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Additional proofs and code for "Data-flow analyses as effects and graded monads"

Authors: Andrej Ivaskovic; Alan Mycroft; Dominic Orchard;

Additional proofs and code for "Data-flow analyses as effects and graded monads"

Abstract

This deposit provides code and additional proofs associated to the paper "Data-flow analyses as effects and graded monads" appearing at FSCD 2020 (5th International Conference On Formal Structures for Computation and Deduction). extra-proofs.pdf provides additional proofs not included in the appendix of the published paper for space reasons. GradedMonad.agda provides further mechanised proofs, referred to from extra-proofs.pdf dataflow-effects-as-grades-fscd2020.zip provides the source code corresponding to Section 4.4 and Appendix B The code is hosted on GitHub as well: https://github.com/dorchard/dataflow-effects-as-grades This .zip corresponds to this release https://github.com/dorchard/dataflow-effects-as-grades/releases/tag/fscd2020 Unzip and see README.md for details on how to build and interact with this code

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Keywords

correctness, effect systems, graded monads, data-flow analysis

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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