Principal Type Schemes for Gradual Programs

By Ronald Garcia, Matteo Cimini. 2015

64 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Program Analysis & Verification

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Citations in the corpus

Do Machine Learning Models Produce TypeScript Types that Type Check? (2023, methodology): “The earliest approach was a variation of unification-based type inference [47], and more recent work uses a wide range of techniques [9, 12, 21, 34, 35, 40].”
Migrating gradual types (2017, methodology): “Second, as Migeed & Palsberg (2019) use type checking from Garcia & Cimini (2015) while our approach uses type inference from Garcia & Cimini (2015) and it is well-known that type inference is often incomplete, their approach can lead to more precise program migrations than ours for certain…”
Dynamic type inference for gradual Hindley–Milner typing (2018, methodology): “…behavior of a program and suggest to substitute the dynamic type for such undecided type variables, which is basically the same idea as Henglein [1994] and Siek and Vachharajani [2008], although some refinement based on static and gradual type parameters [Garcia and Cimini 2015] is discussed.”
What is decidable about gradual types? (2019, methodology): “Later, Garcia and Cimini [2015] presented a different unification-based algorithm for a similar type system. Both Siek and Vachharajani [2008] and Garcia and Cimini [2015] proved correctness, while neither had a report on experiments. Those two papers and our paper use similar forms of consistency c”
Gradual typing: a new perspective (2019, background): “As discussed in the introduction, Siek and Vachharajani [2008a] showed how to do unificationbased inference in a gradually typed language.”