2026-07-25T01:46:56Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:20872 2024-09-12T05:48:34Z ddc:004 open_access

InferType: A Compiler Toolkit for Implementing Efficient Constraint-Based Type Inference Li, Senxi Yamazaki, Tetsuro Chiba, Shigeru Domain Specific Languages Compilation Static Analysis Type Inference Constraint Solving SMT Solver 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. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Senxi Li and Tetsuro Yamazaki and Shigeru Chiba 2024 Is Part Of LIPIcs, Volume 313, 38th European Conference on Object-Oriented Programming (ECOOP 2024) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.ECOOP.2024.23 urn:nbn:de:0030-drops-208728 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECOOP.2024.23 eng https://creativecommons.org/licenses/by/4.0/legalcode