cognitive psychology. PhD 
for what language?
Are there any good frameworks/papers/etc. for source-to-source optimizing-compiler passes? e.g. copy propagation, dead code elimination but for ASTs instead of IRs.
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Working in Python, but any Algol-derivative would be good!
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This one, in the context of Scheme, might provide some inspiration (though it’s tailored toward effect-free code). Definitely ideas in here to steal: Waddell and Dybvig. Fast and effective procedure inlining. http://www.cs.indiana.edu/~dyb/pubs/inlining.pdf … Follow the citation trail too.
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Polyglot (a java source-to-source compiler) has some limited support for this sort of thing. cf:https://github.com/polyglot-compiler/polyglot/blob/master/src/polyglot/visit/CopyPropagator.java …
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Ah great! Seems like they build a control-flow graph on the AST, then do data-flow analysis on the CFG with a transfer function defined over AST nodes (https://github.com/polyglot-compiler/polyglot/blob/master/src/polyglot/visit/CopyPropagator.java#L379-L463 …). Then they transform the AST with the output of dataflow using visitors (https://github.com/polyglot-compiler/polyglot/blob/master/src/polyglot/visit/CopyPropagator.java#L484-L519 …).
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