cognitive psychology. PhD 
Looking for any kind of related work, especially in static analysis or graph algorithms.
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Anyone familiar with program representations that are "dataflow but with mutation"? Eg here I distinguish between flows before and after a mutation: `other` flows to `set` and `set` flows to `orig_len`, but `other` does not flow to `orig_len`.pic.twitter.com/F2fTJIAqYt
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Some cool properties: * A node's ancestors = backward slice (and descendants = forward). * Weakly connected components are independent computations, so they could be e.g. parallelized or trivially factored. * Any topological sort is a valid reordering of the program.
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I don’t have an answer but was just wondering about this
https://twitter.com/Westoncb/status/1462211544641523719 …you almost need a sense of humor to do it, but you could say normal languages are a subset of generalized dataflow languages where the graph structure just shifts constantly during runtime maaybe it's not too crazy to maintain a relevant subgraph to implement
? https://twitter.com/Westoncb/status/1459683578569052160 …Show this thread -
Flowistry / slicing seems pretty close to what you want! Although not sure how you would assign weight to influence.https://github.com/willcrichton/flowistry …
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Might not be exactly what you're asking, but: PEGs are like dataflow graphs, but faithfully represent loops, exceptions, and mutation https://www.jameskoppel.com/files/papers/yogo.pdf … https://www.cs.cornell.edu/~ross/publications/eqsat/ …
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Yes this is definitely what I'm looking for! PEG seems like representing imperative code in a purely functional form via an explicit memory object. The diff from my graph is that mutations are accumulated into a global memory, rather than tracking on the variable-level.
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I have seen some SSA-like approaches. I think most added the CFG node id to the analysis and have a 'next' relation for the nodes, like 'A Datalog Model of Must-Alias Analysis'
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