6,026 research outputs found
The Sketch of a Polymorphic Symphony
In previous work, we have introduced functional strategies, that is,
first-class generic functions that can traverse into terms of any type while
mixing uniform and type-specific behaviour. In the present paper, we give a
detailed description of one particular Haskell-based model of functional
strategies. This model is characterised as follows. Firstly, we employ
first-class polymorphism as a form of second-order polymorphism as for the mere
types of functional strategies. Secondly, we use an encoding scheme of run-time
type case for mixing uniform and type-specific behaviour. Thirdly, we base all
traversal on a fundamental combinator for folding over constructor
applications.
Using this model, we capture common strategic traversal schemes in a highly
parameterised style. We study two original forms of parameterisation. Firstly,
we design parameters for the specific control-flow, data-flow and traversal
characteristics of more concrete traversal schemes. Secondly, we use
overloading to postpone commitment to a specific type scheme of traversal. The
resulting portfolio of traversal schemes can be regarded as a challenging
benchmark for setups for typed generic programming.
The way we develop the model and the suite of traversal schemes, it becomes
clear that parameterised + typed strategic programming is best viewed as a
potent combination of certain bits of parametric, intensional, polytypic, and
ad-hoc polymorphism
A Framework for Datatype Transformation
We study one dimension in program evolution, namely the evolution of the
datatype declarations in a program. To this end, a suite of basic
transformation operators is designed. We cover structure-preserving
refactorings, but also structure-extending and -reducing adaptations. Both the
object programs that are subject to datatype transformations, and the meta
programs that encode datatype transformations are functional programs.Comment: Minor revision; now accepted at LDTA 200
What Does Aspect-Oriented Programming Mean for Functional Programmers?
Aspect-Oriented Programming (AOP) aims at modularising crosscutting concerns that show up in software. The success of AOP has been almost viral and nearly all areas in Software Engineering and Programming Languages have become "infected" by the AOP bug in one way or another. Interestingly the functional programming community (and, in particular, the pure functional programming community) seems to be resistant to the pandemic. The goal of this paper is to debate the possible causes of the functional programming community's resistance and to raise awareness and interest by showcasing the benefits that could be gained from having a functional AOP language. At the same time, we identify the main challenges and explore the possible design-space
Reasoning about modular datatypes with Mendler induction
In functional programming, datatypes a la carte provide a convenient modular
representation of recursive datatypes, based on their initial algebra
semantics. Unfortunately it is highly challenging to implement this technique
in proof assistants that are based on type theory, like Coq. The reason is that
it involves type definitions, such as those of type-level fixpoint operators,
that are not strictly positive. The known work-around of impredicative
encodings is problematic, insofar as it impedes conventional inductive
reasoning. Weak induction principles can be used instead, but they considerably
complicate proofs.
This paper proposes a novel and simpler technique to reason inductively about
impredicative encodings, based on Mendler-style induction. This technique
involves dispensing with dependent induction, ensuring that datatypes can be
lifted to predicates and relying on relational formulations. A case study on
proving subject reduction for structural operational semantics illustrates that
the approach enables modular proofs, and that these proofs are essentially
similar to conventional ones.Comment: In Proceedings FICS 2015, arXiv:1509.0282
Practical Datatype Specializations with Phantom Types and Recursion Schemes
Datatype specialization is a form of subtyping that captures program
invariants on data structures that are expressed using the convenient and
intuitive datatype notation. Of particular interest are structural invariants
such as well-formedness. We investigate the use of phantom types for describing
datatype specializations. We show that it is possible to express
statically-checked specializations within the type system of Standard ML. We
also show that this can be done in a way that does not lose useful programming
facilities such as pattern matching in case expressions.Comment: 25 pages. Appeared in the Proc. of the 2005 ACM SIGPLAN Workshop on
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