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InferType: A Compiler Toolkit for Implementing Efficient Constraint-Based Type Inference

Authors Senxi Li , Tetsuro Yamazaki , Shigeru Chiba

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Subject Classification

ACM Subject Classification
  • Software and its engineering → Domain specific languages
  • Theory of computation → Type theory
Keywords
  • Domain Specific Languages
  • Compilation
  • Static Analysis
  • Type Inference
  • Constraint Solving
  • SMT Solver

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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 Get BibTex

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) https://doi.org/10.4230/LIPIcs.ECOOP.2024.23

Author Details

Senxi Li
  • The University of Tokyo, Japan
Tetsuro Yamazaki
  • The University of Tokyo, Japan
Shigeru Chiba
  • The University of Tokyo, Japan

Funding

This work was supported by JSPS KAKENHI Grant Numbers JP20H00578 and JP24H00688.

Supplementary Materials

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