Shedskin

gistfile1.txt

*** SHED SKIN Python-to-C++ Compiler ***
Copyright 2005-2013 Mark Dufour; License GNU GPL version 3 (See LICENSE)

infer.py: perform iterative type analysis

we combine two techniques from the literature, to analyze both parametric
polymorphism and data polymorphism adaptively. these techniques are agesen's
cartesian product algorithm [0] and plevyak's iterative flow analysis [1] (the data
polymorphic part). for details about these algorithms, see ole agesen's
excellent Phd thesis [2]. for details about the Shed Skin implementation, see mark
dufour's MsC thesis [3].

the cartesian product algorithm duplicates functions (or their graph
counterpart), based on the cartesian product of possible argument types,
whereas iterative flow analysis duplicates classes based on observed
imprecisions at assignment points. the two integers mentioned in the graph.py
description are used to keep track of duplicates along these dimensions (first
class duplicate nr, then function duplicate nr).

the combined technique scales reasonably well, but can explode in many cases.
there are many ways to improve this. some ideas:

-an iterative deepening approach, merging redundant duplicates after each
deepening 

-add and propagate filters across variables. e.g. 'a+1; a=b' implies
that a and b must be of a type that implements '__add__'.

a complementary but very practical approach to (greatly) improve scalability
would be to profile programs before compiling them, resulting in quite precise
(lower bound) type information. type inference can then be used to 'fill in the
gaps'.

iterative_dataflow_analysis(): (FORWARD PHASE) -propagate types along
constraint graph (propagate()) -all the while creating function duplicates
using the cartesian product algorithm(cpa()) -when creating a function
duplicate, fill in allocation points with correct type (ifa_seed_template())
(BACKWARD PHASE) -determine classes to be duplicated, according to found
imprecision points (ifa()) -from imprecision points, follow the constraint
graph (backwards) to find involved allocation points -duplicate classes, and
spread them over these allocation points (CLEANUP) -quit if no further
imprecision points (ifa() did not find anything) -otherwise, restore the
constraint graph to its original state and restart -all the while maintaining
types for each allocation point in gx.alloc_info

update: we now analyze programs incrementally, adding several functions and
redoing the full analysis each time. this seems to greatly help the CPA from
exploding early on.

[0] http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.30.8177

[1] http://www.plevyak.com/ifa-submit.pdf

[2] http://dl.acm.org/citation.cfm?id=237570

[3] http://mark.dufour.googlepages.com/shedskin.pdf