Designing with Static Capabilities and Effects: Use, Mention, and Invariants
May 23, 2020 Β· Declared Dead Β· π European Conference on Object-Oriented Programming
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Colin S. Gordon
arXiv ID
2005.11444
Category
cs.PL: Programming Languages
Citations
18
Venue
European Conference on Object-Oriented Programming
Last Checked
3 months ago
Abstract
Capabilities (whether object or reference capabilities) are fundamentally tools to restrict effects. Thus static capabilities (object or reference) and effect systems take different technical machinery to the same core problem of statically restricting or reasoning about effects in programs. Any time two approaches can in principle address the same sets of problems, it becomes important to understand the trade-offs between the approaches, how these trade-offs might interact with the problem at hand. Experts who have worked in these areas tend to find the trade-offs somewhat obvious, having considered them in context before. However, this kind of design discussion is often written down only implicitly as comparison between two approaches for a specific program reasoning problem, rather than as a discussion of general trade-offs between general classes of techniques. As a result, it is not uncommon to set out to solve a problem with one technique, only to find the other better-suited. We discuss the trade-offs between static capabilities (specifically reference capabilities) and effect systems, articulating the challenges each approach tends to have in isolation, and how these are sometimes mitigated. We also put our discussion in context, by appealing to examples of how these trade-offs were considered in the course of developing prior systems in the area. Along the way, we highlight how seemingly-minor aspects of type systems -- weakening/framing and the mere existence of type contexts -- play a subtle role in the efficacy of these systems.
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