Migrating gradual types
By John Peter Campora III, Sheng Chen, Martin Erwig, Eric Walkingshaw. 2017
22 citations recorded by the source corpus.
Gradual typing allows programs to enjoy the benefits of both static typing and dynamic typing. While it is often desirable to migrate a program from more dynamically-typed to more statically-typed or vice versa, gradual typing itself does not provide a way to facilitate this migration. This places the burden on programmers who have to manually add or remove type annotations. Besides the general challenge of adding type annotations to dynamically typed code, there are subtle interactions between these annotations in gradually typed code that exacerbate the situation. For example, to migrate a program to be as static as possible, in general, all possible combinations of adding or removing type annotations from parameters must be tried out and compared. In this paper, we address this problem by developing migrational typing, which efficiently types all possible ways of adding or removing type annotations from a gradually typed program. The typing result supports automatically migrating a program to be as static as possible, or introducing the least number of dynamic types necessary to remove a type error. The approach can be extended to support user-defined criteria about which annotations to modify. We have implemented migrational typing and evaluated it on large programs. The results show that migrational typing scales linearly with the size of the program and takes only 2–4 times longer than plain gradual typing.
Subfields: Artificial Intelligence, Computer Science Applications, Information Systems, Program Analysis & Verification, Software Engineering
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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].”