Adding Type Constructor Parameterization to Java
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Typescript Language Specification
TypeScript Language Specification Version 1.8 January, 2016 Microsoft is making this Specification available under the Open Web Foundation Final Specification Agreement Version 1.0 ("OWF 1.0") as of October 1, 2012. The OWF 1.0 is available at http://www.openwebfoundation.org/legal/the-owf-1-0-agreements/owfa-1-0. TypeScript is a trademark of Microsoft Corporation. Table of Contents 1 Introduction ................................................................................................................................................................................... 1 1.1 Ambient Declarations ..................................................................................................................................................... 3 1.2 Function Types .................................................................................................................................................................. 3 1.3 Object Types ...................................................................................................................................................................... 4 1.4 Structural Subtyping ....................................................................................................................................................... 6 1.5 Contextual Typing ............................................................................................................................................................ 7 1.6 Classes ................................................................................................................................................................................. -
A System of Constructor Classes: Overloading and Implicit Higher-Order Polymorphism
A system of constructor classes: overloading and implicit higher-order polymorphism Mark P. Jones Yale University, Department of Computer Science, P.O. Box 2158 Yale Station, New Haven, CT 06520-2158. [email protected] Abstract range of other data types, as illustrated by the following examples: This paper describes a flexible type system which combines overloading and higher-order polymorphism in an implicitly data Tree a = Leaf a | Tree a :ˆ: Tree a typed language using a system of constructor classes – a mapTree :: (a → b) → (Tree a → Tree b) natural generalization of type classes in Haskell. mapTree f (Leaf x) = Leaf (f x) We present a wide range of examples which demonstrate mapTree f (l :ˆ: r) = mapTree f l :ˆ: mapTree f r the usefulness of such a system. In particular, we show how data Opt a = Just a | Nothing constructor classes can be used to support the use of monads mapOpt :: (a → b) → (Opt a → Opt b) in a functional language. mapOpt f (Just x) = Just (f x) The underlying type system permits higher-order polymor- mapOpt f Nothing = Nothing phism but retains many of many of the attractive features that have made the use of Hindley/Milner type systems so Each of these functions has a similar type to that of the popular. In particular, there is an effective algorithm which original map and also satisfies the functor laws given above. can be used to calculate principal types without the need for With this in mind, it seems a shame that we have to use explicit type or kind annotations. -
Python Programming
Python Programming Wikibooks.org June 22, 2012 On the 28th of April 2012 the contents of the English as well as German Wikibooks and Wikipedia projects were licensed under Creative Commons Attribution-ShareAlike 3.0 Unported license. An URI to this license is given in the list of figures on page 149. If this document is a derived work from the contents of one of these projects and the content was still licensed by the project under this license at the time of derivation this document has to be licensed under the same, a similar or a compatible license, as stated in section 4b of the license. The list of contributors is included in chapter Contributors on page 143. The licenses GPL, LGPL and GFDL are included in chapter Licenses on page 153, since this book and/or parts of it may or may not be licensed under one or more of these licenses, and thus require inclusion of these licenses. The licenses of the figures are given in the list of figures on page 149. This PDF was generated by the LATEX typesetting software. The LATEX source code is included as an attachment (source.7z.txt) in this PDF file. To extract the source from the PDF file, we recommend the use of http://www.pdflabs.com/tools/pdftk-the-pdf-toolkit/ utility or clicking the paper clip attachment symbol on the lower left of your PDF Viewer, selecting Save Attachment. After extracting it from the PDF file you have to rename it to source.7z. To uncompress the resulting archive we recommend the use of http://www.7-zip.org/. -
Multi-Return Function Call
