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Documents authored by 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
Artifact
DOI: 10.4230/DARTS.9.2.5
Do Machine Learning Models Produce TypeScript Types That Type Check? (Artifact)

Authors: Ming-Ho Yee and Arjun Guha

Published in: DARTS, Volume 9, Issue 2, Special Issue of the 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? (Artifact). In Special Issue of the 37th European Conference on Object-Oriented Programming (ECOOP 2023). Dagstuhl Artifacts Series (DARTS), Volume 9, Issue 2, pp. 5:1-5:3, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@Article{yee_et_al:DARTS.9.2.5,
  author =	{Yee, Ming-Ho and Guha, Arjun},
  title =	{{Do Machine Learning Models Produce TypeScript Types That Type Check? (Artifact)}},
  pages =	{5:1--5:3},
  journal =	{Dagstuhl Artifacts Series},
  ISSN =	{2509-8195},
  year =	{2023},
  volume =	{9},
  number =	{2},
  editor =	{Yee, Ming-Ho and Guha, Arjun},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DARTS.9.2.5},
  URN =		{urn:nbn:de:0030-drops-182456},
  doi =		{10.4230/DARTS.9.2.5},
  annote =	{Keywords: Type migration, deep learning}
}
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