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Gradual Refinement

Blending Pattern Matching with Data Abstraction
  • Meng Wang
  • Jeremy Gibbons
  • Kazutaka Matsuda
  • Zhenjiang Hu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6120)

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.

Keywords

Pattern Match Conversion Function Primitive Function Constructor Function Abstract Data Type 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Meng Wang
    • 1
  • Jeremy Gibbons
    • 1
  • Kazutaka Matsuda
    • 2
  • Zhenjiang Hu
    • 3
  1. 1.Computing LaboratoryOxford UniversityOxfordUK
  2. 2.Graduate School of Information SciencesTohoku UniversityJapan
  3. 3.National Institute of InformaticsGRACE CenterTokyoJapan

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