To appear in J. Functional Programming 1 Multi-return Function Call OLIN SHIVERS and DAVID FISHER College of Computing Georgia Institute of Technology (e-mail: fshivers,[email protected]) Abstract It is possible to extend the basic notion of “function call” to allow functions to have multiple re- turn points. This turns out to be a surprisingly useful mechanism. This article conducts a fairly wide-ranging tour of such a feature: a formal semantics for a minimal λ-calculus capturing the mechanism; motivating examples; monomorphic and parametrically polymorphic static type sys- tems; useful transformations; implementation concerns and experience with an implementation; and comparison to related mechanisms, such as exceptions, sum-types and explicit continuations. We conclude that multiple-return function call is not only a useful and expressive mechanism, at both the source-code and intermediate-representation levels, but also quite inexpensive to implement. Capsule Review Interesting new control-flow constructs don’t come along every day. Shivers and Fisher’s multi- return function call offers intriguing possibilities—but unlike delimited control operators or first- class continuations, it won’t make your head hurt or break the bank. It might even make you smile when you see the well-known tail call generalized to a “semi-tail call” and a “super-tail call.” What I enjoyed the most was the chance to reimagine several of my favorite little hacks using the new mechanism, but this unusually broad paper offers something for everyone: the language designer, the theorist, the implementor, and the programmer. 1 Introduction The purpose of this article is to explore in depth a particular programming-language mech- anism: the ability to specify multiple return points when calling a function. -
Refinement Types for ML
Refinement Types for ML Tim Freeman Frank Pfenning [email protected] [email protected] School of Computer Science School of Computer Science Carnegie Mellon University Carnegie Mellon University Pittsburgh, Pennsylvania 15213-3890 Pittsburgh, Pennsylvania 15213-3890 Abstract tended to the full Standard ML language. To see the opportunity to improve ML’s type system, We describe a refinement of ML’s type system allow- consider the following function which returns the last ing the specification of recursively defined subtypes of cons cell in a list: user-defined datatypes. The resulting system of refine- ment types preserves desirable properties of ML such as datatype α list = nil | cons of α * α list decidability of type inference, while at the same time fun lastcons (last as cons(hd,nil)) = last allowing more errors to be detected at compile-time. | lastcons (cons(hd,tl)) = lastcons tl The type system combines abstract interpretation with We know that this function will be undefined when ideas from the intersection type discipline, but remains called on an empty list, so we would like to obtain a closely tied to ML in that refinement types are given type error at compile-time when lastcons is called with only to programs which are already well-typed in ML. an argument of nil. Using refinement types this can be achieved, thus preventing runtime errors which could be 1 Introduction caught at compile-time. Similarly, we would like to be able to write code such as Standard ML [MTH90] is a practical programming lan- case lastcons y of guage with higher-order functions, polymorphic types, cons(x,nil) => print x and a well-developed module system. -
G22.2110-003 Programming Languages - Fall 2012 Week 14 - Part 1
G22.2110-003 Programming Languages - Fall 2012 Week 14 - Part 1 Thomas Wies New York University Review Last lecture I Exceptions Outline Today: I Generic Programming Sources for today's lecture: I PLP, ch. 8.4 I Programming in Scala, ch. 19, 20.6 Generic programming Subroutines provide a way to abstract over values. Generic programming lets us abstract over types. Examples: I A sorting algorithm has the same structure, regardless of the types being sorted I Stack primitives have the same semantics, regardless of the objects stored on the stack. One common use: I algorithms on containers: updating, iteration, search Language models: I C: macros (textual substitution) or unsafe casts I Ada: generic units and instantiations I C++, Java, C#, Scala: generics (also called templates) I ML: parametric polymorphism, functors Parameterizing software components Construct Parameter(s): array bounds, element type subprogram values (arguments) Ada generic package values, types, packages Ada generic subprogram values, types C++ class template values, types C++ function template values, types Java generic classes Scala generic types (and implicit values) ML function values (including other functions) ML type constructor types ML functor values, types, structures Templates in C++ template <typename T> class Array { public : explicit Array (size_t); // constructor T& operator[] (size_t); // subscript operator ... // other operations private : ... // a size and a pointer to an array }; Array<int> V1(100); // instantiation Array<int> V2; // use default constructor -
Aeroscript Programming Language Reference
