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Understand augmented assignments #192

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Originally reported by: Claudiu Popa (BitBucket: PCManticore, GitHub: @PCManticore)


Closely related to #115.

We should try to understand the various forms of augmented assignments. The simplest case, for instance, causes false positives with numpy.core (which is fixed in astroid through a transform, not the proper fix though)

#!python

a = [...]
a += [...]
a += [...]
a # infer with all mutations

Since I gave numpy.core as an example, the all attribute is computed by adding the all values of subimports into this attribute through augmented assignment.


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