2016
DOI: 10.1017/s0956796816000162
|View full text |Cite
|
Sign up to set email alerts
|

Gradual type-and-effect systems

Abstract: Effect systems have the potential to help software developers, but their practical adoption has been very limited. We conjecture that this limited adoption is due in part to the difficulty of transitioning from a system where effects are implicit and unrestricted to a system with a static effect discipline, which must settle for conservative checking in order to be decidable. To address this hindrance, we develop a theory of gradual effect checking, which makes it possible to incrementally annotate and statica… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1

Citation Types

0
2
0

Year Published

2018
2018
2024
2024

Publication Types

Select...
5
1

Relationship

1
5

Authors

Journals

citations
Cited by 13 publications
(2 citation statements)
references
References 30 publications
0
2
0
Order By: Relevance
“…There has been considerable work in gradual typing both from industry [3,9,27,4] and academia [18,24,26,14,17,1]. The challenge in capturing a calculus such as the π-calculus is that several output processes may compete for a single input.…”
Section: Related Work and (More) Future Workmentioning
confidence: 99%
“…There has been considerable work in gradual typing both from industry [3,9,27,4] and academia [18,24,26,14,17,1]. The challenge in capturing a calculus such as the π-calculus is that several output processes may compete for a single input.…”
Section: Related Work and (More) Future Workmentioning
confidence: 99%
“…The academic field is mainly dedicated to combining gradual typing with other language features, including both expressiveness (object (Siek & Taha, 2007; Chung et al ., 2018), effect (Bañados Schwerter et al ., 2014, 2016;Wadler, 2021), polymorphism (Ahmed et al ., 2011, 2017; Igarashi et al ., 2017), set theoretic types (Toro & Tanter, 2017; Castagna et al ., 2019)), and implementation optimization (Pycket (Bauman et al ., 2015), Grift (Kuhlenschmidt et al ., 2019)).…”
Section: Introductionmentioning
confidence: 99%