AeroScript Programming Language Reference Table of Contents Table of Contents 2 Structure of a Program 5 Comments 6 Preprocessor 7 Text Replacement Macro (#define/#undef) 7 Source File Inclusion (#include) 8 Conditional Inclusion (#if/#ifdef/#ifndef) 8 Data Types and Variables 11 Fundamental Data Types 11 Fundamental Numeric Data Types 11 Fundamental String Data Type 11 Fundamental Axis Data Type 11 Fundamental Handle Data Type 12 Aggregate Data Types 12 Array Data Types 12 Structure Data Types 13 Enumerated Data Types 14 Variables 15 Variable Declaration 15 Variable Names 15 Numeric, Axis, and Handle Variable Declaration Syntax 15 String Variable Declaration Syntax 15 Syntax for Declaring Multiple Variables on the Same Line 16 Array Variable Declaration Syntax 16 Structure Variable Definition and Declaration Syntax 16 Definition Syntax 16 Declaration Syntax 17 Member Access Syntax 17 Enumeration Variable Definition and Declaration Syntax 18 Definition 18 Declaration Syntax 19 Enumerator Access Syntax 19 Variable Initialization Syntax 20 Basic Variable Initialization Syntax 20 Array Variable Initialization Syntax 21 Structure Variable Initialization Syntax 22 Enumeration Variable Initialization Syntax 22 Variable Scope 23 Controller Global Variables 23 User-Defined Variables 23 User-Defined Variable Accessibility 23 User-Defined Local Variable Declaration Location 25 Variable Data Type Conversions 26 Properties 27 Property Declaration 27 Property Names 27 Property Declaration 28 Property Usage 28 Expressions 29 Literals 29 Numeric Literals -
Advanced-Java.Pdf
Advanced java i Advanced java Advanced java ii Contents 1 How to create and destroy objects 1 1.1 Introduction......................................................1 1.2 Instance Construction.................................................1 1.2.1 Implicit (Generated) Constructor.......................................1 1.2.2 Constructors without Arguments.......................................1 1.2.3 Constructors with Arguments........................................2 1.2.4 Initialization Blocks.............................................2 1.2.5 Construction guarantee............................................3 1.2.6 Visibility...................................................4 1.2.7 Garbage collection..............................................4 1.2.8 Finalizers...................................................5 1.3 Static initialization..................................................5 1.4 Construction Patterns.................................................5 1.4.1 Singleton...................................................6 1.4.2 Utility/Helper Class.............................................7 1.4.3 Factory....................................................7 1.4.4 Dependency Injection............................................8 1.5 Download the Source Code..............................................9 1.6 What’s next......................................................9 2 Using methods common to all objects 10 2.1 Introduction...................................................... 10 2.2 Methods equals and hashCode........................................... -
Lecture Slides
Outline Meta-Classes Guy Wiener Introduction AOP Classes Generation 1 Introduction Meta-Classes in Python Logging 2 Meta-Classes in Python Delegation Meta-Classes vs. Traditional OOP 3 Meta-Classes vs. Traditional OOP Outline Meta-Classes Guy Wiener Introduction AOP Classes Generation 1 Introduction Meta-Classes in Python Logging 2 Meta-Classes in Python Delegation Meta-Classes vs. Traditional OOP 3 Meta-Classes vs. Traditional OOP What is Meta-Programming? Meta-Classes Definition Guy Wiener Meta-Program A program that: Introduction AOP Classes One of its inputs is a program Generation (possibly itself) Meta-Classes in Python Its output is a program Logging Delegation Meta-Classes vs. Traditional OOP Meta-Programs Nowadays Meta-Classes Guy Wiener Introduction AOP Classes Generation Compilers Meta-Classes in Python Code Generators Logging Delegation Model-Driven Development Meta-Classes vs. Traditional Templates OOP Syntactic macros (Lisp-like) Meta-Classes The Problem With Static Programming Meta-Classes Guy Wiener Introduction AOP Classes Generation Meta-Classes How to share features between classes and class hierarchies? in Python Logging Share static attributes Delegation Meta-Classes Force classes to adhere to the same protocol vs. Traditional OOP Share code between similar methods Meta-Classes Meta-Classes Guy Wiener Introduction AOP Classes Definition Generation Meta-Classes in Python Meta-Class A class that creates classes Logging Delegation Objects that are instances of the same class Meta-Classes share the same behavior vs. Traditional OOP Classes that are instances of the same meta-class share the same behavior Meta-Classes Meta-Classes Guy Wiener Introduction AOP Classes Definition Generation Meta-Classes in Python Meta-Class A class that creates classes Logging Delegation Objects that are instances of the same class Meta-Classes share the same behavior vs. -
Java Generics Adoption: How New Features Are Introduced, Championed, Or Ignored
