2 Search Results for "Yee, Ming-Ho"

Document
DOI: 10.4230/LIPIcs.ECOOP.2023.37
Do Machine Learning Models Produce TypeScript Types That Type Check?

Authors: Ming-Ho Yee and Arjun Guha

Published in: LIPIcs, Volume 263, 37th European Conference on Object-Oriented Programming (ECOOP 2023)

Abstract
Type migration is the process of adding types to untyped code to gain assurance at compile time. TypeScript and other gradual type systems facilitate type migration by allowing programmers to start with imprecise types and gradually strengthen them. However, adding types is a manual effort and several migrations on large, industry codebases have been reported to have taken several years. In the research community, there has been significant interest in using machine learning to automate TypeScript type migration. Existing machine learning models report a high degree of accuracy in predicting individual TypeScript type annotations. However, in this paper we argue that accuracy can be misleading, and we should address a different question: can an automatic type migration tool produce code that passes the TypeScript type checker? We present TypeWeaver, a TypeScript type migration tool that can be used with an arbitrary type prediction model. We evaluate TypeWeaver with three models from the literature: DeepTyper, a recurrent neural network; LambdaNet, a graph neural network; and InCoder, a general-purpose, multi-language transformer that supports fill-in-the-middle tasks. Our tool automates several steps that are necessary for using a type prediction model, including (1) importing types for a project’s dependencies; (2) migrating JavaScript modules to TypeScript notation; (3) inserting predicted type annotations into the program to produce TypeScript when needed; and (4) rejecting non-type predictions when needed. We evaluate TypeWeaver on a dataset of 513 JavaScript packages, including packages that have never been typed before. With the best type prediction model, we find that only 21% of packages type check, but more encouragingly, 69% of files type check successfully.
Cite as

Ming-Ho Yee and Arjun Guha. Do Machine Learning Models Produce TypeScript Types That Type Check?. In 37th European Conference on Object-Oriented Programming (ECOOP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 263, pp. 37:1-37:28, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2023)

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@InProceedings{yee_et_al:LIPIcs.ECOOP.2023.37,
  author =	{Yee, Ming-Ho and Guha, Arjun},
  title =	{{Do Machine Learning Models Produce TypeScript Types That Type Check?}},
  booktitle =	{37th European Conference on Object-Oriented Programming (ECOOP 2023)},
  pages =	{37:1--37:28},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-281-5},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{263},
  editor =	{Ali, Karim and Salvaneschi, Guido},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECOOP.2023.37},
  URN =		{urn:nbn:de:0030-drops-182307},
  doi =		{10.4230/LIPIcs.ECOOP.2023.37},
  annote =	{Keywords: Type migration, deep learning}
}
Document
Pearl
DOI: 10.4230/LIPIcs.ECOOP.2019.24
Julia’s Efficient Algorithm for Subtyping Unions and Covariant Tuples (Pearl)

Authors: Benjamin Chung, Francesco Zappa Nardelli, and Jan Vitek

Published in: LIPIcs, Volume 134, 33rd European Conference on Object-Oriented Programming (ECOOP 2019)

Abstract
The Julia programming language supports multiple dispatch and provides a rich type annotation language to specify method applicability. When multiple methods are applicable for a given call, Julia relies on subtyping between method signatures to pick the correct method to invoke. Julia’s subtyping algorithm is surprisingly complex, and determining whether it is correct remains an open question. In this paper, we focus on one piece of this problem: the interaction between union types and covariant tuples. Previous work normalized unions inside tuples to disjunctive normal form. However, this strategy has two drawbacks: complex type signatures induce space explosion, and interference between normalization and other features of Julia’s type system. In this paper, we describe the algorithm that Julia uses to compute subtyping between tuples and unions - an algorithm that is immune to space explosion and plays well with other features of the language. We prove this algorithm correct and complete against a semantic-subtyping denotational model in Coq.
Cite as

Benjamin Chung, Francesco Zappa Nardelli, and Jan Vitek. Julia’s Efficient Algorithm for Subtyping Unions and Covariant Tuples (Pearl). In 33rd European Conference on Object-Oriented Programming (ECOOP 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 134, pp. 24:1-24:15, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2019)

Copy BibTex To Clipboard

@InProceedings{chung_et_al:LIPIcs.ECOOP.2019.24,
  author =	{Chung, Benjamin and Zappa Nardelli, Francesco and Vitek, Jan},
  title =	{{Julia’s Efficient Algorithm for Subtyping Unions and Covariant Tuples}},
  booktitle =	{33rd European Conference on Object-Oriented Programming (ECOOP 2019)},
  pages =	{24:1--24:15},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-111-5},
  ISSN =	{1868-8969},
  year =	{2019},
  volume =	{134},
  editor =	{Donaldson, Alastair F.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECOOP.2019.24},
  URN =		{urn:nbn:de:0030-drops-108165},
  doi =		{10.4230/LIPIcs.ECOOP.2019.24},
  annote =	{Keywords: Type systems, Subtyping, Union types}
}
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