit mac, but `unsigned long` in 64bit linux, which made a `make test` failure.
Reviewed By: mbouaziz
Differential Revision: D10459466
fbshipit-source-id: 449ab548e
Summary:
I realized that control variable analysis was broken when we had multiple back-edges for the same loop. This is often the case when we have a switch statement combined with continue in a loop (see `test_switch` in `switch_continue.c`) or when we have disjunctive guards in do-while loops.
This diff fixes that by
- defining a loop by its loophead (the target of its backedges) rather than its back-edges. Then it converts back-edge list to a map from loop_head to sources of the loop's back-edges.
- collecting multiple guard nodes that come from potentially multiple exit nodes per loop head
In addition, it also removes the wrong assumption that an exit node belongs to a single loop head.
Reviewed By: mbouaziz
Differential Revision: D8398061
fbshipit-source-id: abaf288
Summary:
It's useful to test that the bucket a given error is classified as doesn't
change over time without notice.
This records the bucket for *all* the tests, even though some never produce a
bucket. This is to be on the safe size instead of risking to forget adding the
bucket information when the test changes, or when copy/pasting from a test that
doesn't have buckets to one that does.
The implementation is pretty crude: it greps the beginning of the qualifier
string for a `[bucket]`.
Reviewed By: mbouaziz
Differential Revision: D8236393
fbshipit-source-id: b3b1eb9
Summary:
It improves the precision of widening operations of interval:
upper_bound_widen (min(n, s), s) = s
lower_bound_widen (max(n, s), s) = s
Reviewed By: mbouaziz
Differential Revision: D8038941
fbshipit-source-id: 61b10cb
Summary:
Before we were computing the size of an abstract state (`range`) using the `NonNegativeBound` domain but it wasn't able to express product of symbolic values.
This diff introduces a domain for that.
The range of an interval is still computed in `NonNegativeBound` but then the product is done in `TopLiftedPolynomial` so all costs end up being of that type.
The //symbols// of a polynomial are `NonNegativeBound` (so the polynomial only represent non-negative values, perfect for a cost), which handles substitution correctly, i.e. it gives zero instead of negative values.
Reviewed By: ddino
Differential Revision: D7397229
fbshipit-source-id: 6868bb7
Summary: We were wrongly using the underapproximation of `min` rather than the overapproximation
Reviewed By: ddino
Differential Revision: D7844267
fbshipit-source-id: c9d9247
Summary:
We want instr-granular invariant maps so let's use the OneInstrPerNode CFG in the AI analyzers.
This requires specializing the TransferFunctions.
Keep using the normal CFG where we only need node-granular informations.
Depends on D7587241
Depends on D7608526
Reviewed By: sblackshear
Differential Revision: D7618320
fbshipit-source-id: 73918f0
Dino Distefano
Mehdi Bouaziz
Sungkeun Cho
Ezgi Çiçek
Jules Villard
Martino Luca