Java Generics Adoption: How New Features are Introduced, Championed, or Ignored Chris Parnin Christian Bird Emerson Murphy-Hill College of Computing Microsoft Research Dept. of Computer Science Georgia Institute of Redmond, Washington North Carolina State Technology [email protected] University Atlanta, Georgia Raleigh, North Carolina [email protected] [email protected] Far too often, greatly heralded claims and visions of new language features fail to hold or persist in practice. Discus- ABSTRACT sions of the costs and benefits of language features can easily devolve into a religious war with both sides armed with little Support for generic programming was added to the Java more than anecdotes [13]. Empirical evidence about the language in 2004, representing perhaps the most significant adoption and use of past language features should inform change to one of the most widely used programming lan- and encourage a more rational discussion when designing guages today. Researchers and language designers antici- language features and considering how they should be de- pated this addition would relieve many long-standing prob- ployed. Collecting this evidence is not just sensible but a lems plaguing developers, but surprisingly, no one has yet responsibility of our community. measured whether generics actually provide such relief. In In this paper, we examine the adoption and use of generics, this paper, we report on the first empirical investigation into which were introduced into the Java language in 2004. When how Java generics have been integrated into open source Sun introduced generics, they claimed that the language software by automatically mining the history of 20 popular feature was \a long-awaited enhancement to the type system" open source Java programs, traversing more than 500 million that \eliminates the drudgery of casting." Sun recommended lines of code in the process. -
Addressing Common Crosscutting Problems with Arcum∗
Addressing Common Crosscutting Problems with Arcum∗ Macneil Shonle William G. Griswold Sorin Lerner Computer Science & Engineering, UC San Diego La Jolla, CA 92093-0404 {mshonle, wgg, lerner}@cs.ucsd.edu ABSTRACT 1. INTRODUCTION Crosscutting is an inherent part of software development and can Arcum is a framework for declaring and performing user-defined typically be managed through modularization: A module’s stable program checks and transformations, with the goal of increasing properties are defined in an interface while its likely-to-change automated refactoring opportunities for the user [21]. By using Ar- properties are encapsulated within the module [19]. The cross- cum, a programmer can view the implementation of a crosscutting cutting of the stable properties, such as class and method names, design idiom as a form of module. Arcum uses a declarative lan- can be mitigated with automated refactoring tools that allow, for guage to describe the idiom’s implementation, where descriptions example, the interface’s elements to be renamed [9, 18]. However, are composed of Arcum interface and Arcum option constructs. An often the crosscutting from design idioms (such as design patterns option describes one possible implementation of a crosscutting de- and coding styles) are so specific to the program’s domain that sign idiom, and a set of options are related to each other when they their crosscutting would not likely have been anticipated by the all implement the same Arcum interface. developers of an automated refactoring system. Arcum’s declarative language uses a Java-like syntax for first- The Arcum plug-in for Eclipse enables programmers to describe order logic predicate statements, including a special pattern nota- the implementation of a crosscutting design idiom as a set of syn- tion for expressing Java code. -
The Cool Reference Manual∗
The Cool Reference Manual∗ Contents 1 Introduction 3 2 Getting Started 3 3 Classes 4 3.1 Features . 4 3.2 Inheritance . 5 4 Types 6 4.1 SELF TYPE ........................................... 6 4.2 Type Checking . 7 5 Attributes 8 5.1 Void................................................ 8 6 Methods 8 7 Expressions 9 7.1 Constants . 9 7.2 Identifiers . 9 7.3 Assignment . 9 7.4 Dispatch . 10 7.5 Conditionals . 10 7.6 Loops . 11 7.7 Blocks . 11 7.8 Let . 11 7.9 Case . 12 7.10 New . 12 7.11 Isvoid . 12 7.12 Arithmetic and Comparison Operations . 13 ∗Copyright c 1995-2000 by Alex Aiken. All rights reserved. 1 8 Basic Classes 13 8.1 Object . 13 8.2 IO ................................................. 13 8.3 Int................................................. 14 8.4 String . 14 8.5 Bool . 14 9 Main Class 14 10 Lexical Structure 14 10.1 Integers, Identifiers, and Special Notation . 15 10.2 Strings . 15 10.3 Comments . 15 10.4 Keywords . 15 10.5 White Space . 15 11 Cool Syntax 17 11.1 Precedence . 17 12 Type Rules 17 12.1 Type Environments . 17 12.2 Type Checking Rules . 18 13 Operational Semantics 22 13.1 Environment and the Store . 22 13.2 Syntax for Cool Objects . 24 13.3 Class definitions . 24 13.4 Operational Rules . 25 14 Acknowledgements 30 2 1 Introduction This manual describes the programming language Cool: the Classroom Object-Oriented Language. Cool is a small language that can be implemented with reasonable effort in a one semester course. Still, Cool retains many of the features of modern programming languages including objects, static typing, and automatic memory management.