# Symbolic propagation

Symbolic propagation allows Semgrep to perform matching modulo variable assignments. Consider the following Python code:

`import pandas`

def test1():

# ruleid: test

pandas.DataFrame(x).index.set_value(a, b, c)

def test2():

df = pandas.DataFrame(x)

ix = df.index

# ruleid: test

ix.set_value(a, b, c)

If we tried to match the pattern `pandas.DataFrame(...).index.set_value(...)`

against the above code, Semgrep would normally match `test1`

but not `test2`

. It does not match `test2`

because there are intermediate assignments, and Semgrep does not know that `ix`

is equals to `df.index`

or that `df`

is equals to `pandas.DataFrame(x)`

. If we wanted Semgrep to match such code, we had to be explicit about it.

Symbolic propagation is a generalization of constant propagation that addresses this limitation. It enables Semgrep to perform matching modulo variable assignments. Thus, Semgrep is then able to match both `test1`

and `test2`

with the same simple pattern. This feature needs to be enabled explicitly via rule `options:`

by setting `symbolic_propagation: true`

.

## Limitations of symbolic propagation

Currently, symbolic propagation does not cross branching boundaries, such as `if`

clauses or loops. Consider the following Python code, adapted from the example shown above:

`import pandas`

def test1():

# ruleid: test

pandas.DataFrame(x).index.set_value(a, b, c)

def test2():

df = pandas.DataFrame(x)

if (x < 5):

pass

ix = df.index

# ruleid: test

ix.set_value(a, b, c)

In this case, even if `symbolic_propagation: true`

is used, Semgrep does not match `test2`

, because the assignment of `df`

to `pandas.DataFrame(x)`

is not propagated over the conditional to the final two lines.