Making the future safe for the past

Bracha, Gilad; Odersky, Martin; Stoutamire, David; Wadler, Philip

doi:10.1145/286936.286957

Cited by 286 publications

(7 citation statements)

References 15 publications

(13 reference statements)

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“…The method has signature Equal(that a) bool. The situation where a type parameter appears in its own bound is known as F-bounded polymorphism [Canning et al 1989], and a similar idiom is common in Java with generics [Bracha et al 1998;Naftalin and Wadler 2006].…”

Section: Fgg By Examplementioning

confidence: 99%

Featherweight go

Griesemer

Kokke

et al. 2020

Proc. ACM Program. Lang.

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We describe a design for generics in Go inspired by previous work on Featherweight Java by Igarashi, Pierce, and Wadler. Whereas subtyping in Java is nominal, in Go it is structural, and whereas generics in Java are defined via erasure, in Go we use monomorphisation. Although monomorphisation is widely used, we are one of the first to formalise it. Our design also supports a solution to The Expression Problem.

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Section: Fgg By Examplementioning

confidence: 99%

Featherweight go

Griesemer

Kokke

et al. 2020

Proc. ACM Program. Lang.

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“…Finally, virtual classes [MMP89,EOC06], dependent classes [GMO07], and generics [MMN75,BOSW98] are mechanisms to parameterize classes. That work is vaguely related to PCCs in that it is particularly useful for collection classes such as lists, sets, etc.…”

Section: Other Class-instance Associationsmentioning

confidence: 99%

Predicate Collection Classes.

Lopes¹,

Achar²,

Valadares³

2017

JOT

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Collections are at the heart of every program. Modern programming languages have acknowledged this fact by including increasingly better expression mechanisms for manipulating collections of data and objects. When existing objects are selected as elements of collections, often there is an implicit intention that those objects, by means of having been selected, take on new roles. Such is the case, for example, with shared data in distributed simulations, which changes over time, and which distributed components may use for different purposes as the simulation unfolds.This paper presents the concept of Predicate Collection Classes (PCCs). PCCs are both classes and specifications of how to select and reclassify objects from existing collections. We describe the informal semantics of PCCs and demonstrate how they can be used to express several iterative algorithms that make heavy use of collections. Finally, we summarize our use of PCCs in a framework for distributed simulations.

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“…A heterogeneous translation generates separate copies of the generic code for every set of type arguments that its type parameters can be instantiated with (Kennedy and Syme 2001;Leroy 1992;Morrison et al 1991). A homogeneous translation generates a single copy in which each type parameter is erased to a top type such as Object that can accommodate values of any type (Bracha et al 1998). The homogeneous approach has poor performance.…”

Section: Application To Specializationmentioning

confidence: 99%

Call graphs for languages with parametric polymorphism

Petrashko

Ureche

Lhoták

et al. 2016

Proceedings of the 2016 ACM SIGPLAN International Conference on Object-Oriented Programming, Systems, Languages, and Applicatio

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The performance of contemporary object oriented languages depends on optimizations such as devirtualization, inlining, and specialization, and these in turn depend on precise call graph analysis. Existing call graph analyses do not take advantage of the information provided by the rich type systems of contemporary languages, in particular generic type arguments. Many existing approaches analyze Java bytecode, in which generic types have been erased. This paper shows that this discarded information is actually very useful as the context in a context-sensitive analysis, where it significantly improves precision and keeps the running time small. Specifically, we propose and evaluate call graph construction algorithms in which the contexts of a method are (i) the type arguments passed to its type parameters, and (ii) the static types of the arguments passed to its term parameters. The use of static types from the caller as context is effective because it allows more precise dispatch of call sites inside the callee.Our evaluation indicates that the average number of contexts required per method is small. We implement the analysis in the Dotty compiler for Scala, and evaluate it on programs that use the type-parametric Scala collections library and on the Dotty compiler itself. The context-sensitive analysis runs 1.4x faster than a context-insensitive one and discovers 20% more monomorphic call sites at the same time. When applied to method specialization, the imprecision in a context-insensitive call graph would require the average method to be cloned 22 times, whereas the context-sensitive call graph indicates a much more practical 1.00 to 1.50 clones per method.

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Making the future safe for the past

Cited by 286 publications

References 15 publications

Featherweight go

Featherweight go

Predicate Collection Classes.

Call graphs for languages with parametric polymorphism

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