PF
P. Faraldos Pijoan
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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?
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.
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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.