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Gradual Refinement

Blending Pattern Matching with Data Abstraction

  • Conference paper
Mathematics of Program Construction (MPC 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6120))

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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 right-invertible 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.

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Wang, M., Gibbons, J., Matsuda, K., Hu, Z. (2010). Gradual Refinement. In: Bolduc, C., Desharnais, J., Ktari, B. (eds) Mathematics of Program Construction. MPC 2010. Lecture Notes in Computer Science, vol 6120. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13321-3_22

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  • DOI: https://doi.org/10.1007/978-3-642-13321-3_22

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13320-6

  • Online ISBN: 978-3-642-13321-3

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