Literature survey on implementation techniques for type systems: Inductive data types and pattern matching

What are the different implementation techniques for type systems regarding inductive data types and pattern matching that have been proposed in the literature?

Bachelor thesis (2023)

Authors

P. Faraldos Pijoan 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 Pau Faraldos Pijoan
Pattern matching Inductive data types Implementation techniques
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Published Date

28-06-2023

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

Data types and pattern matching are fundamental concepts in programming. Data types define the structure of data, while pattern matching allows efficient manipulation and extraction of the same data. This text provides an overview of different implementation techniques for type systems regarding data types and pattern matching in the existing literature. Data types considered include inductive, coinductive, and mutually inductive, while the main pattern-matching methods considered are decision trees, backtracking finite state automata, and term decomposition. Though approaches for implementation techniques of data types can be compared more objectively, separate approaches for pattern matching have different benefits and drawbacks, thus, a one-fits-all technique does not exist.

Files

Final_Paper.pdf
(.pdf | 0.229 Mb)