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Global Type Inference for Featherweight Generic Java

Authors Andreas Stadelmeier, Martin Plümicke, Peter Thiemann

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Author Details

Andreas Stadelmeier
  • Duale Hochschule Baden-Württemberg Stuttgart, Campus Horb, Germany
Martin Plümicke
  • Duale Hochschule Baden-Württemberg Stuttgart, Campus Horb, Germany
Peter Thiemann
  • Institut für Informatik, Universität Freiburg, Germany

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Andreas Stadelmeier, Martin Plümicke, and Peter Thiemann. Global Type Inference for Featherweight Generic Java. In 36th European Conference on Object-Oriented Programming (ECOOP 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 222, pp. 28:1-28:27, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)
https://doi.org/10.4230/LIPIcs.ECOOP.2022.28

Abstract

Java’s type system mostly relies on type checking augmented with local type inference to improve programmer convenience. We study global type inference for Featherweight Generic Java (FGJ), a functional Java core language. Given generic class headers and field specifications, our inference algorithm infers all method types if classes do not make use of polymorphic recursion. The algorithm is constraint-based and improves on prior work in several respects. Despite the restricted setting, global type inference for FGJ is NP-complete.

Subject Classification

ACM Subject Classification
  • Software and its engineering → Language features
Keywords
  • type inference
  • Java
  • subtyping
  • generics

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