Search Results

Documents authored by Li, Senxi

Document
DOI: 10.4230/LIPIcs.ECOOP.2024.23
InferType: A Compiler Toolkit for Implementing Efficient Constraint-Based Type Inference

Authors: Senxi Li, Tetsuro Yamazaki, and Shigeru Chiba

Published in: LIPIcs, Volume 313, 38th European Conference on Object-Oriented Programming (ECOOP 2024)

Abstract
Supporting automatic type inference is in demand in modern language development. It is a challenging task but without appropriate supporting toolkits. This paper presents InferType, a Java library that helps implement constraint-based type inference. A compiler writer uses InferType’s classes and methods to describe type constraints and typing rules for type inference. InferType then performs constraint solving by translation to the Z3 SMT solver. InferType is equipped with our developed optimization technique. It reduces the search space for type variables by pre-computing the structures of those type variables for mitigating the performance bottleneck of constraint solving with deeply nested types. We use InferType to implement type inference for a subset of Python, and conduct experiments to evaluate how the developed optimization technique can affect the performance of type inference. Our results show that InferType’s optimization can greatly mitigate the performance bottleneck for programs with deeply nested types, and can potentially improve the performance for large nested types.
Cite as

Senxi Li, Tetsuro Yamazaki, and Shigeru Chiba. InferType: A Compiler Toolkit for Implementing Efficient Constraint-Based Type Inference. In 38th European Conference on Object-Oriented Programming (ECOOP 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 313, pp. 23:1-23:28, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

Copy BibTex To Clipboard

@InProceedings{li_et_al:LIPIcs.ECOOP.2024.23,
  author =	{Li, Senxi and Yamazaki, Tetsuro and Chiba, Shigeru},
  title =	{{InferType: A Compiler Toolkit for Implementing Efficient Constraint-Based Type Inference}},
  booktitle =	{38th European Conference on Object-Oriented Programming (ECOOP 2024)},
  pages =	{23:1--23:28},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-341-6},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{313},
  editor =	{Aldrich, Jonathan 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.2024.23},
  URN =		{urn:nbn:de:0030-drops-208728},
  doi =		{10.4230/LIPIcs.ECOOP.2024.23},
  annote =	{Keywords: Domain Specific Languages, Compilation, Static Analysis, Type Inference, Constraint Solving, SMT Solver}
}
Document
Artifact
DOI: 10.4230/DARTS.10.2.11
InferType: A Compiler Toolkit for Implementing Efficient Constraint-Based Type Inference (Artifact)

Authors: Senxi Li, Tetsuro Yamazaki, and Shigeru Chiba

Published in: DARTS, Volume 10, Issue 2, Special Issue of the 38th European Conference on Object-Oriented Programming (ECOOP 2024)

Abstract
Supporting automatic type inference is in demand in modern language development. It is a challenging task but without appropriate supporting toolkits. This paper presents InferType, a Java library that helps implement constraint-based type inference. A compiler writer uses InferType’s classes and methods to describe type constraints and typing rules for type inference. InferType then performs constraint solving by translation to the Z3 SMT solver. InferType is equipped with our developed optimization technique. It reduces the search space for type variables by pre-computing the structures of those type variables for mitigating the performance bottleneck of constraint solving with deeply nested types. We use InferType to implement type inference for a subset of Python, and conduct experiments to evaluate how the developed optimization technique can affect the performance of type inference. Our results show that InferType’s optimization can greatly mitigate the performance bottleneck for programs with deeply nested types, and can potentially improve the performance for large nested types.
Cite as

Senxi Li, Tetsuro Yamazaki, and Shigeru Chiba. InferType: A Compiler Toolkit for Implementing Efficient Constraint-Based Type Inference (Artifact). In Special Issue of the 38th European Conference on Object-Oriented Programming (ECOOP 2024). Dagstuhl Artifacts Series (DARTS), Volume 10, Issue 2, pp. 11:1-11:2, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

Copy BibTex To Clipboard

@Article{li_et_al:DARTS.10.2.11,
  author =	{Li, Senxi and Yamazaki, Tetsuro and Chiba, Shigeru},
  title =	{{InferType: A Compiler Toolkit for Implementing Efficient Constraint-Based Type Inference (Artifact)}},
  pages =	{11:1--11:2},
  journal =	{Dagstuhl Artifacts Series},
  ISBN =	{978-3-95977-342-3},
  ISSN =	{2509-8195},
  year =	{2024},
  volume =	{10},
  number =	{2},
  editor =	{Li, Senxi and Yamazaki, Tetsuro and Chiba, Shigeru},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DARTS.10.2.11},
  URN =		{urn:nbn:de:0030-drops-209094},
  doi =		{10.4230/DARTS.10.2.11},
  annote =	{Keywords: Domain Specific Languages, Compilation, Static Analysis, Type Inference, Constraint Solving, SMT Solver}
}
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail
© 2023-2024 Schloss Dagstuhl – LZI GmbH About DROPS Imprint Privacy Contact