Literature survey of type inference algorithms for statically typed languages

Bachelor thesis (2023)

Authors

S. Jakovonis Electrical Engineering, Mathematics and Computer Science

Contributors

J.G.H. Cockx Programming Languages - EEMCS (supervisor 1)
B. Liesnikov Programming Languages - EEMCS (supervisor 1)
A. Panichella Software Engineering - EEMCS (supervisor 2)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2023 Saulius Jakovonis
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Published Date

28-06-2023

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

The success of dynamically typed languages such as Python has resulted in an increased interest in supporting type inference in statically typed lan- guages. Type inference refers to automatic type detection based on surrounding context and allows retaining the type safety (and other advantages) of static types, while matching the ease of use of dy- namically typed languages. Unfortunately, imple- menting type inference can be tricky. Researchers have been proposing various methods for type in- ference ever since the 1970s, however there is no single solution that works for all languages. This paper presents and analyses the proposed methods together with motivations, intuitions, use-cases and examples from practice with the aim of helping new programming language developers understand type inference principles and choose the right technique for their needs.