Datatype-Generic Programming International Spring School, SSDGP 2006, Nottingham, UK, April 24-27, 2006, Revised Lectures / [electronic resource] :
edited by Roland Backhouse, Jeremy Gibbons, Ralf Hinze, Johan Jeuring.
- XI, 372 p. online resource.
- Lecture Notes in Computer Science, 4719 0302-9743 ; .
- Lecture Notes in Computer Science, 4719 .
Datatype-Generic Programming -- Comparing Approaches to Generic Programming in Haskell -- Generic Programming, Now! -- Generic Programming with Dependent Types -- Generic Programming in ?mega -- Revealing the X/O Impedance Mismatch.
ZDB-2-SCS ZDB-2-LNC
A leitmotif in the evolution of programming paradigms has been the level and extent of parametrisation that is facilitated the so-called genericity of the paradigm. The sorts of parameters that can be envisaged in a programming language range from simple values, like integers and ?oating-point numbers, through structured values, types and classes, to kinds (the type of types and/or classes).Datatype-generic programming is about parametrising programsby the structure of the data that they manipulate. Toappreciatethe importanceofdatatypegenericity,oneneedlooknofurther than the internet. The internet is a massive repository of structured data, but the structure is rarely exploited. For example, compression of data can be much more e?ective if its structure is known, but most compression algorithms regard the input data as simply a string of bits, and take no account of its internal organisation. Datatype-genericprogrammingisaboutexploitingthestructureofdatawhen it is relevant and ignoring it when it is not. Programming languages most c- monly used at the present time do not provide e?ective mechanisms for do- menting and implementing datatype genericity. This volume is a contribution towards improving the state of the art. The emergence of datatype genericity can be traced back to the late 1980s.
9783540767862
10.1007/978-3-540-76786-2 doi
Computer science. Software engineering. Data structures (Computer science). Logic design. Computer Science. Programming Techniques. Programming Languages, Compilers, Interpreters. Software Engineering. Data Structures. Logics and Meanings of Programs.