The Fearless Journey [Draft]
May 10, 2024 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Nick Webster, Marco Servetto, Michael Homer
arXiv ID
2405.06233
Category
cs.PL: Programming Languages
Citations
1
Venue
arXiv.org
Last Checked
4 months ago
Abstract
Existing minimal Object-Oriented models (OO), like Featherweight Java (FJ), are valuable for modelling programs and designing new programming languages and tools. However, their utility in developing real-world programs is limited. We introduce the 'Fearless Heart', a novel object calculus preserving FJ's minimal and extensible nature while being more suited for constructing complex, real-world applications. To illustrate the extensibility of the Fearless Heart, we extend it with Reference Capabilities (RC), creating R-Fearless. It supports mutability and other side effects while retaining the reasoning advantages of functional programming and gaining support for features that are well-known to be enabled by RC, like automatic parallelism, caching and invariants. R-Fearless is still minimal enough to allow further extensions. It is an ideal foundation for constructing both practical systems and formal models.
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