Types and Semantics for Extensible Data Types (Extended Version)
September 26, 2023 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Cas van der Rest, Casper Bach Poulsen
arXiv ID
2309.14985
Category
cs.PL: Programming Languages
Citations
0
Venue
arXiv.org
Last Checked
4 months ago
Abstract
Developing and maintaining software commonly requires (1) adding new data type constructors to existing applications, but also (2) adding new functions that work on existing data. Most programming languages have native support for defining data types and functions in a way that supports either (1) or (2), but not both. This lack of native support makes it difficult to use and extend libraries. A theoretically well-studied solution is to define data types and functions using initial algebra semantics. While it is possible to encode this solution in existing programming languages, such encodings add syntactic and interpretive overhead, and commonly fail to take advantage of the map and fold fusion laws of initial algebras which compilers could exploit to generate more efficient code. A solution to these is to provide native support for initial algebra semantics. In this paper, we develop such a solution and present a type discipline and core calculus for a language with native support for initial algebra semantics.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Programming Languages
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions
R.I.P.
π»
Ghosted
Glow: Graph Lowering Compiler Techniques for Neural Networks
R.I.P.
π»
Ghosted
Learnable Programming: Blocks and Beyond
R.I.P.
π»
Ghosted
Scenic: A Language for Scenario Specification and Scene Generation
R.I.P.
π»
Ghosted
Vandal: A Scalable Security Analysis Framework for Smart Contracts
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted