Gradual Refinement Blending Pattern Matching with Data Abstraction

Gradual Refinement Blending Pattern Matching with Data Abstraction

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by Meng Wang , Jeremy Gibbons , Kazutaka Matsuda , Zhenjiang Hu

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Datum	Value	Source
TITLE	Gradual Refinement Blending Pattern Matching with Data Abstraction	SVM HeaderParse 0.2
AUTHOR NAME	Meng Wang	SVM HeaderParse 0.2
AUTHOR AFFIL	1; Oxford University Computing Laboratory	SVM HeaderParse 0.2
AUTHOR ADDR	Wolfson Building, Parks Road, Oxford OX1 3QD, UK	SVM HeaderParse 0.2
AUTHOR NAME	Jeremy Gibbons	SVM HeaderParse 0.2
AUTHOR AFFIL	1; Oxford University Computing Laboratory	SVM HeaderParse 0.2
AUTHOR ADDR	Wolfson Building, Parks Road, Oxford OX1 3QD, UK	SVM HeaderParse 0.2
AUTHOR NAME	Kazutaka Matsuda	SVM HeaderParse 0.2
AUTHOR AFFIL	2; Graduate School of Information Sciences, Tohoku University	SVM HeaderParse 0.2
AUTHOR ADDR	Aramaki aza Aoba 6-3-09, Aoba-ku, Sendai-city, Miyagi-pref. 980-8579, Japan	SVM HeaderParse 0.2
AUTHOR NAME	Zhenjiang Hu	SVM HeaderParse 0.2
AUTHOR AFFIL	3; GRACE Center, National Institute of Informatics	SVM HeaderParse 0.2
AUTHOR ADDR	2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo 101-8430, Japan	SVM HeaderParse 0.2
ABSTRACT	Abstract. Pattern matching is advantageous for understanding and reasoning about function definitions, but it tends to tightly couple the interface and implementation of a datatype. Significant effort has been invested in tackling this loss of modularity; however, decoupling patterns from concrete representations while maintaining soundness of reasoning has been a challenge. Inspired by the development of invertible programming, we propose an approach to abstract datatypes based on a rightinvertible language rinv—every function has a right (or pre-) inverse. We show how this new design is able to permit a smooth incremental transition from programs with algebraic datatypes and pattern matching, to ones with proper encapsulation (implemented as abstract datatypes), while maintaining simple and sound reasoning.	SVM HeaderParse 0.2
CITATIONS	43 found	ParsCit 1.0